Initial commit: hardened DeerFlow factory
Vendored deer-flow upstream (bytedance/deer-flow) plus prompt-injection hardening: - New deerflow.security package: content_delimiter, html_cleaner, sanitizer (8 layers — invisible chars, control chars, symbols, NFC, PUA, tag chars, horizontal whitespace collapse with newline/tab preservation, length cap) - New deerflow.community.searx package: web_search, web_fetch, image_search backed by a private SearX instance, every external string sanitized and wrapped in <<<EXTERNAL_UNTRUSTED_CONTENT>>> delimiters - All native community web providers (ddg_search, tavily, exa, firecrawl, jina_ai, infoquest, image_search) replaced with hard-fail stubs that raise NativeWebToolDisabledError at import time, so a misconfigured tool.use path fails loud rather than silently falling back to unsanitized output - Native client back-doors (jina_client.py, infoquest_client.py) stubbed too - Native-tool tests quarantined under tests/_disabled_native/ (collect_ignore_glob via local conftest.py) - Sanitizer Layer 7 fix: only collapse horizontal whitespace, preserve newlines and tabs so list/table structure survives - Hardened runtime config.yaml references only the searx-backed tools - Factory overlay (backend/) kept in sync with deer-flow tree as a reference / source See HARDENING.md for the full audit trail and verification steps.
This commit is contained in:
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deer-flow/backend/.gitignore
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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.coverage
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.coverage.*
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.ruff_cache
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agent_history.gif
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static/browser_history/*.gif
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log/
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log/*
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# Virtual environments
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.venv
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venv/
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# User config file
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config.yaml
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# Langgraph
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.langgraph_api
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# Claude Code settings
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.claude/settings.local.json
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3.12
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deer-flow/backend/AGENTS.md
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For the backend architecture and design patterns:
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@./CLAUDE.md
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557
deer-flow/backend/CLAUDE.md
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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DeerFlow is a LangGraph-based AI super agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution, persistent memory, subagent delegation, and extensible tool integration - all operating in per-thread isolated environments.
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**Architecture**:
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- **LangGraph Server** (port 2024): Agent runtime and workflow execution
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- **Gateway API** (port 8001): REST API for models, MCP, skills, memory, artifacts, uploads, and local thread cleanup
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- **Frontend** (port 3000): Next.js web interface
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- **Nginx** (port 2026): Unified reverse proxy entry point
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- **Provisioner** (port 8002, optional in Docker dev): Started only when sandbox is configured for provisioner/Kubernetes mode
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**Runtime Modes**:
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- **Standard mode** (`make dev`): LangGraph Server handles agent execution as a separate process. 4 processes total.
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- **Gateway mode** (`make dev-pro`, experimental): Agent runtime embedded in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Service manages its own concurrency via async tasks. 3 processes total, no LangGraph Server.
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**Project Structure**:
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```
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deer-flow/
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├── Makefile # Root commands (check, install, dev, stop)
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├── config.yaml # Main application configuration
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├── extensions_config.json # MCP servers and skills configuration
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├── backend/ # Backend application (this directory)
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│ ├── Makefile # Backend-only commands (dev, gateway, lint)
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│ ├── langgraph.json # LangGraph server configuration
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│ ├── packages/
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│ │ └── harness/ # deerflow-harness package (import: deerflow.*)
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│ │ ├── pyproject.toml
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│ │ └── deerflow/
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│ │ ├── agents/ # LangGraph agent system
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│ │ │ ├── lead_agent/ # Main agent (factory + system prompt)
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│ │ │ ├── middlewares/ # 10 middleware components
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│ │ │ ├── memory/ # Memory extraction, queue, prompts
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│ │ │ └── thread_state.py # ThreadState schema
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│ │ ├── sandbox/ # Sandbox execution system
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│ │ │ ├── local/ # Local filesystem provider
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│ │ │ ├── sandbox.py # Abstract Sandbox interface
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│ │ │ ├── tools.py # bash, ls, read/write/str_replace
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│ │ │ └── middleware.py # Sandbox lifecycle management
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│ │ ├── subagents/ # Subagent delegation system
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│ │ │ ├── builtins/ # general-purpose, bash agents
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│ │ │ ├── executor.py # Background execution engine
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│ │ │ └── registry.py # Agent registry
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│ │ ├── tools/builtins/ # Built-in tools (present_files, ask_clarification, view_image)
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│ │ ├── mcp/ # MCP integration (tools, cache, client)
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│ │ ├── models/ # Model factory with thinking/vision support
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│ │ ├── skills/ # Skills discovery, loading, parsing
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│ │ ├── config/ # Configuration system (app, model, sandbox, tool, etc.)
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│ │ ├── community/ # Community tools (tavily, jina_ai, firecrawl, image_search, aio_sandbox)
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│ │ ├── reflection/ # Dynamic module loading (resolve_variable, resolve_class)
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│ │ ├── utils/ # Utilities (network, readability)
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│ │ └── client.py # Embedded Python client (DeerFlowClient)
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│ ├── app/ # Application layer (import: app.*)
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│ │ ├── gateway/ # FastAPI Gateway API
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│ │ │ ├── app.py # FastAPI application
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│ │ │ └── routers/ # FastAPI route modules (models, mcp, memory, skills, uploads, threads, artifacts, agents, suggestions, channels)
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│ │ └── channels/ # IM platform integrations
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│ ├── tests/ # Test suite
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│ └── docs/ # Documentation
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├── frontend/ # Next.js frontend application
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└── skills/ # Agent skills directory
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├── public/ # Public skills (committed)
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└── custom/ # Custom skills (gitignored)
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```
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## Important Development Guidelines
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### Documentation Update Policy
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**CRITICAL: Always update README.md and CLAUDE.md after every code change**
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When making code changes, you MUST update the relevant documentation:
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- Update `README.md` for user-facing changes (features, setup, usage instructions)
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- Update `CLAUDE.md` for development changes (architecture, commands, workflows, internal systems)
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- Keep documentation synchronized with the codebase at all times
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- Ensure accuracy and timeliness of all documentation
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## Commands
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**Root directory** (for full application):
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```bash
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make check # Check system requirements
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make install # Install all dependencies (frontend + backend)
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make dev # Start all services (LangGraph + Gateway + Frontend + Nginx), with config.yaml preflight
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make dev-pro # Gateway mode (experimental): skip LangGraph, agent runtime embedded in Gateway
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make start-pro # Production + Gateway mode (experimental)
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make stop # Stop all services
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```
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**Backend directory** (for backend development only):
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```bash
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make install # Install backend dependencies
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make dev # Run LangGraph server only (port 2024)
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make gateway # Run Gateway API only (port 8001)
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make test # Run all backend tests
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make lint # Lint with ruff
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make format # Format code with ruff
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```
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Regression tests related to Docker/provisioner behavior:
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- `tests/test_docker_sandbox_mode_detection.py` (mode detection from `config.yaml`)
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- `tests/test_provisioner_kubeconfig.py` (kubeconfig file/directory handling)
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|
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Boundary check (harness → app import firewall):
|
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- `tests/test_harness_boundary.py` — ensures `packages/harness/deerflow/` never imports from `app.*`
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CI runs these regression tests for every pull request via [.github/workflows/backend-unit-tests.yml](../.github/workflows/backend-unit-tests.yml).
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## Architecture
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### Harness / App Split
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The backend is split into two layers with a strict dependency direction:
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- **Harness** (`packages/harness/deerflow/`): Publishable agent framework package (`deerflow-harness`). Import prefix: `deerflow.*`. Contains agent orchestration, tools, sandbox, models, MCP, skills, config — everything needed to build and run agents.
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- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram).
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**Dependency rule**: App imports deerflow, but deerflow never imports app. This boundary is enforced by `tests/test_harness_boundary.py` which runs in CI.
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**Import conventions**:
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```python
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# Harness internal
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from deerflow.agents import make_lead_agent
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from deerflow.models import create_chat_model
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# App internal
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from app.gateway.app import app
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from app.channels.service import start_channel_service
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# App → Harness (allowed)
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from deerflow.config import get_app_config
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# Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
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# from app.gateway.routers.uploads import ... # ← will fail CI
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```
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### Agent System
|
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**Lead Agent** (`packages/harness/deerflow/agents/lead_agent/agent.py`):
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- Entry point: `make_lead_agent(config: RunnableConfig)` registered in `langgraph.json`
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- Dynamic model selection via `create_chat_model()` with thinking/vision support
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- Tools loaded via `get_available_tools()` - combines sandbox, built-in, MCP, community, and subagent tools
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- System prompt generated by `apply_prompt_template()` with skills, memory, and subagent instructions
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|
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**ThreadState** (`packages/harness/deerflow/agents/thread_state.py`):
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- Extends `AgentState` with: `sandbox`, `thread_data`, `title`, `artifacts`, `todos`, `uploaded_files`, `viewed_images`
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- Uses custom reducers: `merge_artifacts` (deduplicate), `merge_viewed_images` (merge/clear)
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**Runtime Configuration** (via `config.configurable`):
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- `thinking_enabled` - Enable model's extended thinking
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- `model_name` - Select specific LLM model
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- `is_plan_mode` - Enable TodoList middleware
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- `subagent_enabled` - Enable task delegation tool
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|
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### Middleware Chain
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Middlewares execute in strict order in `packages/harness/deerflow/agents/lead_agent/agent.py`:
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1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local `.deer-flow/threads/{thread_id}` directory
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2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
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3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
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4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
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5. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
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6. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
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7. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
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8. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
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9. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
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10. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
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11. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
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12. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
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|
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### Configuration System
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**Main Configuration** (`config.yaml`):
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Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** directory.
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|
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**Config Versioning**: `config.example.yaml` has a `config_version` field. On startup, `AppConfig.from_file()` compares user version vs example version and emits a warning if outdated. Missing `config_version` = version 0. Run `make config-upgrade` to auto-merge missing fields. When changing the config schema, bump `config_version` in `config.example.yaml`.
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**Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.
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|
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Configuration priority:
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1. Explicit `config_path` argument
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2. `DEER_FLOW_CONFIG_PATH` environment variable
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3. `config.yaml` in current directory (backend/)
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4. `config.yaml` in parent directory (project root - **recommended location**)
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|
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Config values starting with `$` are resolved as environment variables (e.g., `$OPENAI_API_KEY`).
|
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`ModelConfig` also declares `use_responses_api` and `output_version` so OpenAI `/v1/responses` can be enabled explicitly while still using `langchain_openai:ChatOpenAI`.
|
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|
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**Extensions Configuration** (`extensions_config.json`):
|
||||
|
||||
MCP servers and skills are configured together in `extensions_config.json` in project root:
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|
||||
Configuration priority:
|
||||
1. Explicit `config_path` argument
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2. `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable
|
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3. `extensions_config.json` in current directory (backend/)
|
||||
4. `extensions_config.json` in parent directory (project root - **recommended location**)
|
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|
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### Gateway API (`app/gateway/`)
|
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|
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FastAPI application on port 8001 with health check at `GET /health`.
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|
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**Routers**:
|
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|
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| Router | Endpoints |
|
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|--------|-----------|
|
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| **Models** (`/api/models`) | `GET /` - list models; `GET /{name}` - model details |
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| **MCP** (`/api/mcp`) | `GET /config` - get config; `PUT /config` - update config (saves to extensions_config.json) |
|
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| **Skills** (`/api/skills`) | `GET /` - list skills; `GET /{name}` - details; `PUT /{name}` - update enabled; `POST /install` - install from .skill archive (accepts standard optional frontmatter like `version`, `author`, `compatibility`) |
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| **Memory** (`/api/memory`) | `GET /` - memory data; `POST /reload` - force reload; `GET /config` - config; `GET /status` - config + data |
|
||||
| **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
|
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| **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
|
||||
| **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
|
||||
| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized before JSON parsing |
|
||||
|
||||
Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
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||||
|
||||
### Sandbox System (`packages/harness/deerflow/sandbox/`)
|
||||
|
||||
**Interface**: Abstract `Sandbox` with `execute_command`, `read_file`, `write_file`, `list_dir`
|
||||
**Provider Pattern**: `SandboxProvider` with `acquire`, `get`, `release` lifecycle
|
||||
**Implementations**:
|
||||
- `LocalSandboxProvider` - Singleton local filesystem execution with path mappings
|
||||
- `AioSandboxProvider` (`packages/harness/deerflow/community/`) - Docker-based isolation
|
||||
|
||||
**Virtual Path System**:
|
||||
- Agent sees: `/mnt/user-data/{workspace,uploads,outputs}`, `/mnt/skills`
|
||||
- Physical: `backend/.deer-flow/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
|
||||
- Translation: `replace_virtual_path()` / `replace_virtual_paths_in_command()`
|
||||
- Detection: `is_local_sandbox()` checks `sandbox_id == "local"`
|
||||
|
||||
**Sandbox Tools** (in `packages/harness/deerflow/sandbox/tools.py`):
|
||||
- `bash` - Execute commands with path translation and error handling
|
||||
- `ls` - Directory listing (tree format, max 2 levels)
|
||||
- `read_file` - Read file contents with optional line range
|
||||
- `write_file` - Write/append to files, creates directories
|
||||
- `str_replace` - Substring replacement (single or all occurrences); same-path serialization is scoped to `(sandbox.id, path)` so isolated sandboxes do not contend on identical virtual paths inside one process
|
||||
|
||||
### Subagent System (`packages/harness/deerflow/subagents/`)
|
||||
|
||||
**Built-in Agents**: `general-purpose` (all tools except `task`) and `bash` (command specialist)
|
||||
**Execution**: Dual thread pool - `_scheduler_pool` (3 workers) + `_execution_pool` (3 workers)
|
||||
**Concurrency**: `MAX_CONCURRENT_SUBAGENTS = 3` enforced by `SubagentLimitMiddleware` (truncates excess tool calls in `after_model`), 15-minute timeout
|
||||
**Flow**: `task()` tool → `SubagentExecutor` → background thread → poll 5s → SSE events → result
|
||||
**Events**: `task_started`, `task_running`, `task_completed`/`task_failed`/`task_timed_out`
|
||||
|
||||
### Tool System (`packages/harness/deerflow/tools/`)
|
||||
|
||||
`get_available_tools(groups, include_mcp, model_name, subagent_enabled)` assembles:
|
||||
1. **Config-defined tools** - Resolved from `config.yaml` via `resolve_variable()`
|
||||
2. **MCP tools** - From enabled MCP servers (lazy initialized, cached with mtime invalidation)
|
||||
3. **Built-in tools**:
|
||||
- `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
|
||||
- `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
|
||||
- `view_image` - Read image as base64 (added only if model supports vision)
|
||||
4. **Subagent tool** (if enabled):
|
||||
- `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
|
||||
|
||||
**Community tools** (`packages/harness/deerflow/community/`):
|
||||
- `tavily/` - Web search (5 results default) and web fetch (4KB limit)
|
||||
- `jina_ai/` - Web fetch via Jina reader API with readability extraction
|
||||
- `firecrawl/` - Web scraping via Firecrawl API
|
||||
|
||||
**ACP agent tools**:
|
||||
- `invoke_acp_agent` - Invokes external ACP-compatible agents from `config.yaml`
|
||||
- ACP launchers must be real ACP adapters. The standard `codex` CLI is not ACP-compatible by itself; configure a wrapper such as `npx -y @zed-industries/codex-acp` or an installed `codex-acp` binary
|
||||
- Missing ACP executables now return an actionable error message instead of a raw `[Errno 2]`
|
||||
- Each ACP agent uses a per-thread workspace at `{base_dir}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
|
||||
- `image_search/` - Image search via DuckDuckGo
|
||||
|
||||
### MCP System (`packages/harness/deerflow/mcp/`)
|
||||
|
||||
- Uses `langchain-mcp-adapters` `MultiServerMCPClient` for multi-server management
|
||||
- **Lazy initialization**: Tools loaded on first use via `get_cached_mcp_tools()`
|
||||
- **Cache invalidation**: Detects config file changes via mtime comparison
|
||||
- **Transports**: stdio (command-based), SSE, HTTP
|
||||
- **OAuth (HTTP/SSE)**: Supports token endpoint flows (`client_credentials`, `refresh_token`) with automatic token refresh + Authorization header injection
|
||||
- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
|
||||
|
||||
### Skills System (`packages/harness/deerflow/skills/`)
|
||||
|
||||
- **Location**: `deer-flow/skills/{public,custom}/`
|
||||
- **Format**: Directory with `SKILL.md` (YAML frontmatter: name, description, license, allowed-tools)
|
||||
- **Loading**: `load_skills()` recursively scans `skills/{public,custom}` for `SKILL.md`, parses metadata, and reads enabled state from extensions_config.json
|
||||
- **Injection**: Enabled skills listed in agent system prompt with container paths
|
||||
- **Installation**: `POST /api/skills/install` extracts .skill ZIP archive to custom/ directory
|
||||
|
||||
### Model Factory (`packages/harness/deerflow/models/factory.py`)
|
||||
|
||||
- `create_chat_model(name, thinking_enabled)` instantiates LLM from config via reflection
|
||||
- Supports `thinking_enabled` flag with per-model `when_thinking_enabled` overrides
|
||||
- Supports vLLM-style thinking toggles via `when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking` for Qwen reasoning models, while normalizing legacy `thinking` configs for backward compatibility
|
||||
- Supports `supports_vision` flag for image understanding models
|
||||
- Config values starting with `$` resolved as environment variables
|
||||
- Missing provider modules surface actionable install hints from reflection resolvers (for example `uv add langchain-google-genai`)
|
||||
|
||||
### vLLM Provider (`packages/harness/deerflow/models/vllm_provider.py`)
|
||||
|
||||
- `VllmChatModel` subclasses `langchain_openai:ChatOpenAI` for vLLM 0.19.0 OpenAI-compatible endpoints
|
||||
- Preserves vLLM's non-standard assistant `reasoning` field on full responses, streaming deltas, and follow-up tool-call turns
|
||||
- Designed for configs that enable thinking through `extra_body.chat_template_kwargs.enable_thinking` on vLLM 0.19.0 Qwen reasoning models, while accepting the older `thinking` alias
|
||||
|
||||
### IM Channels System (`app/channels/`)
|
||||
|
||||
Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
|
||||
|
||||
**Architecture**: Channels communicate with the LangGraph Server through `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side.
|
||||
|
||||
**Components**:
|
||||
- `message_bus.py` - Async pub/sub hub (`InboundMessage` → queue → dispatcher; `OutboundMessage` → callbacks → channels)
|
||||
- `store.py` - JSON-file persistence mapping `channel_name:chat_id[:topic_id]` → `thread_id` (keys are `channel:chat` for root conversations and `channel:chat:topic` for threaded conversations)
|
||||
- `manager.py` - Core dispatcher: creates threads via `client.threads.create()`, routes commands, keeps Slack/Telegram on `client.runs.wait()`, and uses `client.runs.stream(["messages-tuple", "values"])` for Feishu incremental outbound updates
|
||||
- `base.py` - Abstract `Channel` base class (start/stop/send lifecycle)
|
||||
- `service.py` - Manages lifecycle of all configured channels from `config.yaml`
|
||||
- `slack.py` / `feishu.py` / `telegram.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place)
|
||||
|
||||
**Message Flow**:
|
||||
1. External platform -> Channel impl -> `MessageBus.publish_inbound()`
|
||||
2. `ChannelManager._dispatch_loop()` consumes from queue
|
||||
3. For chat: look up/create thread on LangGraph Server
|
||||
4. Feishu chat: `runs.stream()` → accumulate AI text → publish multiple outbound updates (`is_final=False`) → publish final outbound (`is_final=True`)
|
||||
5. Slack/Telegram chat: `runs.wait()` → extract final response → publish outbound
|
||||
6. Feishu channel sends one running reply card up front, then patches the same card for each outbound update (card JSON sets `config.update_multi=true` for Feishu's patch API requirement)
|
||||
7. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
|
||||
8. Outbound → channel callbacks → platform reply
|
||||
|
||||
**Configuration** (`config.yaml` -> `channels`):
|
||||
- `langgraph_url` - LangGraph Server URL (default: `http://localhost:2024`)
|
||||
- `gateway_url` - Gateway API URL for auxiliary commands (default: `http://localhost:8001`)
|
||||
- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://langgraph:2024` / `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
|
||||
- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token)
|
||||
|
||||
### Memory System (`packages/harness/deerflow/agents/memory/`)
|
||||
|
||||
**Components**:
|
||||
- `updater.py` - LLM-based memory updates with fact extraction, whitespace-normalized fact deduplication (trims leading/trailing whitespace before comparing), and atomic file I/O
|
||||
- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time)
|
||||
- `prompt.py` - Prompt templates for memory updates
|
||||
|
||||
**Data Structure** (stored in `backend/.deer-flow/memory.json`):
|
||||
- **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
|
||||
- **History**: `recentMonths`, `earlierContext`, `longTermBackground`
|
||||
- **Facts**: Discrete facts with `id`, `content`, `category` (preference/knowledge/context/behavior/goal), `confidence` (0-1), `createdAt`, `source`
|
||||
|
||||
**Workflow**:
|
||||
1. `MemoryMiddleware` filters messages (user inputs + final AI responses) and queues conversation
|
||||
2. Queue debounces (30s default), batches updates, deduplicates per-thread
|
||||
3. Background thread invokes LLM to extract context updates and facts
|
||||
4. Applies updates atomically (temp file + rename) with cache invalidation, skipping duplicate fact content before append
|
||||
5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
|
||||
|
||||
Focused regression coverage for the updater lives in `backend/tests/test_memory_updater.py`.
|
||||
|
||||
**Configuration** (`config.yaml` → `memory`):
|
||||
- `enabled` / `injection_enabled` - Master switches
|
||||
- `storage_path` - Path to memory.json
|
||||
- `debounce_seconds` - Wait time before processing (default: 30)
|
||||
- `model_name` - LLM for updates (null = default model)
|
||||
- `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
|
||||
- `max_injection_tokens` - Token limit for prompt injection (2000)
|
||||
|
||||
### Reflection System (`packages/harness/deerflow/reflection/`)
|
||||
|
||||
- `resolve_variable(path)` - Import module and return variable (e.g., `module.path:variable_name`)
|
||||
- `resolve_class(path, base_class)` - Import and validate class against base class
|
||||
|
||||
### Config Schema
|
||||
|
||||
**`config.yaml`** key sections:
|
||||
- `models[]` - LLM configs with `use` class path, `supports_thinking`, `supports_vision`, provider-specific fields
|
||||
- vLLM reasoning models should use `deerflow.models.vllm_provider:VllmChatModel`; for Qwen-style parsers prefer `when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking`, and DeerFlow will also normalize the older `thinking` alias
|
||||
- `tools[]` - Tool configs with `use` variable path and `group`
|
||||
- `tool_groups[]` - Logical groupings for tools
|
||||
- `sandbox.use` - Sandbox provider class path
|
||||
- `skills.path` / `skills.container_path` - Host and container paths to skills directory
|
||||
- `title` - Auto-title generation (enabled, max_words, max_chars, prompt_template)
|
||||
- `summarization` - Context summarization (enabled, trigger conditions, keep policy)
|
||||
- `subagents.enabled` - Master switch for subagent delegation
|
||||
- `memory` - Memory system (enabled, storage_path, debounce_seconds, model_name, max_facts, fact_confidence_threshold, injection_enabled, max_injection_tokens)
|
||||
|
||||
**`extensions_config.json`**:
|
||||
- `mcpServers` - Map of server name → config (enabled, type, command, args, env, url, headers, oauth, description)
|
||||
- `skills` - Map of skill name → state (enabled)
|
||||
|
||||
Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` methods.
|
||||
|
||||
### Embedded Client (`packages/harness/deerflow/client.py`)
|
||||
|
||||
`DeerFlowClient` provides direct in-process access to all DeerFlow capabilities without HTTP services. All return types align with the Gateway API response schemas, so consumer code works identically in HTTP and embedded modes.
|
||||
|
||||
**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
|
||||
|
||||
**Agent Conversation** (replaces LangGraph Server):
|
||||
- `chat(message, thread_id)` — synchronous, accumulates streaming deltas per message-id and returns the final AI text
|
||||
- `stream(message, thread_id)` — subscribes to LangGraph `stream_mode=["values", "messages", "custom"]` and yields `StreamEvent`:
|
||||
- `"values"` — full state snapshot (title, messages, artifacts); AI text already delivered via `messages` mode is **not** re-synthesized here to avoid duplicate deliveries
|
||||
- `"messages-tuple"` — per-chunk update: for AI text this is a **delta** (concat per `id` to rebuild the full message); tool calls and tool results are emitted once each
|
||||
- `"custom"` — forwarded from `StreamWriter`
|
||||
- `"end"` — stream finished (carries cumulative `usage` counted once per message id)
|
||||
- Agent created lazily via `create_agent()` + `_build_middlewares()`, same as `make_lead_agent`
|
||||
- Supports `checkpointer` parameter for state persistence across turns
|
||||
- `reset_agent()` forces agent recreation (e.g. after memory or skill changes)
|
||||
- See [docs/STREAMING.md](docs/STREAMING.md) for the full design: why Gateway and DeerFlowClient are parallel paths, LangGraph's `stream_mode` semantics, the per-id dedup invariants, and regression testing strategy
|
||||
|
||||
**Gateway Equivalent Methods** (replaces Gateway API):
|
||||
|
||||
| Category | Methods | Return format |
|
||||
|----------|---------|---------------|
|
||||
| Models | `list_models()`, `get_model(name)` | `{"models": [...]}`, `{name, display_name, ...}` |
|
||||
| MCP | `get_mcp_config()`, `update_mcp_config(servers)` | `{"mcp_servers": {...}}` |
|
||||
| Skills | `list_skills()`, `get_skill(name)`, `update_skill(name, enabled)`, `install_skill(path)` | `{"skills": [...]}` |
|
||||
| Memory | `get_memory()`, `reload_memory()`, `get_memory_config()`, `get_memory_status()` | dict |
|
||||
| Uploads | `upload_files(thread_id, files)`, `list_uploads(thread_id)`, `delete_upload(thread_id, filename)` | `{"success": true, "files": [...]}`, `{"files": [...], "count": N}` |
|
||||
| Artifacts | `get_artifact(thread_id, path)` → `(bytes, mime_type)` | tuple |
|
||||
|
||||
**Key difference from Gateway**: Upload accepts local `Path` objects instead of HTTP `UploadFile`, rejects directory paths before copying, and reuses a single worker when document conversion must run inside an active event loop. Artifact returns `(bytes, mime_type)` instead of HTTP Response. The new Gateway-only thread cleanup route deletes `.deer-flow/threads/{thread_id}` after LangGraph thread deletion; there is no matching `DeerFlowClient` method yet. `update_mcp_config()` and `update_skill()` automatically invalidate the cached agent.
|
||||
|
||||
**Tests**: `tests/test_client.py` (77 unit tests including `TestGatewayConformance`), `tests/test_client_live.py` (live integration tests, requires config.yaml)
|
||||
|
||||
**Gateway Conformance Tests** (`TestGatewayConformance`): Validate that every dict-returning client method conforms to the corresponding Gateway Pydantic response model. Each test parses the client output through the Gateway model — if Gateway adds a required field that the client doesn't provide, Pydantic raises `ValidationError` and CI catches the drift. Covers: `ModelsListResponse`, `ModelResponse`, `SkillsListResponse`, `SkillResponse`, `SkillInstallResponse`, `McpConfigResponse`, `UploadResponse`, `MemoryConfigResponse`, `MemoryStatusResponse`.
|
||||
|
||||
## Development Workflow
|
||||
|
||||
### Test-Driven Development (TDD) — MANDATORY
|
||||
|
||||
**Every new feature or bug fix MUST be accompanied by unit tests. No exceptions.**
|
||||
|
||||
- Write tests in `backend/tests/` following the existing naming convention `test_<feature>.py`
|
||||
- Run the full suite before and after your change: `make test`
|
||||
- Tests must pass before a feature is considered complete
|
||||
- For lightweight config/utility modules, prefer pure unit tests with no external dependencies
|
||||
- If a module causes circular import issues in tests, add a `sys.modules` mock in `tests/conftest.py` (see existing example for `deerflow.subagents.executor`)
|
||||
|
||||
```bash
|
||||
# Run all tests
|
||||
make test
|
||||
|
||||
# Run a specific test file
|
||||
PYTHONPATH=. uv run pytest tests/test_<feature>.py -v
|
||||
```
|
||||
|
||||
### Running the Full Application
|
||||
|
||||
From the **project root** directory:
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
This starts all services and makes the application available at `http://localhost:2026`.
|
||||
|
||||
**All startup modes:**
|
||||
|
||||
| | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
|
||||
|---|---|---|---|---|
|
||||
| **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
|
||||
| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
|
||||
| **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
|
||||
| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
|
||||
|
||||
| Action | Local | Docker Dev | Docker Prod |
|
||||
|---|---|---|---|
|
||||
| **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
|
||||
| **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
|
||||
|
||||
Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
|
||||
|
||||
**Nginx routing**:
|
||||
- Standard mode: `/api/langgraph/*` → LangGraph Server (2024)
|
||||
- Gateway mode: `/api/langgraph/*` → Gateway embedded runtime (8001) (via envsubst)
|
||||
- `/api/*` (other) → Gateway API (8001)
|
||||
- `/` (non-API) → Frontend (3000)
|
||||
|
||||
### Running Backend Services Separately
|
||||
|
||||
From the **backend** directory:
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
Direct access (without nginx):
|
||||
- LangGraph: `http://localhost:2024`
|
||||
- Gateway: `http://localhost:8001`
|
||||
|
||||
### Frontend Configuration
|
||||
|
||||
The frontend uses environment variables to connect to backend services:
|
||||
- `NEXT_PUBLIC_LANGGRAPH_BASE_URL` - Defaults to `/api/langgraph` (through nginx)
|
||||
- `NEXT_PUBLIC_BACKEND_BASE_URL` - Defaults to empty string (through nginx)
|
||||
|
||||
When using `make dev` from root, the frontend automatically connects through nginx.
|
||||
|
||||
## Key Features
|
||||
|
||||
### File Upload
|
||||
|
||||
Multi-file upload with automatic document conversion:
|
||||
- Endpoint: `POST /api/threads/{thread_id}/uploads`
|
||||
- Supports: PDF, PPT, Excel, Word documents (converted via `markitdown`)
|
||||
- Rejects directory inputs before copying so uploads stay all-or-nothing
|
||||
- Reuses one conversion worker per request when called from an active event loop
|
||||
- Files stored in thread-isolated directories
|
||||
- Agent receives uploaded file list via `UploadsMiddleware`
|
||||
|
||||
See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.
|
||||
|
||||
### Plan Mode
|
||||
|
||||
TodoList middleware for complex multi-step tasks:
|
||||
- Controlled via runtime config: `config.configurable.is_plan_mode = True`
|
||||
- Provides `write_todos` tool for task tracking
|
||||
- One task in_progress at a time, real-time updates
|
||||
|
||||
See [docs/plan_mode_usage.md](docs/plan_mode_usage.md) for details.
|
||||
|
||||
### Context Summarization
|
||||
|
||||
Automatic conversation summarization when approaching token limits:
|
||||
- Configured in `config.yaml` under `summarization` key
|
||||
- Trigger types: tokens, messages, or fraction of max input
|
||||
- Keeps recent messages while summarizing older ones
|
||||
|
||||
See [docs/summarization.md](docs/summarization.md) for details.
|
||||
|
||||
### Vision Support
|
||||
|
||||
For models with `supports_vision: true`:
|
||||
- `ViewImageMiddleware` processes images in conversation
|
||||
- `view_image_tool` added to agent's toolset
|
||||
- Images automatically converted to base64 and injected into state
|
||||
|
||||
## Code Style
|
||||
|
||||
- Uses `ruff` for linting and formatting
|
||||
- Line length: 240 characters
|
||||
- Python 3.12+ with type hints
|
||||
- Double quotes, space indentation
|
||||
|
||||
## Documentation
|
||||
|
||||
See `docs/` directory for detailed documentation:
|
||||
- [CONFIGURATION.md](docs/CONFIGURATION.md) - Configuration options
|
||||
- [ARCHITECTURE.md](docs/ARCHITECTURE.md) - Architecture details
|
||||
- [API.md](docs/API.md) - API reference
|
||||
- [SETUP.md](docs/SETUP.md) - Setup guide
|
||||
- [FILE_UPLOAD.md](docs/FILE_UPLOAD.md) - File upload feature
|
||||
- [PATH_EXAMPLES.md](docs/PATH_EXAMPLES.md) - Path types and usage
|
||||
- [summarization.md](docs/summarization.md) - Context summarization
|
||||
- [plan_mode_usage.md](docs/plan_mode_usage.md) - Plan mode with TodoList
|
||||
426
deer-flow/backend/CONTRIBUTING.md
Normal file
426
deer-flow/backend/CONTRIBUTING.md
Normal file
@@ -0,0 +1,426 @@
|
||||
# Contributing to DeerFlow Backend
|
||||
|
||||
Thank you for your interest in contributing to DeerFlow! This document provides guidelines and instructions for contributing to the backend codebase.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Getting Started](#getting-started)
|
||||
- [Development Setup](#development-setup)
|
||||
- [Project Structure](#project-structure)
|
||||
- [Code Style](#code-style)
|
||||
- [Making Changes](#making-changes)
|
||||
- [Testing](#testing)
|
||||
- [Pull Request Process](#pull-request-process)
|
||||
- [Architecture Guidelines](#architecture-guidelines)
|
||||
|
||||
## Getting Started
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Python 3.12 or higher
|
||||
- [uv](https://docs.astral.sh/uv/) package manager
|
||||
- Git
|
||||
- Docker (optional, for Docker sandbox testing)
|
||||
|
||||
### Fork and Clone
|
||||
|
||||
1. Fork the repository on GitHub
|
||||
2. Clone your fork locally:
|
||||
```bash
|
||||
git clone https://github.com/YOUR_USERNAME/deer-flow.git
|
||||
cd deer-flow
|
||||
```
|
||||
|
||||
## Development Setup
|
||||
|
||||
### Install Dependencies
|
||||
|
||||
```bash
|
||||
# From project root
|
||||
cp config.example.yaml config.yaml
|
||||
|
||||
# Install backend dependencies
|
||||
cd backend
|
||||
make install
|
||||
```
|
||||
|
||||
### Configure Environment
|
||||
|
||||
Set up your API keys for testing:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="your-api-key"
|
||||
# Add other keys as needed
|
||||
```
|
||||
|
||||
### Run the Development Server
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
backend/src/
|
||||
├── agents/ # Agent system
|
||||
│ ├── lead_agent/ # Main agent implementation
|
||||
│ │ └── agent.py # Agent factory and creation
|
||||
│ ├── middlewares/ # Agent middlewares
|
||||
│ │ ├── thread_data_middleware.py
|
||||
│ │ ├── sandbox_middleware.py
|
||||
│ │ ├── title_middleware.py
|
||||
│ │ ├── uploads_middleware.py
|
||||
│ │ ├── view_image_middleware.py
|
||||
│ │ └── clarification_middleware.py
|
||||
│ └── thread_state.py # Thread state definition
|
||||
│
|
||||
├── gateway/ # FastAPI Gateway
|
||||
│ ├── app.py # FastAPI application
|
||||
│ └── routers/ # Route handlers
|
||||
│ ├── models.py # /api/models endpoints
|
||||
│ ├── mcp.py # /api/mcp endpoints
|
||||
│ ├── skills.py # /api/skills endpoints
|
||||
│ ├── artifacts.py # /api/threads/.../artifacts
|
||||
│ └── uploads.py # /api/threads/.../uploads
|
||||
│
|
||||
├── sandbox/ # Sandbox execution
|
||||
│ ├── __init__.py # Sandbox interface
|
||||
│ ├── local.py # Local sandbox provider
|
||||
│ └── tools.py # Sandbox tools (bash, file ops)
|
||||
│
|
||||
├── tools/ # Agent tools
|
||||
│ └── builtins/ # Built-in tools
|
||||
│ ├── present_file_tool.py
|
||||
│ ├── ask_clarification_tool.py
|
||||
│ └── view_image_tool.py
|
||||
│
|
||||
├── mcp/ # MCP integration
|
||||
│ └── manager.py # MCP server management
|
||||
│
|
||||
├── models/ # Model system
|
||||
│ └── factory.py # Model factory
|
||||
│
|
||||
├── skills/ # Skills system
|
||||
│ └── loader.py # Skills loader
|
||||
│
|
||||
├── config/ # Configuration
|
||||
│ ├── app_config.py # Main app config
|
||||
│ ├── extensions_config.py # Extensions config
|
||||
│ └── summarization_config.py
|
||||
│
|
||||
├── community/ # Community tools
|
||||
│ ├── tavily/ # Tavily web search
|
||||
│ ├── jina/ # Jina web fetch
|
||||
│ ├── firecrawl/ # Firecrawl scraping
|
||||
│ └── aio_sandbox/ # Docker sandbox
|
||||
│
|
||||
├── reflection/ # Dynamic loading
|
||||
│ └── __init__.py # Module resolution
|
||||
│
|
||||
└── utils/ # Utilities
|
||||
└── __init__.py
|
||||
```
|
||||
|
||||
## Code Style
|
||||
|
||||
### Linting and Formatting
|
||||
|
||||
We use `ruff` for both linting and formatting:
|
||||
|
||||
```bash
|
||||
# Check for issues
|
||||
make lint
|
||||
|
||||
# Auto-fix and format
|
||||
make format
|
||||
```
|
||||
|
||||
### Style Guidelines
|
||||
|
||||
- **Line length**: 240 characters maximum
|
||||
- **Python version**: 3.12+ features allowed
|
||||
- **Type hints**: Use type hints for function signatures
|
||||
- **Quotes**: Double quotes for strings
|
||||
- **Indentation**: 4 spaces (no tabs)
|
||||
- **Imports**: Group by standard library, third-party, local
|
||||
|
||||
### Docstrings
|
||||
|
||||
Use docstrings for public functions and classes:
|
||||
|
||||
```python
|
||||
def create_chat_model(name: str, thinking_enabled: bool = False) -> BaseChatModel:
|
||||
"""Create a chat model instance from configuration.
|
||||
|
||||
Args:
|
||||
name: The model name as defined in config.yaml
|
||||
thinking_enabled: Whether to enable extended thinking
|
||||
|
||||
Returns:
|
||||
A configured LangChain chat model instance
|
||||
|
||||
Raises:
|
||||
ValueError: If the model name is not found in configuration
|
||||
"""
|
||||
...
|
||||
```
|
||||
|
||||
## Making Changes
|
||||
|
||||
### Branch Naming
|
||||
|
||||
Use descriptive branch names:
|
||||
|
||||
- `feature/add-new-tool` - New features
|
||||
- `fix/sandbox-timeout` - Bug fixes
|
||||
- `docs/update-readme` - Documentation
|
||||
- `refactor/config-system` - Code refactoring
|
||||
|
||||
### Commit Messages
|
||||
|
||||
Write clear, concise commit messages:
|
||||
|
||||
```
|
||||
feat: add support for Claude 3.5 model
|
||||
|
||||
- Add model configuration in config.yaml
|
||||
- Update model factory to handle Claude-specific settings
|
||||
- Add tests for new model
|
||||
```
|
||||
|
||||
Prefix types:
|
||||
- `feat:` - New feature
|
||||
- `fix:` - Bug fix
|
||||
- `docs:` - Documentation
|
||||
- `refactor:` - Code refactoring
|
||||
- `test:` - Tests
|
||||
- `chore:` - Build/config changes
|
||||
|
||||
## Testing
|
||||
|
||||
### Running Tests
|
||||
|
||||
```bash
|
||||
uv run pytest
|
||||
```
|
||||
|
||||
### Writing Tests
|
||||
|
||||
Place tests in the `tests/` directory mirroring the source structure:
|
||||
|
||||
```
|
||||
tests/
|
||||
├── test_models/
|
||||
│ └── test_factory.py
|
||||
├── test_sandbox/
|
||||
│ └── test_local.py
|
||||
└── test_gateway/
|
||||
└── test_models_router.py
|
||||
```
|
||||
|
||||
Example test:
|
||||
|
||||
```python
|
||||
import pytest
|
||||
from deerflow.models.factory import create_chat_model
|
||||
|
||||
def test_create_chat_model_with_valid_name():
|
||||
"""Test that a valid model name creates a model instance."""
|
||||
model = create_chat_model("gpt-4")
|
||||
assert model is not None
|
||||
|
||||
def test_create_chat_model_with_invalid_name():
|
||||
"""Test that an invalid model name raises ValueError."""
|
||||
with pytest.raises(ValueError):
|
||||
create_chat_model("nonexistent-model")
|
||||
```
|
||||
|
||||
## Pull Request Process
|
||||
|
||||
### Before Submitting
|
||||
|
||||
1. **Ensure tests pass**: `uv run pytest`
|
||||
2. **Run linter**: `make lint`
|
||||
3. **Format code**: `make format`
|
||||
4. **Update documentation** if needed
|
||||
|
||||
### PR Description
|
||||
|
||||
Include in your PR description:
|
||||
|
||||
- **What**: Brief description of changes
|
||||
- **Why**: Motivation for the change
|
||||
- **How**: Implementation approach
|
||||
- **Testing**: How you tested the changes
|
||||
|
||||
### Review Process
|
||||
|
||||
1. Submit PR with clear description
|
||||
2. Address review feedback
|
||||
3. Ensure CI passes
|
||||
4. Maintainer will merge when approved
|
||||
|
||||
## Architecture Guidelines
|
||||
|
||||
### Adding New Tools
|
||||
|
||||
1. Create tool in `packages/harness/deerflow/tools/builtins/` or `packages/harness/deerflow/community/`:
|
||||
|
||||
```python
|
||||
# packages/harness/deerflow/tools/builtins/my_tool.py
|
||||
from langchain_core.tools import tool
|
||||
|
||||
@tool
|
||||
def my_tool(param: str) -> str:
|
||||
"""Tool description for the agent.
|
||||
|
||||
Args:
|
||||
param: Description of the parameter
|
||||
|
||||
Returns:
|
||||
Description of return value
|
||||
"""
|
||||
return f"Result: {param}"
|
||||
```
|
||||
|
||||
2. Register in `config.yaml`:
|
||||
|
||||
```yaml
|
||||
tools:
|
||||
- name: my_tool
|
||||
group: my_group
|
||||
use: deerflow.tools.builtins.my_tool:my_tool
|
||||
```
|
||||
|
||||
### Adding New Middleware
|
||||
|
||||
1. Create middleware in `packages/harness/deerflow/agents/middlewares/`:
|
||||
|
||||
```python
|
||||
# packages/harness/deerflow/agents/middlewares/my_middleware.py
|
||||
from langchain.agents.middleware import BaseMiddleware
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
|
||||
class MyMiddleware(BaseMiddleware):
|
||||
"""Middleware description."""
|
||||
|
||||
def transform_state(self, state: dict, config: RunnableConfig) -> dict:
|
||||
"""Transform the state before agent execution."""
|
||||
# Modify state as needed
|
||||
return state
|
||||
```
|
||||
|
||||
2. Register in `packages/harness/deerflow/agents/lead_agent/agent.py`:
|
||||
|
||||
```python
|
||||
middlewares = [
|
||||
ThreadDataMiddleware(),
|
||||
SandboxMiddleware(),
|
||||
MyMiddleware(), # Add your middleware
|
||||
TitleMiddleware(),
|
||||
ClarificationMiddleware(),
|
||||
]
|
||||
```
|
||||
|
||||
### Adding New API Endpoints
|
||||
|
||||
1. Create router in `app/gateway/routers/`:
|
||||
|
||||
```python
|
||||
# app/gateway/routers/my_router.py
|
||||
from fastapi import APIRouter
|
||||
|
||||
router = APIRouter(prefix="/my-endpoint", tags=["my-endpoint"])
|
||||
|
||||
@router.get("/")
|
||||
async def get_items():
|
||||
"""Get all items."""
|
||||
return {"items": []}
|
||||
|
||||
@router.post("/")
|
||||
async def create_item(data: dict):
|
||||
"""Create a new item."""
|
||||
return {"created": data}
|
||||
```
|
||||
|
||||
2. Register in `app/gateway/app.py`:
|
||||
|
||||
```python
|
||||
from app.gateway.routers import my_router
|
||||
|
||||
app.include_router(my_router.router)
|
||||
```
|
||||
|
||||
### Configuration Changes
|
||||
|
||||
When adding new configuration options:
|
||||
|
||||
1. Update `packages/harness/deerflow/config/app_config.py` with new fields
|
||||
2. Add default values in `config.example.yaml`
|
||||
3. Document in `docs/CONFIGURATION.md`
|
||||
|
||||
### MCP Server Integration
|
||||
|
||||
To add support for a new MCP server:
|
||||
|
||||
1. Add configuration in `extensions_config.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"my-server": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@my-org/mcp-server"],
|
||||
"description": "My MCP Server"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
2. Update `extensions_config.example.json` with the new server
|
||||
|
||||
### Skills Development
|
||||
|
||||
To create a new skill:
|
||||
|
||||
1. Create directory in `skills/public/` or `skills/custom/`:
|
||||
|
||||
```
|
||||
skills/public/my-skill/
|
||||
└── SKILL.md
|
||||
```
|
||||
|
||||
2. Write `SKILL.md` with YAML front matter:
|
||||
|
||||
```markdown
|
||||
---
|
||||
name: My Skill
|
||||
description: What this skill does
|
||||
license: MIT
|
||||
allowed-tools:
|
||||
- read_file
|
||||
- write_file
|
||||
- bash
|
||||
---
|
||||
|
||||
# My Skill
|
||||
|
||||
Instructions for the agent when this skill is enabled...
|
||||
```
|
||||
|
||||
## Questions?
|
||||
|
||||
If you have questions about contributing:
|
||||
|
||||
1. Check existing documentation in `docs/`
|
||||
2. Look for similar issues or PRs on GitHub
|
||||
3. Open a discussion or issue on GitHub
|
||||
|
||||
Thank you for contributing to DeerFlow!
|
||||
87
deer-flow/backend/Dockerfile
Normal file
87
deer-flow/backend/Dockerfile
Normal file
@@ -0,0 +1,87 @@
|
||||
# Backend Dockerfile — multi-stage build
|
||||
# Stage 1 (builder): compiles native Python extensions with build-essential
|
||||
# Stage 2 (dev): retains toolchain for dev containers (uv sync at startup)
|
||||
# Stage 3 (runtime): clean image without compiler toolchain for production
|
||||
|
||||
# UV source image (override for restricted networks that cannot reach ghcr.io)
|
||||
ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.7.20
|
||||
FROM ${UV_IMAGE} AS uv-source
|
||||
|
||||
# ── Stage 1: Builder ──────────────────────────────────────────────────────────
|
||||
FROM python:3.12-slim-bookworm AS builder
|
||||
|
||||
ARG NODE_MAJOR=22
|
||||
ARG APT_MIRROR
|
||||
ARG UV_INDEX_URL
|
||||
|
||||
# Optionally override apt mirror for restricted networks (e.g. APT_MIRROR=mirrors.aliyun.com)
|
||||
RUN if [ -n "${APT_MIRROR}" ]; then \
|
||||
sed -i "s|deb.debian.org|${APT_MIRROR}|g" /etc/apt/sources.list.d/debian.sources 2>/dev/null || true; \
|
||||
sed -i "s|deb.debian.org|${APT_MIRROR}|g" /etc/apt/sources.list 2>/dev/null || true; \
|
||||
fi
|
||||
|
||||
# Install build tools + Node.js (build-essential needed for native Python extensions)
|
||||
RUN apt-get update && apt-get install -y \
|
||||
curl \
|
||||
build-essential \
|
||||
gnupg \
|
||||
ca-certificates \
|
||||
&& mkdir -p /etc/apt/keyrings \
|
||||
&& curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
|
||||
&& echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_${NODE_MAJOR}.x nodistro main" > /etc/apt/sources.list.d/nodesource.list \
|
||||
&& apt-get update \
|
||||
&& apt-get install -y nodejs \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install uv (source image overridable via UV_IMAGE build arg)
|
||||
COPY --from=uv-source /uv /uvx /usr/local/bin/
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Copy backend source code
|
||||
COPY backend ./backend
|
||||
|
||||
# Install dependencies with cache mount
|
||||
RUN --mount=type=cache,target=/root/.cache/uv \
|
||||
sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync"
|
||||
|
||||
# ── Stage 2: Dev ──────────────────────────────────────────────────────────────
|
||||
# Retains compiler toolchain from builder so startup-time `uv sync` can build
|
||||
# source distributions in development containers.
|
||||
FROM builder AS dev
|
||||
|
||||
# Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
|
||||
COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
|
||||
|
||||
EXPOSE 8001 2024
|
||||
|
||||
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
|
||||
|
||||
# ── Stage 3: Runtime ──────────────────────────────────────────────────────────
|
||||
# Clean image without build-essential — reduces size (~200 MB) and attack surface.
|
||||
FROM python:3.12-slim-bookworm
|
||||
|
||||
# Copy Node.js runtime from builder (provides npx for MCP servers)
|
||||
COPY --from=builder /usr/bin/node /usr/bin/node
|
||||
COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
|
||||
RUN ln -s ../lib/node_modules/npm/bin/npm-cli.js /usr/bin/npm \
|
||||
&& ln -s ../lib/node_modules/npm/bin/npx-cli.js /usr/bin/npx
|
||||
|
||||
# Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
|
||||
COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
|
||||
|
||||
# Install uv (source image overridable via UV_IMAGE build arg)
|
||||
COPY --from=uv-source /uv /uvx /usr/local/bin/
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Copy backend with pre-built virtualenv from builder
|
||||
COPY --from=builder /app/backend ./backend
|
||||
|
||||
# Expose ports (gateway: 8001, langgraph: 2024)
|
||||
EXPOSE 8001 2024
|
||||
|
||||
# Default command (can be overridden in docker-compose)
|
||||
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run --no-sync uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
|
||||
18
deer-flow/backend/Makefile
Normal file
18
deer-flow/backend/Makefile
Normal file
@@ -0,0 +1,18 @@
|
||||
install:
|
||||
uv sync
|
||||
|
||||
dev:
|
||||
uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
|
||||
|
||||
gateway:
|
||||
PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
|
||||
|
||||
test:
|
||||
PYTHONPATH=. uv run pytest tests/ -v
|
||||
|
||||
lint:
|
||||
uvx ruff check .
|
||||
uvx ruff format --check .
|
||||
|
||||
format:
|
||||
uvx ruff check . --fix && uvx ruff format .
|
||||
418
deer-flow/backend/README.md
Normal file
418
deer-flow/backend/README.md
Normal file
@@ -0,0 +1,418 @@
|
||||
# DeerFlow Backend
|
||||
|
||||
DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent memory, and extensible tool integration. The backend enables AI agents to execute code, browse the web, manage files, delegate tasks to subagents, and retain context across conversations - all in isolated, per-thread environments.
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────┐
|
||||
│ Nginx (Port 2026) │
|
||||
│ Unified reverse proxy │
|
||||
└───────┬──────────────────┬───────────┘
|
||||
│ │
|
||||
/api/langgraph/* │ │ /api/* (other)
|
||||
▼ ▼
|
||||
┌────────────────────┐ ┌────────────────────────┐
|
||||
│ LangGraph Server │ │ Gateway API (8001) │
|
||||
│ (Port 2024) │ │ FastAPI REST │
|
||||
│ │ │ │
|
||||
│ ┌────────────────┐ │ │ Models, MCP, Skills, │
|
||||
│ │ Lead Agent │ │ │ Memory, Uploads, │
|
||||
│ │ ┌──────────┐ │ │ │ Artifacts │
|
||||
│ │ │Middleware│ │ │ └────────────────────────┘
|
||||
│ │ │ Chain │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ │ ┌──────────┐ │ │
|
||||
│ │ │ Tools │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ │ ┌──────────┐ │ │
|
||||
│ │ │Subagents │ │ │
|
||||
│ │ └──────────┘ │ │
|
||||
│ └────────────────┘ │
|
||||
└────────────────────┘
|
||||
```
|
||||
|
||||
**Request Routing** (via Nginx):
|
||||
- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
|
||||
- `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup
|
||||
- `/` (non-API) → Frontend - Next.js web interface
|
||||
|
||||
---
|
||||
|
||||
## Core Components
|
||||
|
||||
### Lead Agent
|
||||
|
||||
The single LangGraph agent (`lead_agent`) is the runtime entry point, created via `make_lead_agent(config)`. It combines:
|
||||
|
||||
- **Dynamic model selection** with thinking and vision support
|
||||
- **Middleware chain** for cross-cutting concerns (9 middlewares)
|
||||
- **Tool system** with sandbox, MCP, community, and built-in tools
|
||||
- **Subagent delegation** for parallel task execution
|
||||
- **System prompt** with skills injection, memory context, and working directory guidance
|
||||
|
||||
### Middleware Chain
|
||||
|
||||
Middlewares execute in strict order, each handling a specific concern:
|
||||
|
||||
| # | Middleware | Purpose |
|
||||
|---|-----------|---------|
|
||||
| 1 | **ThreadDataMiddleware** | Creates per-thread isolated directories (workspace, uploads, outputs) |
|
||||
| 2 | **UploadsMiddleware** | Injects newly uploaded files into conversation context |
|
||||
| 3 | **SandboxMiddleware** | Acquires sandbox environment for code execution |
|
||||
| 4 | **SummarizationMiddleware** | Reduces context when approaching token limits (optional) |
|
||||
| 5 | **TodoListMiddleware** | Tracks multi-step tasks in plan mode (optional) |
|
||||
| 6 | **TitleMiddleware** | Auto-generates conversation titles after first exchange |
|
||||
| 7 | **MemoryMiddleware** | Queues conversations for async memory extraction |
|
||||
| 8 | **ViewImageMiddleware** | Injects image data for vision-capable models (conditional) |
|
||||
| 9 | **ClarificationMiddleware** | Intercepts clarification requests and interrupts execution (must be last) |
|
||||
|
||||
### Sandbox System
|
||||
|
||||
Per-thread isolated execution with virtual path translation:
|
||||
|
||||
- **Abstract interface**: `execute_command`, `read_file`, `write_file`, `list_dir`
|
||||
- **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/)
|
||||
- **Virtual paths**: `/mnt/user-data/{workspace,uploads,outputs}` → thread-specific physical directories
|
||||
- **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory
|
||||
- **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths
|
||||
- **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match
|
||||
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
|
||||
|
||||
### Subagent System
|
||||
|
||||
Async task delegation with concurrent execution:
|
||||
|
||||
- **Built-in agents**: `general-purpose` (full toolset) and `bash` (command specialist, exposed only when shell access is available)
|
||||
- **Concurrency**: Max 3 subagents per turn, 15-minute timeout
|
||||
- **Execution**: Background thread pools with status tracking and SSE events
|
||||
- **Flow**: Agent calls `task()` tool → executor runs subagent in background → polls for completion → returns result
|
||||
|
||||
### Memory System
|
||||
|
||||
LLM-powered persistent context retention across conversations:
|
||||
|
||||
- **Automatic extraction**: Analyzes conversations for user context, facts, and preferences
|
||||
- **Structured storage**: User context (work, personal, top-of-mind), history, and confidence-scored facts
|
||||
- **Debounced updates**: Batches updates to minimize LLM calls (configurable wait time)
|
||||
- **System prompt injection**: Top facts + context injected into agent prompts
|
||||
- **Storage**: JSON file with mtime-based cache invalidation
|
||||
|
||||
### Tool Ecosystem
|
||||
|
||||
| Category | Tools |
|
||||
|----------|-------|
|
||||
| **Sandbox** | `bash`, `ls`, `read_file`, `write_file`, `str_replace` |
|
||||
| **Built-in** | `present_files`, `ask_clarification`, `view_image`, `task` (subagent) |
|
||||
| **Community** | Tavily (web search), Jina AI (web fetch), Firecrawl (scraping), DuckDuckGo (image search) |
|
||||
| **MCP** | Any Model Context Protocol server (stdio, SSE, HTTP transports) |
|
||||
| **Skills** | Domain-specific workflows injected via system prompt |
|
||||
|
||||
### Gateway API
|
||||
|
||||
FastAPI application providing REST endpoints for frontend integration:
|
||||
|
||||
| Route | Purpose |
|
||||
|-------|---------|
|
||||
| `GET /api/models` | List available LLM models |
|
||||
| `GET/PUT /api/mcp/config` | Manage MCP server configurations |
|
||||
| `GET/PUT /api/skills` | List and manage skills |
|
||||
| `POST /api/skills/install` | Install skill from `.skill` archive |
|
||||
| `GET /api/memory` | Retrieve memory data |
|
||||
| `POST /api/memory/reload` | Force memory reload |
|
||||
| `GET /api/memory/config` | Memory configuration |
|
||||
| `GET /api/memory/status` | Combined config + data |
|
||||
| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
|
||||
| `GET /api/threads/{id}/uploads/list` | List uploaded files |
|
||||
| `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
|
||||
| `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
|
||||
|
||||
### IM Channels
|
||||
|
||||
The IM bridge supports Feishu, Slack, and Telegram. Slack and Telegram still use the final `runs.wait()` response path, while Feishu now streams through `runs.stream(["messages-tuple", "values"])` and updates a single in-thread card in place.
|
||||
|
||||
For Feishu card updates, DeerFlow stores the running card's `message_id` per inbound message and patches that same card until the run finishes, preserving the existing `OK` / `DONE` reaction flow.
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Python 3.12+
|
||||
- [uv](https://docs.astral.sh/uv/) package manager
|
||||
- API keys for your chosen LLM provider
|
||||
|
||||
### Installation
|
||||
|
||||
```bash
|
||||
cd deer-flow
|
||||
|
||||
# Copy configuration files
|
||||
cp config.example.yaml config.yaml
|
||||
|
||||
# Install backend dependencies
|
||||
cd backend
|
||||
make install
|
||||
```
|
||||
|
||||
### Configuration
|
||||
|
||||
Edit `config.yaml` in the project root:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4o
|
||||
display_name: GPT-4o
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-4o
|
||||
api_key: $OPENAI_API_KEY
|
||||
supports_thinking: false
|
||||
supports_vision: true
|
||||
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
supports_vision: true
|
||||
```
|
||||
|
||||
Set your API keys:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="your-api-key-here"
|
||||
```
|
||||
|
||||
### Running
|
||||
|
||||
**Full Application** (from project root):
|
||||
|
||||
```bash
|
||||
make dev # Starts LangGraph + Gateway + Frontend + Nginx
|
||||
```
|
||||
|
||||
Access at: http://localhost:2026
|
||||
|
||||
**Backend Only** (from backend directory):
|
||||
|
||||
```bash
|
||||
# Terminal 1: LangGraph server
|
||||
make dev
|
||||
|
||||
# Terminal 2: Gateway API
|
||||
make gateway
|
||||
```
|
||||
|
||||
Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
backend/
|
||||
├── src/
|
||||
│ ├── agents/ # Agent system
|
||||
│ │ ├── lead_agent/ # Main agent (factory, prompts)
|
||||
│ │ ├── middlewares/ # 9 middleware components
|
||||
│ │ ├── memory/ # Memory extraction & storage
|
||||
│ │ └── thread_state.py # ThreadState schema
|
||||
│ ├── gateway/ # FastAPI Gateway API
|
||||
│ │ ├── app.py # Application setup
|
||||
│ │ └── routers/ # 6 route modules
|
||||
│ ├── sandbox/ # Sandbox execution
|
||||
│ │ ├── local/ # Local filesystem provider
|
||||
│ │ ├── sandbox.py # Abstract interface
|
||||
│ │ ├── tools.py # bash, ls, read/write/str_replace
|
||||
│ │ └── middleware.py # Sandbox lifecycle
|
||||
│ ├── subagents/ # Subagent delegation
|
||||
│ │ ├── builtins/ # general-purpose, bash agents
|
||||
│ │ ├── executor.py # Background execution engine
|
||||
│ │ └── registry.py # Agent registry
|
||||
│ ├── tools/builtins/ # Built-in tools
|
||||
│ ├── mcp/ # MCP protocol integration
|
||||
│ ├── models/ # Model factory
|
||||
│ ├── skills/ # Skill discovery & loading
|
||||
│ ├── config/ # Configuration system
|
||||
│ ├── community/ # Community tools & providers
|
||||
│ ├── reflection/ # Dynamic module loading
|
||||
│ └── utils/ # Utilities
|
||||
├── docs/ # Documentation
|
||||
├── tests/ # Test suite
|
||||
├── langgraph.json # LangGraph server configuration
|
||||
├── pyproject.toml # Python dependencies
|
||||
├── Makefile # Development commands
|
||||
└── Dockerfile # Container build
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Main Configuration (`config.yaml`)
|
||||
|
||||
Place in project root. Config values starting with `$` resolve as environment variables.
|
||||
|
||||
Key sections:
|
||||
- `models` - LLM configurations with class paths, API keys, thinking/vision flags
|
||||
- `tools` - Tool definitions with module paths and groups
|
||||
- `tool_groups` - Logical tool groupings
|
||||
- `sandbox` - Execution environment provider
|
||||
- `skills` - Skills directory paths
|
||||
- `title` - Auto-title generation settings
|
||||
- `summarization` - Context summarization settings
|
||||
- `subagents` - Subagent system (enabled/disabled)
|
||||
- `memory` - Memory system settings (enabled, storage, debounce, facts limits)
|
||||
|
||||
Provider note:
|
||||
- `models[*].use` references provider classes by module path (for example `langchain_openai:ChatOpenAI`).
|
||||
- If a provider module is missing, DeerFlow now returns an actionable error with install guidance (for example `uv add langchain-google-genai`).
|
||||
|
||||
### Extensions Configuration (`extensions_config.json`)
|
||||
|
||||
MCP servers and skill states in a single file:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"}
|
||||
},
|
||||
"secure-http": {
|
||||
"enabled": true,
|
||||
"type": "http",
|
||||
"url": "https://api.example.com/mcp",
|
||||
"oauth": {
|
||||
"enabled": true,
|
||||
"token_url": "https://auth.example.com/oauth/token",
|
||||
"grant_type": "client_credentials",
|
||||
"client_id": "$MCP_OAUTH_CLIENT_ID",
|
||||
"client_secret": "$MCP_OAUTH_CLIENT_SECRET"
|
||||
}
|
||||
}
|
||||
},
|
||||
"skills": {
|
||||
"pdf-processing": {"enabled": true}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Environment Variables
|
||||
|
||||
- `DEER_FLOW_CONFIG_PATH` - Override config.yaml location
|
||||
- `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Override extensions_config.json location
|
||||
- Model API keys: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `DEEPSEEK_API_KEY`, etc.
|
||||
- Tool API keys: `TAVILY_API_KEY`, `GITHUB_TOKEN`, etc.
|
||||
|
||||
### LangSmith Tracing
|
||||
|
||||
DeerFlow has built-in [LangSmith](https://smith.langchain.com) integration for observability. When enabled, all LLM calls, agent runs, tool executions, and middleware processing are traced and visible in the LangSmith dashboard.
|
||||
|
||||
**Setup:**
|
||||
|
||||
1. Sign up at [smith.langchain.com](https://smith.langchain.com) and create a project.
|
||||
2. Add the following to your `.env` file in the project root:
|
||||
|
||||
```bash
|
||||
LANGSMITH_TRACING=true
|
||||
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
|
||||
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
|
||||
LANGSMITH_PROJECT=xxx
|
||||
```
|
||||
|
||||
**Legacy variables:** The `LANGCHAIN_TRACING_V2`, `LANGCHAIN_API_KEY`, `LANGCHAIN_PROJECT`, and `LANGCHAIN_ENDPOINT` variables are also supported for backward compatibility. `LANGSMITH_*` variables take precedence when both are set.
|
||||
|
||||
### Langfuse Tracing
|
||||
|
||||
DeerFlow also supports [Langfuse](https://langfuse.com) observability for LangChain-compatible runs.
|
||||
|
||||
Add the following to your `.env` file:
|
||||
|
||||
```bash
|
||||
LANGFUSE_TRACING=true
|
||||
LANGFUSE_PUBLIC_KEY=pk-lf-xxxxxxxxxxxxxxxx
|
||||
LANGFUSE_SECRET_KEY=sk-lf-xxxxxxxxxxxxxxxx
|
||||
LANGFUSE_BASE_URL=https://cloud.langfuse.com
|
||||
```
|
||||
|
||||
If you are using a self-hosted Langfuse deployment, set `LANGFUSE_BASE_URL` to your Langfuse host.
|
||||
|
||||
### Dual Provider Behavior
|
||||
|
||||
If both LangSmith and Langfuse are enabled, DeerFlow initializes and attaches both callbacks so the same run data is reported to both systems.
|
||||
|
||||
If a provider is explicitly enabled but required credentials are missing, or the provider callback cannot be initialized, DeerFlow raises an error when tracing is initialized during model creation instead of silently disabling tracing.
|
||||
|
||||
**Docker:** In `docker-compose.yaml`, tracing is disabled by default (`LANGSMITH_TRACING=false`). Set `LANGSMITH_TRACING=true` and/or `LANGFUSE_TRACING=true` in your `.env`, together with the required credentials, to enable tracing in containerized deployments.
|
||||
|
||||
---
|
||||
|
||||
## Development
|
||||
|
||||
### Commands
|
||||
|
||||
```bash
|
||||
make install # Install dependencies
|
||||
make dev # Run LangGraph server (port 2024)
|
||||
make gateway # Run Gateway API (port 8001)
|
||||
make lint # Run linter (ruff)
|
||||
make format # Format code (ruff)
|
||||
```
|
||||
|
||||
### Code Style
|
||||
|
||||
- **Linter/Formatter**: `ruff`
|
||||
- **Line length**: 240 characters
|
||||
- **Python**: 3.12+ with type hints
|
||||
- **Quotes**: Double quotes
|
||||
- **Indentation**: 4 spaces
|
||||
|
||||
### Testing
|
||||
|
||||
```bash
|
||||
uv run pytest
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Technology Stack
|
||||
|
||||
- **LangGraph** (1.0.6+) - Agent framework and multi-agent orchestration
|
||||
- **LangChain** (1.2.3+) - LLM abstractions and tool system
|
||||
- **FastAPI** (0.115.0+) - Gateway REST API
|
||||
- **langchain-mcp-adapters** - Model Context Protocol support
|
||||
- **agent-sandbox** - Sandboxed code execution
|
||||
- **markitdown** - Multi-format document conversion
|
||||
- **tavily-python** / **firecrawl-py** - Web search and scraping
|
||||
|
||||
---
|
||||
|
||||
## Documentation
|
||||
|
||||
- [Configuration Guide](docs/CONFIGURATION.md)
|
||||
- [Architecture Details](docs/ARCHITECTURE.md)
|
||||
- [API Reference](docs/API.md)
|
||||
- [File Upload](docs/FILE_UPLOAD.md)
|
||||
- [Path Examples](docs/PATH_EXAMPLES.md)
|
||||
- [Context Summarization](docs/summarization.md)
|
||||
- [Plan Mode](docs/plan_mode_usage.md)
|
||||
- [Setup Guide](docs/SETUP.md)
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
See the [LICENSE](../LICENSE) file in the project root.
|
||||
|
||||
## Contributing
|
||||
|
||||
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.
|
||||
0
deer-flow/backend/app/__init__.py
Normal file
0
deer-flow/backend/app/__init__.py
Normal file
16
deer-flow/backend/app/channels/__init__.py
Normal file
16
deer-flow/backend/app/channels/__init__.py
Normal file
@@ -0,0 +1,16 @@
|
||||
"""IM Channel integration for DeerFlow.
|
||||
|
||||
Provides a pluggable channel system that connects external messaging platforms
|
||||
(Feishu/Lark, Slack, Telegram) to the DeerFlow agent via the ChannelManager,
|
||||
which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
|
||||
"""
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessage, MessageBus, OutboundMessage
|
||||
|
||||
__all__ = [
|
||||
"Channel",
|
||||
"InboundMessage",
|
||||
"MessageBus",
|
||||
"OutboundMessage",
|
||||
]
|
||||
126
deer-flow/backend/app/channels/base.py
Normal file
126
deer-flow/backend/app/channels/base.py
Normal file
@@ -0,0 +1,126 @@
|
||||
"""Abstract base class for IM channels."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Channel(ABC):
|
||||
"""Base class for all IM channel implementations.
|
||||
|
||||
Each channel connects to an external messaging platform and:
|
||||
1. Receives messages, wraps them as InboundMessage, publishes to the bus.
|
||||
2. Subscribes to outbound messages and sends replies back to the platform.
|
||||
|
||||
Subclasses must implement ``start``, ``stop``, and ``send``.
|
||||
"""
|
||||
|
||||
def __init__(self, name: str, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
self.name = name
|
||||
self.bus = bus
|
||||
self.config = config
|
||||
self._running = False
|
||||
|
||||
@property
|
||||
def is_running(self) -> bool:
|
||||
return self._running
|
||||
|
||||
# -- lifecycle ---------------------------------------------------------
|
||||
|
||||
@abstractmethod
|
||||
async def start(self) -> None:
|
||||
"""Start listening for messages from the external platform."""
|
||||
|
||||
@abstractmethod
|
||||
async def stop(self) -> None:
|
||||
"""Gracefully stop the channel."""
|
||||
|
||||
# -- outbound ----------------------------------------------------------
|
||||
|
||||
@abstractmethod
|
||||
async def send(self, msg: OutboundMessage) -> None:
|
||||
"""Send a message back to the external platform.
|
||||
|
||||
The implementation should use ``msg.chat_id`` and ``msg.thread_ts``
|
||||
to route the reply to the correct conversation/thread.
|
||||
"""
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
"""Upload a single file attachment to the platform.
|
||||
|
||||
Returns True if the upload succeeded, False otherwise.
|
||||
Default implementation returns False (no file upload support).
|
||||
"""
|
||||
return False
|
||||
|
||||
# -- helpers -----------------------------------------------------------
|
||||
|
||||
def _make_inbound(
|
||||
self,
|
||||
chat_id: str,
|
||||
user_id: str,
|
||||
text: str,
|
||||
*,
|
||||
msg_type: InboundMessageType = InboundMessageType.CHAT,
|
||||
thread_ts: str | None = None,
|
||||
files: list[dict[str, Any]] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
) -> InboundMessage:
|
||||
"""Convenience factory for creating InboundMessage instances."""
|
||||
return InboundMessage(
|
||||
channel_name=self.name,
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_ts,
|
||||
files=files or [],
|
||||
metadata=metadata or {},
|
||||
)
|
||||
|
||||
async def _on_outbound(self, msg: OutboundMessage) -> None:
|
||||
"""Outbound callback registered with the bus.
|
||||
|
||||
Only forwards messages targeted at this channel.
|
||||
Sends the text message first, then uploads any file attachments.
|
||||
File uploads are skipped entirely when the text send fails to avoid
|
||||
partial deliveries (files without accompanying text).
|
||||
"""
|
||||
if msg.channel_name == self.name:
|
||||
try:
|
||||
await self.send(msg)
|
||||
except Exception:
|
||||
logger.exception("Failed to send outbound message on channel %s", self.name)
|
||||
return # Do not attempt file uploads when the text message failed
|
||||
|
||||
for attachment in msg.attachments:
|
||||
try:
|
||||
success = await self.send_file(msg, attachment)
|
||||
if not success:
|
||||
logger.warning("[%s] file upload skipped for %s", self.name, attachment.filename)
|
||||
except Exception:
|
||||
logger.exception("[%s] failed to upload file %s", self.name, attachment.filename)
|
||||
|
||||
async def receive_file(self, msg: InboundMessage, thread_id: str) -> InboundMessage:
|
||||
"""
|
||||
Optionally process and materialize inbound file attachments for this channel.
|
||||
|
||||
By default, this method does nothing and simply returns the original message.
|
||||
Subclasses (e.g. FeishuChannel) may override this to download files (images, documents, etc)
|
||||
referenced in msg.files, save them to the sandbox, and update msg.text to include
|
||||
the sandbox file paths for downstream model consumption.
|
||||
|
||||
Args:
|
||||
msg: The inbound message, possibly containing file metadata in msg.files.
|
||||
thread_id: The resolved DeerFlow thread ID for sandbox path context.
|
||||
|
||||
Returns:
|
||||
The (possibly modified) InboundMessage, with text and/or files updated as needed.
|
||||
"""
|
||||
return msg
|
||||
20
deer-flow/backend/app/channels/commands.py
Normal file
20
deer-flow/backend/app/channels/commands.py
Normal file
@@ -0,0 +1,20 @@
|
||||
"""Shared command definitions used by all channel implementations.
|
||||
|
||||
Keeping the authoritative command set in one place ensures that channel
|
||||
parsers (e.g. Feishu) and the ChannelManager dispatcher stay in sync
|
||||
automatically — adding or removing a command here is the single edit
|
||||
required.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
KNOWN_CHANNEL_COMMANDS: frozenset[str] = frozenset(
|
||||
{
|
||||
"/bootstrap",
|
||||
"/new",
|
||||
"/status",
|
||||
"/models",
|
||||
"/memory",
|
||||
"/help",
|
||||
}
|
||||
)
|
||||
273
deer-flow/backend/app/channels/discord.py
Normal file
273
deer-flow/backend/app/channels/discord.py
Normal file
@@ -0,0 +1,273 @@
|
||||
"""Discord channel integration using discord.py."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_DISCORD_MAX_MESSAGE_LEN = 2000
|
||||
|
||||
|
||||
class DiscordChannel(Channel):
|
||||
"""Discord bot channel.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.discord``):
|
||||
- ``bot_token``: Discord Bot token.
|
||||
- ``allowed_guilds``: (optional) List of allowed Discord guild IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="discord", bus=bus, config=config)
|
||||
self._bot_token = str(config.get("bot_token", "")).strip()
|
||||
self._allowed_guilds: set[int] = set()
|
||||
for guild_id in config.get("allowed_guilds", []):
|
||||
try:
|
||||
self._allowed_guilds.add(int(guild_id))
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
|
||||
self._client = None
|
||||
self._thread: threading.Thread | None = None
|
||||
self._discord_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._discord_module = None
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
import discord
|
||||
except ImportError:
|
||||
logger.error("discord.py is not installed. Install it with: uv add discord.py")
|
||||
return
|
||||
|
||||
if not self._bot_token:
|
||||
logger.error("Discord channel requires bot_token")
|
||||
return
|
||||
|
||||
intents = discord.Intents.default()
|
||||
intents.messages = True
|
||||
intents.guilds = True
|
||||
intents.message_content = True
|
||||
|
||||
client = discord.Client(
|
||||
intents=intents,
|
||||
allowed_mentions=discord.AllowedMentions.none(),
|
||||
)
|
||||
self._client = client
|
||||
self._discord_module = discord
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
|
||||
@client.event
|
||||
async def on_message(message) -> None:
|
||||
await self._on_message(message)
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
self._thread = threading.Thread(target=self._run_client, daemon=True)
|
||||
self._thread.start()
|
||||
logger.info("Discord channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
|
||||
if self._client and self._discord_loop and self._discord_loop.is_running():
|
||||
close_future = asyncio.run_coroutine_threadsafe(self._client.close(), self._discord_loop)
|
||||
try:
|
||||
await asyncio.wait_for(asyncio.wrap_future(close_future), timeout=10)
|
||||
except TimeoutError:
|
||||
logger.warning("[Discord] client close timed out after 10s")
|
||||
except Exception:
|
||||
logger.exception("[Discord] error while closing client")
|
||||
|
||||
if self._thread:
|
||||
self._thread.join(timeout=10)
|
||||
self._thread = None
|
||||
|
||||
self._client = None
|
||||
self._discord_loop = None
|
||||
self._discord_module = None
|
||||
logger.info("Discord channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage) -> None:
|
||||
target = await self._resolve_target(msg)
|
||||
if target is None:
|
||||
logger.error("[Discord] target not found for chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
|
||||
return
|
||||
|
||||
text = msg.text or ""
|
||||
for chunk in self._split_text(text):
|
||||
send_future = asyncio.run_coroutine_threadsafe(target.send(chunk), self._discord_loop)
|
||||
await asyncio.wrap_future(send_future)
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
target = await self._resolve_target(msg)
|
||||
if target is None:
|
||||
logger.error("[Discord] target not found for file upload chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
|
||||
return False
|
||||
|
||||
if self._discord_module is None:
|
||||
return False
|
||||
|
||||
try:
|
||||
fp = open(str(attachment.actual_path), "rb") # noqa: SIM115
|
||||
file = self._discord_module.File(fp, filename=attachment.filename)
|
||||
send_future = asyncio.run_coroutine_threadsafe(target.send(file=file), self._discord_loop)
|
||||
await asyncio.wrap_future(send_future)
|
||||
logger.info("[Discord] file uploaded: %s", attachment.filename)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to upload file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _on_message(self, message) -> None:
|
||||
if not self._running or not self._client:
|
||||
return
|
||||
|
||||
if message.author.bot:
|
||||
return
|
||||
|
||||
if self._client.user and message.author.id == self._client.user.id:
|
||||
return
|
||||
|
||||
guild = message.guild
|
||||
if self._allowed_guilds:
|
||||
if guild is None or guild.id not in self._allowed_guilds:
|
||||
return
|
||||
|
||||
text = (message.content or "").strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
if self._discord_module is None:
|
||||
return
|
||||
|
||||
if isinstance(message.channel, self._discord_module.Thread):
|
||||
chat_id = str(message.channel.parent_id or message.channel.id)
|
||||
thread_id = str(message.channel.id)
|
||||
else:
|
||||
thread = await self._create_thread(message)
|
||||
if thread is None:
|
||||
return
|
||||
chat_id = str(message.channel.id)
|
||||
thread_id = str(thread.id)
|
||||
|
||||
msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=str(message.author.id),
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_id,
|
||||
metadata={
|
||||
"guild_id": str(guild.id) if guild else None,
|
||||
"channel_id": str(message.channel.id),
|
||||
"message_id": str(message.id),
|
||||
},
|
||||
)
|
||||
inbound.topic_id = thread_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
future = asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._main_loop)
|
||||
future.add_done_callback(lambda f: logger.exception("[Discord] publish_inbound failed", exc_info=f.exception()) if f.exception() else None)
|
||||
|
||||
def _run_client(self) -> None:
|
||||
self._discord_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self._discord_loop)
|
||||
try:
|
||||
self._discord_loop.run_until_complete(self._client.start(self._bot_token))
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Discord client error")
|
||||
finally:
|
||||
try:
|
||||
if self._client and not self._client.is_closed():
|
||||
self._discord_loop.run_until_complete(self._client.close())
|
||||
except Exception:
|
||||
logger.exception("Error during Discord shutdown")
|
||||
|
||||
async def _create_thread(self, message):
|
||||
try:
|
||||
thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
|
||||
return await message.create_thread(name=thread_name)
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to create thread for message=%s (threads may be disabled or missing permissions)", message.id)
|
||||
try:
|
||||
await message.channel.send("Could not create a thread for your message. Please check that threads are enabled in this channel.")
|
||||
except Exception:
|
||||
pass
|
||||
return None
|
||||
|
||||
async def _resolve_target(self, msg: OutboundMessage):
|
||||
if not self._client or not self._discord_loop:
|
||||
return None
|
||||
|
||||
target_ids: list[str] = []
|
||||
if msg.thread_ts:
|
||||
target_ids.append(msg.thread_ts)
|
||||
if msg.chat_id and msg.chat_id not in target_ids:
|
||||
target_ids.append(msg.chat_id)
|
||||
|
||||
for raw_id in target_ids:
|
||||
target = await self._get_channel_or_thread(raw_id)
|
||||
if target is not None:
|
||||
return target
|
||||
return None
|
||||
|
||||
async def _get_channel_or_thread(self, raw_id: str):
|
||||
if not self._client or not self._discord_loop:
|
||||
return None
|
||||
|
||||
try:
|
||||
target_id = int(raw_id)
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
|
||||
get_future = asyncio.run_coroutine_threadsafe(self._fetch_channel(target_id), self._discord_loop)
|
||||
try:
|
||||
return await asyncio.wrap_future(get_future)
|
||||
except Exception:
|
||||
logger.exception("[Discord] failed to resolve target id=%s", raw_id)
|
||||
return None
|
||||
|
||||
async def _fetch_channel(self, target_id: int):
|
||||
if not self._client:
|
||||
return None
|
||||
|
||||
channel = self._client.get_channel(target_id)
|
||||
if channel is not None:
|
||||
return channel
|
||||
|
||||
try:
|
||||
return await self._client.fetch_channel(target_id)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _split_text(text: str) -> list[str]:
|
||||
if not text:
|
||||
return [""]
|
||||
|
||||
chunks: list[str] = []
|
||||
remaining = text
|
||||
while len(remaining) > _DISCORD_MAX_MESSAGE_LEN:
|
||||
split_at = remaining.rfind("\n", 0, _DISCORD_MAX_MESSAGE_LEN)
|
||||
if split_at <= 0:
|
||||
split_at = _DISCORD_MAX_MESSAGE_LEN
|
||||
chunks.append(remaining[:split_at])
|
||||
remaining = remaining[split_at:].lstrip("\n")
|
||||
|
||||
if remaining:
|
||||
chunks.append(remaining)
|
||||
|
||||
return chunks
|
||||
692
deer-flow/backend/app/channels/feishu.py
Normal file
692
deer-flow/backend/app/channels/feishu.py
Normal file
@@ -0,0 +1,692 @@
|
||||
"""Feishu/Lark channel — connects to Feishu via WebSocket (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import threading
|
||||
from typing import Any, Literal
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
|
||||
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _is_feishu_command(text: str) -> bool:
|
||||
if not text.startswith("/"):
|
||||
return False
|
||||
return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
|
||||
|
||||
|
||||
class FeishuChannel(Channel):
|
||||
"""Feishu/Lark IM channel using the ``lark-oapi`` WebSocket client.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.feishu``):
|
||||
- ``app_id``: Feishu app ID.
|
||||
- ``app_secret``: Feishu app secret.
|
||||
- ``verification_token``: (optional) Event verification token.
|
||||
|
||||
The channel uses WebSocket long-connection mode so no public IP is required.
|
||||
|
||||
Message flow:
|
||||
1. User sends a message → bot adds "OK" emoji reaction
|
||||
2. Bot replies in thread: "Working on it......"
|
||||
3. Agent processes the message and returns a result
|
||||
4. Bot replies in thread with the result
|
||||
5. Bot adds "DONE" emoji reaction to the original message
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="feishu", bus=bus, config=config)
|
||||
self._thread: threading.Thread | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._api_client = None
|
||||
self._CreateMessageReactionRequest = None
|
||||
self._CreateMessageReactionRequestBody = None
|
||||
self._Emoji = None
|
||||
self._PatchMessageRequest = None
|
||||
self._PatchMessageRequestBody = None
|
||||
self._background_tasks: set[asyncio.Task] = set()
|
||||
self._running_card_ids: dict[str, str] = {}
|
||||
self._running_card_tasks: dict[str, asyncio.Task] = {}
|
||||
self._CreateFileRequest = None
|
||||
self._CreateFileRequestBody = None
|
||||
self._CreateImageRequest = None
|
||||
self._CreateImageRequestBody = None
|
||||
self._GetMessageResourceRequest = None
|
||||
self._thread_lock = threading.Lock()
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
import lark_oapi as lark
|
||||
from lark_oapi.api.im.v1 import (
|
||||
CreateFileRequest,
|
||||
CreateFileRequestBody,
|
||||
CreateImageRequest,
|
||||
CreateImageRequestBody,
|
||||
CreateMessageReactionRequest,
|
||||
CreateMessageReactionRequestBody,
|
||||
CreateMessageRequest,
|
||||
CreateMessageRequestBody,
|
||||
Emoji,
|
||||
GetMessageResourceRequest,
|
||||
PatchMessageRequest,
|
||||
PatchMessageRequestBody,
|
||||
ReplyMessageRequest,
|
||||
ReplyMessageRequestBody,
|
||||
)
|
||||
except ImportError:
|
||||
logger.error("lark-oapi is not installed. Install it with: uv add lark-oapi")
|
||||
return
|
||||
|
||||
self._lark = lark
|
||||
self._CreateMessageRequest = CreateMessageRequest
|
||||
self._CreateMessageRequestBody = CreateMessageRequestBody
|
||||
self._ReplyMessageRequest = ReplyMessageRequest
|
||||
self._ReplyMessageRequestBody = ReplyMessageRequestBody
|
||||
self._CreateMessageReactionRequest = CreateMessageReactionRequest
|
||||
self._CreateMessageReactionRequestBody = CreateMessageReactionRequestBody
|
||||
self._Emoji = Emoji
|
||||
self._PatchMessageRequest = PatchMessageRequest
|
||||
self._PatchMessageRequestBody = PatchMessageRequestBody
|
||||
self._CreateFileRequest = CreateFileRequest
|
||||
self._CreateFileRequestBody = CreateFileRequestBody
|
||||
self._CreateImageRequest = CreateImageRequest
|
||||
self._CreateImageRequestBody = CreateImageRequestBody
|
||||
self._GetMessageResourceRequest = GetMessageResourceRequest
|
||||
|
||||
app_id = self.config.get("app_id", "")
|
||||
app_secret = self.config.get("app_secret", "")
|
||||
domain = self.config.get("domain", "https://open.feishu.cn")
|
||||
|
||||
if not app_id or not app_secret:
|
||||
logger.error("Feishu channel requires app_id and app_secret")
|
||||
return
|
||||
|
||||
self._api_client = lark.Client.builder().app_id(app_id).app_secret(app_secret).domain(domain).build()
|
||||
logger.info("[Feishu] using domain: %s", domain)
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Both ws.Client construction and start() must happen in a dedicated
|
||||
# thread with its own event loop. lark-oapi caches the running loop
|
||||
# at construction time and later calls loop.run_until_complete(),
|
||||
# which conflicts with an already-running uvloop.
|
||||
self._thread = threading.Thread(
|
||||
target=self._run_ws,
|
||||
args=(app_id, app_secret, domain),
|
||||
daemon=True,
|
||||
)
|
||||
self._thread.start()
|
||||
logger.info("Feishu channel started")
|
||||
|
||||
def _run_ws(self, app_id: str, app_secret: str, domain: str) -> None:
|
||||
"""Construct and run the lark WS client in a thread with a fresh event loop.
|
||||
|
||||
The lark-oapi SDK captures a module-level event loop at import time
|
||||
(``lark_oapi.ws.client.loop``). When uvicorn uses uvloop, that
|
||||
captured loop is the *main* thread's uvloop — which is already
|
||||
running, so ``loop.run_until_complete()`` inside ``Client.start()``
|
||||
raises ``RuntimeError``.
|
||||
|
||||
We work around this by creating a plain asyncio event loop for this
|
||||
thread and patching the SDK's module-level reference before calling
|
||||
``start()``.
|
||||
"""
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
import lark_oapi as lark
|
||||
import lark_oapi.ws.client as _ws_client_mod
|
||||
|
||||
# Replace the SDK's module-level loop so Client.start() uses
|
||||
# this thread's (non-running) event loop instead of the main
|
||||
# thread's uvloop.
|
||||
_ws_client_mod.loop = loop
|
||||
|
||||
event_handler = lark.EventDispatcherHandler.builder("", "").register_p2_im_message_receive_v1(self._on_message).build()
|
||||
ws_client = lark.ws.Client(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
event_handler=event_handler,
|
||||
log_level=lark.LogLevel.INFO,
|
||||
domain=domain,
|
||||
)
|
||||
ws_client.start()
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Feishu WebSocket error")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
for task in list(self._background_tasks):
|
||||
task.cancel()
|
||||
self._background_tasks.clear()
|
||||
for task in list(self._running_card_tasks.values()):
|
||||
task.cancel()
|
||||
self._running_card_tasks.clear()
|
||||
if self._thread:
|
||||
self._thread.join(timeout=5)
|
||||
self._thread = None
|
||||
logger.info("Feishu channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._api_client:
|
||||
logger.warning("[Feishu] send called but no api_client available")
|
||||
return
|
||||
|
||||
logger.info(
|
||||
"[Feishu] sending reply: chat_id=%s, thread_ts=%s, text_len=%d",
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
len(msg.text),
|
||||
)
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._send_card_message(msg)
|
||||
return # success
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Feishu] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Feishu] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Feishu send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._api_client:
|
||||
return False
|
||||
|
||||
# Check size limits (image: 10MB, file: 30MB)
|
||||
if attachment.is_image and attachment.size > 10 * 1024 * 1024:
|
||||
logger.warning("[Feishu] image too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
if not attachment.is_image and attachment.size > 30 * 1024 * 1024:
|
||||
logger.warning("[Feishu] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
|
||||
try:
|
||||
if attachment.is_image:
|
||||
file_key = await self._upload_image(attachment.actual_path)
|
||||
msg_type = "image"
|
||||
content = json.dumps({"image_key": file_key})
|
||||
else:
|
||||
file_key = await self._upload_file(attachment.actual_path, attachment.filename)
|
||||
msg_type = "file"
|
||||
content = json.dumps({"file_key": file_key})
|
||||
|
||||
if msg.thread_ts:
|
||||
request = self._ReplyMessageRequest.builder().message_id(msg.thread_ts).request_body(self._ReplyMessageRequestBody.builder().msg_type(msg_type).content(content).reply_in_thread(True).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.reply, request)
|
||||
else:
|
||||
request = self._CreateMessageRequest.builder().receive_id_type("chat_id").request_body(self._CreateMessageRequestBody.builder().receive_id(msg.chat_id).msg_type(msg_type).content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.create, request)
|
||||
|
||||
logger.info("[Feishu] file sent: %s (type=%s)", attachment.filename, msg_type)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to upload/send file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _upload_image(self, path) -> str:
|
||||
"""Upload an image to Feishu and return the image_key."""
|
||||
with open(str(path), "rb") as f:
|
||||
request = self._CreateImageRequest.builder().request_body(self._CreateImageRequestBody.builder().image_type("message").image(f).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.image.create, request)
|
||||
if not response.success():
|
||||
raise RuntimeError(f"Feishu image upload failed: code={response.code}, msg={response.msg}")
|
||||
return response.data.image_key
|
||||
|
||||
async def _upload_file(self, path, filename: str) -> str:
|
||||
"""Upload a file to Feishu and return the file_key."""
|
||||
suffix = path.suffix.lower() if hasattr(path, "suffix") else ""
|
||||
if suffix in (".xls", ".xlsx", ".csv"):
|
||||
file_type = "xls"
|
||||
elif suffix in (".ppt", ".pptx"):
|
||||
file_type = "ppt"
|
||||
elif suffix == ".pdf":
|
||||
file_type = "pdf"
|
||||
elif suffix in (".doc", ".docx"):
|
||||
file_type = "doc"
|
||||
else:
|
||||
file_type = "stream"
|
||||
|
||||
with open(str(path), "rb") as f:
|
||||
request = self._CreateFileRequest.builder().request_body(self._CreateFileRequestBody.builder().file_type(file_type).file_name(filename).file(f).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.file.create, request)
|
||||
if not response.success():
|
||||
raise RuntimeError(f"Feishu file upload failed: code={response.code}, msg={response.msg}")
|
||||
return response.data.file_key
|
||||
|
||||
async def receive_file(self, msg: InboundMessage, thread_id: str) -> InboundMessage:
|
||||
"""Download a Feishu file into the thread uploads directory.
|
||||
|
||||
Returns the sandbox virtual path when the image is persisted successfully.
|
||||
"""
|
||||
if not msg.thread_ts:
|
||||
logger.warning("[Feishu] received file message without thread_ts, cannot associate with conversation: %s", msg)
|
||||
return msg
|
||||
files = msg.files
|
||||
if not files:
|
||||
logger.warning("[Feishu] received message with no files: %s", msg)
|
||||
return msg
|
||||
text = msg.text
|
||||
for file in files:
|
||||
if file.get("image_key"):
|
||||
virtual_path = await self._receive_single_file(msg.thread_ts, file["image_key"], "image", thread_id)
|
||||
text = text.replace("[image]", virtual_path, 1)
|
||||
elif file.get("file_key"):
|
||||
virtual_path = await self._receive_single_file(msg.thread_ts, file["file_key"], "file", thread_id)
|
||||
text = text.replace("[file]", virtual_path, 1)
|
||||
msg.text = text
|
||||
return msg
|
||||
|
||||
async def _receive_single_file(self, message_id: str, file_key: str, type: Literal["image", "file"], thread_id: str) -> str:
|
||||
request = self._GetMessageResourceRequest.builder().message_id(message_id).file_key(file_key).type(type).build()
|
||||
|
||||
def inner():
|
||||
return self._api_client.im.v1.message_resource.get(request)
|
||||
|
||||
try:
|
||||
response = await asyncio.to_thread(inner)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] resource get request failed for resource_key=%s type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
if not response.success():
|
||||
logger.warning(
|
||||
"[Feishu] resource get failed: resource_key=%s, type=%s, code=%s, msg=%s, log_id=%s ",
|
||||
file_key,
|
||||
type,
|
||||
response.code,
|
||||
response.msg,
|
||||
response.get_log_id(),
|
||||
)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
image_stream = getattr(response, "file", None)
|
||||
if image_stream is None:
|
||||
logger.warning("[Feishu] resource get returned no file stream: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
try:
|
||||
content: bytes = await asyncio.to_thread(image_stream.read)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to read resource stream: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
if not content:
|
||||
logger.warning("[Feishu] empty resource content: resource_key=%s, type=%s", file_key, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
paths = get_paths()
|
||||
paths.ensure_thread_dirs(thread_id)
|
||||
uploads_dir = paths.sandbox_uploads_dir(thread_id).resolve()
|
||||
|
||||
ext = "png" if type == "image" else "bin"
|
||||
raw_filename = getattr(response, "file_name", "") or f"feishu_{file_key[-12:]}.{ext}"
|
||||
|
||||
# Sanitize filename: preserve extension, replace path chars in name part
|
||||
if "." in raw_filename:
|
||||
name_part, ext = raw_filename.rsplit(".", 1)
|
||||
name_part = re.sub(r"[./\\]", "_", name_part)
|
||||
filename = f"{name_part}.{ext}"
|
||||
else:
|
||||
filename = re.sub(r"[./\\]", "_", raw_filename)
|
||||
resolved_target = uploads_dir / filename
|
||||
|
||||
def down_load():
|
||||
# use thread_lock to avoid filename conflicts when writing
|
||||
with self._thread_lock:
|
||||
resolved_target.write_bytes(content)
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(down_load)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to persist downloaded resource: %s, type=%s", resolved_target, type)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"
|
||||
|
||||
try:
|
||||
sandbox_provider = get_sandbox_provider()
|
||||
sandbox_id = sandbox_provider.acquire(thread_id)
|
||||
if sandbox_id != "local":
|
||||
sandbox = sandbox_provider.get(sandbox_id)
|
||||
if sandbox is None:
|
||||
logger.warning("[Feishu] sandbox not found for thread_id=%s", thread_id)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
sandbox.update_file(virtual_path, content)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to sync resource into non-local sandbox: %s", virtual_path)
|
||||
return f"Failed to obtain the [{type}]"
|
||||
|
||||
logger.info("[Feishu] downloaded resource mapped: file_key=%s -> %s", file_key, virtual_path)
|
||||
return virtual_path
|
||||
|
||||
# -- message formatting ------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _build_card_content(text: str) -> str:
|
||||
"""Build a Feishu interactive card with markdown content.
|
||||
|
||||
Feishu's interactive card format natively renders markdown, including
|
||||
headers, bold/italic, code blocks, lists, and links.
|
||||
"""
|
||||
card = {
|
||||
"config": {"wide_screen_mode": True, "update_multi": True},
|
||||
"elements": [{"tag": "markdown", "content": text}],
|
||||
}
|
||||
return json.dumps(card)
|
||||
|
||||
# -- reaction helpers --------------------------------------------------
|
||||
|
||||
async def _add_reaction(self, message_id: str, emoji_type: str = "THUMBSUP") -> None:
|
||||
"""Add an emoji reaction to a message."""
|
||||
if not self._api_client or not self._CreateMessageReactionRequest:
|
||||
return
|
||||
try:
|
||||
request = self._CreateMessageReactionRequest.builder().message_id(message_id).request_body(self._CreateMessageReactionRequestBody.builder().reaction_type(self._Emoji.builder().emoji_type(emoji_type).build()).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message_reaction.create, request)
|
||||
logger.info("[Feishu] reaction '%s' added to message %s", emoji_type, message_id)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to add reaction '%s' to message %s", emoji_type, message_id)
|
||||
|
||||
async def _reply_card(self, message_id: str, text: str) -> str | None:
|
||||
"""Reply with an interactive card and return the created card message ID."""
|
||||
if not self._api_client:
|
||||
return None
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._ReplyMessageRequest.builder().message_id(message_id).request_body(self._ReplyMessageRequestBody.builder().msg_type("interactive").content(content).reply_in_thread(True).build()).build()
|
||||
response = await asyncio.to_thread(self._api_client.im.v1.message.reply, request)
|
||||
response_data = getattr(response, "data", None)
|
||||
return getattr(response_data, "message_id", None)
|
||||
|
||||
async def _create_card(self, chat_id: str, text: str) -> None:
|
||||
"""Create a new card message in the target chat."""
|
||||
if not self._api_client:
|
||||
return
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._CreateMessageRequest.builder().receive_id_type("chat_id").request_body(self._CreateMessageRequestBody.builder().receive_id(chat_id).msg_type("interactive").content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.create, request)
|
||||
|
||||
async def _update_card(self, message_id: str, text: str) -> None:
|
||||
"""Patch an existing card message in place."""
|
||||
if not self._api_client or not self._PatchMessageRequest:
|
||||
return
|
||||
|
||||
content = self._build_card_content(text)
|
||||
request = self._PatchMessageRequest.builder().message_id(message_id).request_body(self._PatchMessageRequestBody.builder().content(content).build()).build()
|
||||
await asyncio.to_thread(self._api_client.im.v1.message.patch, request)
|
||||
|
||||
def _track_background_task(self, task: asyncio.Task, *, name: str, msg_id: str) -> None:
|
||||
"""Keep a strong reference to fire-and-forget tasks and surface errors."""
|
||||
self._background_tasks.add(task)
|
||||
task.add_done_callback(lambda done_task, task_name=name, mid=msg_id: self._finalize_background_task(done_task, task_name, mid))
|
||||
|
||||
def _finalize_background_task(self, task: asyncio.Task, name: str, msg_id: str) -> None:
|
||||
self._background_tasks.discard(task)
|
||||
self._log_task_error(task, name, msg_id)
|
||||
|
||||
async def _create_running_card(self, source_message_id: str, text: str) -> str | None:
|
||||
"""Create the running card and cache its message ID when available."""
|
||||
running_card_id = await self._reply_card(source_message_id, text)
|
||||
if running_card_id:
|
||||
self._running_card_ids[source_message_id] = running_card_id
|
||||
logger.info("[Feishu] running card created: source=%s card=%s", source_message_id, running_card_id)
|
||||
else:
|
||||
logger.warning("[Feishu] running card creation returned no message_id for source=%s, subsequent updates will fall back to new replies", source_message_id)
|
||||
return running_card_id
|
||||
|
||||
def _ensure_running_card_started(self, source_message_id: str, text: str = "Working on it...") -> asyncio.Task | None:
|
||||
"""Start running-card creation once per source message."""
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
if running_card_id:
|
||||
return None
|
||||
|
||||
running_card_task = self._running_card_tasks.get(source_message_id)
|
||||
if running_card_task:
|
||||
return running_card_task
|
||||
|
||||
running_card_task = asyncio.create_task(self._create_running_card(source_message_id, text))
|
||||
self._running_card_tasks[source_message_id] = running_card_task
|
||||
running_card_task.add_done_callback(lambda done_task, mid=source_message_id: self._finalize_running_card_task(mid, done_task))
|
||||
return running_card_task
|
||||
|
||||
def _finalize_running_card_task(self, source_message_id: str, task: asyncio.Task) -> None:
|
||||
if self._running_card_tasks.get(source_message_id) is task:
|
||||
self._running_card_tasks.pop(source_message_id, None)
|
||||
self._log_task_error(task, "create_running_card", source_message_id)
|
||||
|
||||
async def _ensure_running_card(self, source_message_id: str, text: str = "Working on it...") -> str | None:
|
||||
"""Ensure the in-thread running card exists and track its message ID."""
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
if running_card_id:
|
||||
return running_card_id
|
||||
|
||||
running_card_task = self._ensure_running_card_started(source_message_id, text)
|
||||
if running_card_task is None:
|
||||
return self._running_card_ids.get(source_message_id)
|
||||
return await running_card_task
|
||||
|
||||
async def _send_running_reply(self, message_id: str) -> None:
|
||||
"""Reply to a message in-thread with a running card."""
|
||||
try:
|
||||
await self._ensure_running_card(message_id)
|
||||
except Exception:
|
||||
logger.exception("[Feishu] failed to send running reply for message %s", message_id)
|
||||
|
||||
async def _send_card_message(self, msg: OutboundMessage) -> None:
|
||||
"""Send or update the Feishu card tied to the current request."""
|
||||
source_message_id = msg.thread_ts
|
||||
if source_message_id:
|
||||
running_card_id = self._running_card_ids.get(source_message_id)
|
||||
awaited_running_card_task = False
|
||||
|
||||
if not running_card_id:
|
||||
running_card_task = self._running_card_tasks.get(source_message_id)
|
||||
if running_card_task:
|
||||
awaited_running_card_task = True
|
||||
running_card_id = await running_card_task
|
||||
|
||||
if running_card_id:
|
||||
try:
|
||||
await self._update_card(running_card_id, msg.text)
|
||||
except Exception:
|
||||
if not msg.is_final:
|
||||
raise
|
||||
logger.exception(
|
||||
"[Feishu] failed to patch running card %s, falling back to final reply",
|
||||
running_card_id,
|
||||
)
|
||||
await self._reply_card(source_message_id, msg.text)
|
||||
else:
|
||||
logger.info("[Feishu] running card updated: source=%s card=%s", source_message_id, running_card_id)
|
||||
elif msg.is_final:
|
||||
await self._reply_card(source_message_id, msg.text)
|
||||
elif awaited_running_card_task:
|
||||
logger.warning(
|
||||
"[Feishu] running card task finished without message_id for source=%s, skipping duplicate non-final creation",
|
||||
source_message_id,
|
||||
)
|
||||
else:
|
||||
await self._ensure_running_card(source_message_id, msg.text)
|
||||
|
||||
if msg.is_final:
|
||||
self._running_card_ids.pop(source_message_id, None)
|
||||
await self._add_reaction(source_message_id, "DONE")
|
||||
return
|
||||
|
||||
await self._create_card(msg.chat_id, msg.text)
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _log_future_error(fut, name: str, msg_id: str) -> None:
|
||||
"""Callback for run_coroutine_threadsafe futures to surface errors."""
|
||||
try:
|
||||
exc = fut.exception()
|
||||
if exc:
|
||||
logger.error("[Feishu] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def _log_task_error(task: asyncio.Task, name: str, msg_id: str) -> None:
|
||||
"""Callback for background asyncio tasks to surface errors."""
|
||||
try:
|
||||
exc = task.exception()
|
||||
if exc:
|
||||
logger.error("[Feishu] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except asyncio.CancelledError:
|
||||
logger.info("[Feishu] %s cancelled for msg_id=%s", name, msg_id)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
async def _prepare_inbound(self, msg_id: str, inbound) -> None:
|
||||
"""Kick off Feishu side effects without delaying inbound dispatch."""
|
||||
reaction_task = asyncio.create_task(self._add_reaction(msg_id, "OK"))
|
||||
self._track_background_task(reaction_task, name="add_reaction", msg_id=msg_id)
|
||||
self._ensure_running_card_started(msg_id)
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
def _on_message(self, event) -> None:
|
||||
"""Called by lark-oapi when a message is received (runs in lark thread)."""
|
||||
try:
|
||||
logger.info("[Feishu] raw event received: type=%s", type(event).__name__)
|
||||
message = event.event.message
|
||||
chat_id = message.chat_id
|
||||
msg_id = message.message_id
|
||||
sender_id = event.event.sender.sender_id.open_id
|
||||
|
||||
# root_id is set when the message is a reply within a Feishu thread.
|
||||
# Use it as topic_id so all replies share the same DeerFlow thread.
|
||||
root_id = getattr(message, "root_id", None) or None
|
||||
|
||||
# Parse message content
|
||||
content = json.loads(message.content)
|
||||
|
||||
# files_list store the any-file-key in feishu messages, which can be used to download the file content later
|
||||
# In Feishu channel, image_keys are independent of file_keys.
|
||||
# The file_key includes files, videos, and audio, but does not include stickers.
|
||||
files_list = []
|
||||
|
||||
if "text" in content:
|
||||
# Handle plain text messages
|
||||
text = content["text"]
|
||||
elif "file_key" in content:
|
||||
file_key = content.get("file_key")
|
||||
if isinstance(file_key, str) and file_key:
|
||||
files_list.append({"file_key": file_key})
|
||||
text = "[file]"
|
||||
else:
|
||||
text = ""
|
||||
elif "image_key" in content:
|
||||
image_key = content.get("image_key")
|
||||
if isinstance(image_key, str) and image_key:
|
||||
files_list.append({"image_key": image_key})
|
||||
text = "[image]"
|
||||
else:
|
||||
text = ""
|
||||
elif "content" in content and isinstance(content["content"], list):
|
||||
# Handle rich-text messages with a top-level "content" list (e.g., topic groups/posts)
|
||||
text_paragraphs: list[str] = []
|
||||
for paragraph in content["content"]:
|
||||
if isinstance(paragraph, list):
|
||||
paragraph_text_parts: list[str] = []
|
||||
for element in paragraph:
|
||||
if isinstance(element, dict):
|
||||
# Include both normal text and @ mentions
|
||||
if element.get("tag") in ("text", "at"):
|
||||
text_value = element.get("text", "")
|
||||
if text_value:
|
||||
paragraph_text_parts.append(text_value)
|
||||
elif element.get("tag") == "img":
|
||||
image_key = element.get("image_key")
|
||||
if isinstance(image_key, str) and image_key:
|
||||
files_list.append({"image_key": image_key})
|
||||
paragraph_text_parts.append("[image]")
|
||||
elif element.get("tag") in ("file", "media"):
|
||||
file_key = element.get("file_key")
|
||||
if isinstance(file_key, str) and file_key:
|
||||
files_list.append({"file_key": file_key})
|
||||
paragraph_text_parts.append("[file]")
|
||||
if paragraph_text_parts:
|
||||
# Join text segments within a paragraph with spaces to avoid "helloworld"
|
||||
text_paragraphs.append(" ".join(paragraph_text_parts))
|
||||
|
||||
# Join paragraphs with blank lines to preserve paragraph boundaries
|
||||
text = "\n\n".join(text_paragraphs)
|
||||
else:
|
||||
text = ""
|
||||
text = text.strip()
|
||||
|
||||
logger.info(
|
||||
"[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, sender=%s, text=%r",
|
||||
chat_id,
|
||||
msg_id,
|
||||
root_id,
|
||||
sender_id,
|
||||
text[:100] if text else "",
|
||||
)
|
||||
|
||||
if not (text or files_list):
|
||||
logger.info("[Feishu] empty text, ignoring message")
|
||||
return
|
||||
|
||||
# Only treat known slash commands as commands; absolute paths and
|
||||
# other slash-prefixed text should be handled as normal chat.
|
||||
if _is_feishu_command(text):
|
||||
msg_type = InboundMessageType.COMMAND
|
||||
else:
|
||||
msg_type = InboundMessageType.CHAT
|
||||
|
||||
# topic_id: use root_id for replies (same topic), msg_id for new messages (new topic)
|
||||
topic_id = root_id or msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=sender_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=msg_id,
|
||||
files=files_list,
|
||||
metadata={"message_id": msg_id, "root_id": root_id},
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
# Schedule on the async event loop
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
logger.info("[Feishu] publishing inbound message to bus (type=%s, msg_id=%s)", msg_type.value, msg_id)
|
||||
fut = asyncio.run_coroutine_threadsafe(self._prepare_inbound(msg_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f, mid=msg_id: self._log_future_error(f, "prepare_inbound", mid))
|
||||
else:
|
||||
logger.warning("[Feishu] main loop not running, cannot publish inbound message")
|
||||
except Exception:
|
||||
logger.exception("[Feishu] error processing message")
|
||||
960
deer-flow/backend/app/channels/manager.py
Normal file
960
deer-flow/backend/app/channels/manager.py
Normal file
@@ -0,0 +1,960 @@
|
||||
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import mimetypes
|
||||
import re
|
||||
import time
|
||||
from collections.abc import Awaitable, Callable, Mapping
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
from langgraph_sdk.errors import ConflictError
|
||||
|
||||
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
from app.channels.store import ChannelStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_LANGGRAPH_URL = "http://localhost:2024"
|
||||
DEFAULT_GATEWAY_URL = "http://localhost:8001"
|
||||
DEFAULT_ASSISTANT_ID = "lead_agent"
|
||||
CUSTOM_AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
|
||||
|
||||
DEFAULT_RUN_CONFIG: dict[str, Any] = {"recursion_limit": 100}
|
||||
DEFAULT_RUN_CONTEXT: dict[str, Any] = {
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": False,
|
||||
"subagent_enabled": False,
|
||||
}
|
||||
STREAM_UPDATE_MIN_INTERVAL_SECONDS = 0.35
|
||||
THREAD_BUSY_MESSAGE = "This conversation is already processing another request. Please wait for it to finish and try again."
|
||||
|
||||
CHANNEL_CAPABILITIES = {
|
||||
"discord": {"supports_streaming": False},
|
||||
"feishu": {"supports_streaming": True},
|
||||
"slack": {"supports_streaming": False},
|
||||
"telegram": {"supports_streaming": False},
|
||||
"wechat": {"supports_streaming": False},
|
||||
"wecom": {"supports_streaming": True},
|
||||
}
|
||||
|
||||
InboundFileReader = Callable[[dict[str, Any], httpx.AsyncClient], Awaitable[bytes | None]]
|
||||
|
||||
|
||||
INBOUND_FILE_READERS: dict[str, InboundFileReader] = {}
|
||||
|
||||
|
||||
def register_inbound_file_reader(channel_name: str, reader: InboundFileReader) -> None:
|
||||
INBOUND_FILE_READERS[channel_name] = reader
|
||||
|
||||
|
||||
async def _read_http_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
url = file_info.get("url")
|
||||
if not isinstance(url, str) or not url:
|
||||
return None
|
||||
|
||||
resp = await client.get(url)
|
||||
resp.raise_for_status()
|
||||
return resp.content
|
||||
|
||||
|
||||
async def _read_wecom_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
data = await _read_http_inbound_file(file_info, client)
|
||||
if data is None:
|
||||
return None
|
||||
|
||||
aeskey = file_info.get("aeskey") if isinstance(file_info.get("aeskey"), str) else None
|
||||
if not aeskey:
|
||||
return data
|
||||
|
||||
try:
|
||||
from aibot.crypto_utils import decrypt_file
|
||||
except Exception:
|
||||
logger.exception("[Manager] failed to import WeCom decrypt_file")
|
||||
return None
|
||||
|
||||
return decrypt_file(data, aeskey)
|
||||
|
||||
|
||||
async def _read_wechat_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
|
||||
raw_path = file_info.get("path")
|
||||
if isinstance(raw_path, str) and raw_path.strip():
|
||||
try:
|
||||
return await asyncio.to_thread(Path(raw_path).read_bytes)
|
||||
except OSError:
|
||||
logger.exception("[Manager] failed to read WeChat inbound file from local path: %s", raw_path)
|
||||
return None
|
||||
|
||||
full_url = file_info.get("full_url")
|
||||
if isinstance(full_url, str) and full_url.strip():
|
||||
return await _read_http_inbound_file({"url": full_url}, client)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
register_inbound_file_reader("wecom", _read_wecom_inbound_file)
|
||||
register_inbound_file_reader("wechat", _read_wechat_inbound_file)
|
||||
|
||||
|
||||
class InvalidChannelSessionConfigError(ValueError):
|
||||
"""Raised when IM channel session overrides contain invalid agent config."""
|
||||
|
||||
|
||||
def _is_thread_busy_error(exc: BaseException | None) -> bool:
|
||||
if exc is None:
|
||||
return False
|
||||
if isinstance(exc, ConflictError):
|
||||
return True
|
||||
return "already running a task" in str(exc)
|
||||
|
||||
|
||||
def _as_dict(value: Any) -> dict[str, Any]:
|
||||
return dict(value) if isinstance(value, Mapping) else {}
|
||||
|
||||
|
||||
def _merge_dicts(*layers: Any) -> dict[str, Any]:
|
||||
merged: dict[str, Any] = {}
|
||||
for layer in layers:
|
||||
if isinstance(layer, Mapping):
|
||||
merged.update(layer)
|
||||
return merged
|
||||
|
||||
|
||||
def _normalize_custom_agent_name(raw_value: str) -> str:
|
||||
"""Normalize legacy channel assistant IDs into valid custom agent names."""
|
||||
normalized = raw_value.strip().lower().replace("_", "-")
|
||||
if not normalized:
|
||||
raise InvalidChannelSessionConfigError("Channel session assistant_id is empty. Use 'lead_agent' or a valid custom agent name.")
|
||||
if not CUSTOM_AGENT_NAME_PATTERN.fullmatch(normalized):
|
||||
raise InvalidChannelSessionConfigError(f"Invalid channel session assistant_id {raw_value!r}. Use 'lead_agent' or a custom agent name containing only letters, digits, and hyphens.")
|
||||
return normalized
|
||||
|
||||
|
||||
def _extract_response_text(result: dict | list) -> str:
|
||||
"""Extract the last AI message text from a LangGraph runs.wait result.
|
||||
|
||||
``runs.wait`` returns the final state dict which contains a ``messages``
|
||||
list. Each message is a dict with at least ``type`` and ``content``.
|
||||
|
||||
Handles special cases:
|
||||
- Regular AI text responses
|
||||
- Clarification interrupts (``ask_clarification`` tool messages)
|
||||
- AI messages with tool_calls but no text content
|
||||
"""
|
||||
if isinstance(result, list):
|
||||
messages = result
|
||||
elif isinstance(result, dict):
|
||||
messages = result.get("messages", [])
|
||||
else:
|
||||
return ""
|
||||
|
||||
# Walk backwards to find usable response text, but stop at the last
|
||||
# human message to avoid returning text from a previous turn.
|
||||
for msg in reversed(messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
|
||||
msg_type = msg.get("type")
|
||||
|
||||
# Stop at the last human message — anything before it is a previous turn
|
||||
if msg_type == "human":
|
||||
break
|
||||
|
||||
# Check for tool messages from ask_clarification (interrupt case)
|
||||
if msg_type == "tool" and msg.get("name") == "ask_clarification":
|
||||
content = msg.get("content", "")
|
||||
if isinstance(content, str) and content:
|
||||
return content
|
||||
|
||||
# Regular AI message with text content
|
||||
if msg_type == "ai":
|
||||
content = msg.get("content", "")
|
||||
if isinstance(content, str) and content:
|
||||
return content
|
||||
# content can be a list of content blocks
|
||||
if isinstance(content, list):
|
||||
parts = []
|
||||
for block in content:
|
||||
if isinstance(block, dict) and block.get("type") == "text":
|
||||
parts.append(block.get("text", ""))
|
||||
elif isinstance(block, str):
|
||||
parts.append(block)
|
||||
text = "".join(parts)
|
||||
if text:
|
||||
return text
|
||||
return ""
|
||||
|
||||
|
||||
def _extract_text_content(content: Any) -> str:
|
||||
"""Extract text from a streaming payload content field."""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts: list[str] = []
|
||||
for block in content:
|
||||
if isinstance(block, str):
|
||||
parts.append(block)
|
||||
elif isinstance(block, Mapping):
|
||||
text = block.get("text")
|
||||
if isinstance(text, str):
|
||||
parts.append(text)
|
||||
else:
|
||||
nested = block.get("content")
|
||||
if isinstance(nested, str):
|
||||
parts.append(nested)
|
||||
return "".join(parts)
|
||||
if isinstance(content, Mapping):
|
||||
for key in ("text", "content"):
|
||||
value = content.get(key)
|
||||
if isinstance(value, str):
|
||||
return value
|
||||
return ""
|
||||
|
||||
|
||||
def _merge_stream_text(existing: str, chunk: str) -> str:
|
||||
"""Merge either delta text or cumulative text into a single snapshot."""
|
||||
if not chunk:
|
||||
return existing
|
||||
if not existing or chunk == existing:
|
||||
return chunk or existing
|
||||
if chunk.startswith(existing):
|
||||
return chunk
|
||||
if existing.endswith(chunk):
|
||||
return existing
|
||||
return existing + chunk
|
||||
|
||||
|
||||
def _extract_stream_message_id(payload: Any, metadata: Any) -> str | None:
|
||||
"""Best-effort extraction of the streamed AI message identifier."""
|
||||
candidates = [payload, metadata]
|
||||
if isinstance(payload, Mapping):
|
||||
candidates.append(payload.get("kwargs"))
|
||||
|
||||
for candidate in candidates:
|
||||
if not isinstance(candidate, Mapping):
|
||||
continue
|
||||
for key in ("id", "message_id"):
|
||||
value = candidate.get(key)
|
||||
if isinstance(value, str) and value:
|
||||
return value
|
||||
return None
|
||||
|
||||
|
||||
def _accumulate_stream_text(
|
||||
buffers: dict[str, str],
|
||||
current_message_id: str | None,
|
||||
event_data: Any,
|
||||
) -> tuple[str | None, str | None]:
|
||||
"""Convert a ``messages-tuple`` event into the latest displayable AI text."""
|
||||
payload = event_data
|
||||
metadata: Any = None
|
||||
if isinstance(event_data, (list, tuple)):
|
||||
if event_data:
|
||||
payload = event_data[0]
|
||||
if len(event_data) > 1:
|
||||
metadata = event_data[1]
|
||||
|
||||
if isinstance(payload, str):
|
||||
message_id = current_message_id or "__default__"
|
||||
buffers[message_id] = _merge_stream_text(buffers.get(message_id, ""), payload)
|
||||
return buffers[message_id], message_id
|
||||
|
||||
if not isinstance(payload, Mapping):
|
||||
return None, current_message_id
|
||||
|
||||
payload_type = str(payload.get("type", "")).lower()
|
||||
if "tool" in payload_type:
|
||||
return None, current_message_id
|
||||
|
||||
text = _extract_text_content(payload.get("content"))
|
||||
if not text and isinstance(payload.get("kwargs"), Mapping):
|
||||
text = _extract_text_content(payload["kwargs"].get("content"))
|
||||
if not text:
|
||||
return None, current_message_id
|
||||
|
||||
message_id = _extract_stream_message_id(payload, metadata) or current_message_id or "__default__"
|
||||
buffers[message_id] = _merge_stream_text(buffers.get(message_id, ""), text)
|
||||
return buffers[message_id], message_id
|
||||
|
||||
|
||||
def _extract_artifacts(result: dict | list) -> list[str]:
|
||||
"""Extract artifact paths from the last AI response cycle only.
|
||||
|
||||
Instead of reading the full accumulated ``artifacts`` state (which contains
|
||||
all artifacts ever produced in the thread), this inspects the messages after
|
||||
the last human message and collects file paths from ``present_files`` tool
|
||||
calls. This ensures only newly-produced artifacts are returned.
|
||||
"""
|
||||
if isinstance(result, list):
|
||||
messages = result
|
||||
elif isinstance(result, dict):
|
||||
messages = result.get("messages", [])
|
||||
else:
|
||||
return []
|
||||
|
||||
artifacts: list[str] = []
|
||||
for msg in reversed(messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
# Stop at the last human message — anything before it is a previous turn
|
||||
if msg.get("type") == "human":
|
||||
break
|
||||
# Look for AI messages with present_files tool calls
|
||||
if msg.get("type") == "ai":
|
||||
for tc in msg.get("tool_calls", []):
|
||||
if isinstance(tc, dict) and tc.get("name") == "present_files":
|
||||
args = tc.get("args", {})
|
||||
paths = args.get("filepaths", [])
|
||||
if isinstance(paths, list):
|
||||
artifacts.extend(p for p in paths if isinstance(p, str))
|
||||
return artifacts
|
||||
|
||||
|
||||
def _format_artifact_text(artifacts: list[str]) -> str:
|
||||
"""Format artifact paths into a human-readable text block listing filenames."""
|
||||
import posixpath
|
||||
|
||||
filenames = [posixpath.basename(p) for p in artifacts]
|
||||
if len(filenames) == 1:
|
||||
return f"Created File: 📎 {filenames[0]}"
|
||||
return "Created Files: 📎 " + "、".join(filenames)
|
||||
|
||||
|
||||
_OUTPUTS_VIRTUAL_PREFIX = "/mnt/user-data/outputs/"
|
||||
|
||||
|
||||
def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedAttachment]:
|
||||
"""Resolve virtual artifact paths to host filesystem paths with metadata.
|
||||
|
||||
Only paths under ``/mnt/user-data/outputs/`` are accepted; any other
|
||||
virtual path is rejected with a warning to prevent exfiltrating uploads
|
||||
or workspace files via IM channels.
|
||||
|
||||
Skips artifacts that cannot be resolved (missing files, invalid paths)
|
||||
and logs warnings for them.
|
||||
"""
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
attachments: list[ResolvedAttachment] = []
|
||||
paths = get_paths()
|
||||
outputs_dir = paths.sandbox_outputs_dir(thread_id).resolve()
|
||||
for virtual_path in artifacts:
|
||||
# Security: only allow files from the agent outputs directory
|
||||
if not virtual_path.startswith(_OUTPUTS_VIRTUAL_PREFIX):
|
||||
logger.warning("[Manager] rejected non-outputs artifact path: %s", virtual_path)
|
||||
continue
|
||||
try:
|
||||
actual = paths.resolve_virtual_path(thread_id, virtual_path)
|
||||
# Verify the resolved path is actually under the outputs directory
|
||||
# (guards against path-traversal even after prefix check)
|
||||
try:
|
||||
actual.resolve().relative_to(outputs_dir)
|
||||
except ValueError:
|
||||
logger.warning("[Manager] artifact path escapes outputs dir: %s -> %s", virtual_path, actual)
|
||||
continue
|
||||
if not actual.is_file():
|
||||
logger.warning("[Manager] artifact not found on disk: %s -> %s", virtual_path, actual)
|
||||
continue
|
||||
mime, _ = mimetypes.guess_type(str(actual))
|
||||
mime = mime or "application/octet-stream"
|
||||
attachments.append(
|
||||
ResolvedAttachment(
|
||||
virtual_path=virtual_path,
|
||||
actual_path=actual,
|
||||
filename=actual.name,
|
||||
mime_type=mime,
|
||||
size=actual.stat().st_size,
|
||||
is_image=mime.startswith("image/"),
|
||||
)
|
||||
)
|
||||
except (ValueError, OSError) as exc:
|
||||
logger.warning("[Manager] failed to resolve artifact %s: %s", virtual_path, exc)
|
||||
return attachments
|
||||
|
||||
|
||||
def _prepare_artifact_delivery(
|
||||
thread_id: str,
|
||||
response_text: str,
|
||||
artifacts: list[str],
|
||||
) -> tuple[str, list[ResolvedAttachment]]:
|
||||
"""Resolve attachments and append filename fallbacks to the text response."""
|
||||
attachments: list[ResolvedAttachment] = []
|
||||
if not artifacts:
|
||||
return response_text, attachments
|
||||
|
||||
attachments = _resolve_attachments(thread_id, artifacts)
|
||||
resolved_virtuals = {attachment.virtual_path for attachment in attachments}
|
||||
unresolved = [path for path in artifacts if path not in resolved_virtuals]
|
||||
|
||||
if unresolved:
|
||||
artifact_text = _format_artifact_text(unresolved)
|
||||
response_text = (response_text + "\n\n" + artifact_text) if response_text else artifact_text
|
||||
|
||||
# Always include resolved attachment filenames as a text fallback so files
|
||||
# remain discoverable even when the upload is skipped or fails.
|
||||
if attachments:
|
||||
resolved_text = _format_artifact_text([attachment.virtual_path for attachment in attachments])
|
||||
response_text = (response_text + "\n\n" + resolved_text) if response_text else resolved_text
|
||||
|
||||
return response_text, attachments
|
||||
|
||||
|
||||
async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dict[str, Any]]:
|
||||
if not msg.files:
|
||||
return []
|
||||
|
||||
from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
|
||||
|
||||
uploads_dir = ensure_uploads_dir(thread_id)
|
||||
seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
|
||||
|
||||
created: list[dict[str, Any]] = []
|
||||
file_reader = INBOUND_FILE_READERS.get(msg.channel_name, _read_http_inbound_file)
|
||||
async with httpx.AsyncClient(timeout=httpx.Timeout(20.0)) as client:
|
||||
for idx, f in enumerate(msg.files):
|
||||
if not isinstance(f, dict):
|
||||
continue
|
||||
|
||||
ftype = f.get("type") if isinstance(f.get("type"), str) else "file"
|
||||
filename = f.get("filename") if isinstance(f.get("filename"), str) else ""
|
||||
|
||||
try:
|
||||
data = await file_reader(f, client)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"[Manager] failed to read inbound file: channel=%s, file=%s",
|
||||
msg.channel_name,
|
||||
f.get("url") or filename or idx,
|
||||
)
|
||||
continue
|
||||
|
||||
if data is None:
|
||||
logger.warning(
|
||||
"[Manager] inbound file reader returned no data: channel=%s, file=%s",
|
||||
msg.channel_name,
|
||||
f.get("url") or filename or idx,
|
||||
)
|
||||
continue
|
||||
|
||||
if not filename:
|
||||
ext = ".bin"
|
||||
if ftype == "image":
|
||||
ext = ".png"
|
||||
filename = f"{msg.thread_ts or 'msg'}_{idx}{ext}"
|
||||
|
||||
try:
|
||||
safe_name = claim_unique_filename(normalize_filename(filename), seen_names)
|
||||
except ValueError:
|
||||
logger.warning(
|
||||
"[Manager] skipping inbound file with unsafe filename: channel=%s, file=%r",
|
||||
msg.channel_name,
|
||||
filename,
|
||||
)
|
||||
continue
|
||||
|
||||
dest = uploads_dir / safe_name
|
||||
try:
|
||||
dest.write_bytes(data)
|
||||
except Exception:
|
||||
logger.exception("[Manager] failed to write inbound file: %s", dest)
|
||||
continue
|
||||
|
||||
created.append(
|
||||
{
|
||||
"filename": safe_name,
|
||||
"size": len(data),
|
||||
"path": f"/mnt/user-data/uploads/{safe_name}",
|
||||
"is_image": ftype == "image",
|
||||
}
|
||||
)
|
||||
|
||||
return created
|
||||
|
||||
|
||||
def _format_uploaded_files_block(files: list[dict[str, Any]]) -> str:
|
||||
lines = [
|
||||
"<uploaded_files>",
|
||||
"The following files were uploaded in this message:",
|
||||
"",
|
||||
]
|
||||
if not files:
|
||||
lines.append("(empty)")
|
||||
else:
|
||||
for f in files:
|
||||
filename = f.get("filename", "")
|
||||
size = int(f.get("size") or 0)
|
||||
size_kb = size / 1024 if size else 0
|
||||
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
|
||||
path = f.get("path", "")
|
||||
is_image = bool(f.get("is_image"))
|
||||
file_kind = "image" if is_image else "file"
|
||||
lines.append(f"- {filename} ({size_str})")
|
||||
lines.append(f" Type: {file_kind}")
|
||||
lines.append(f" Path: {path}")
|
||||
lines.append("")
|
||||
lines.append("Use `read_file` for text-based files and documents.")
|
||||
lines.append("Use `view_image` for image files (jpg, jpeg, png, webp) so the model can inspect the image content.")
|
||||
lines.append("</uploaded_files>")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
class ChannelManager:
|
||||
"""Core dispatcher that bridges IM channels to the DeerFlow agent.
|
||||
|
||||
It reads from the MessageBus inbound queue, creates/reuses threads on
|
||||
the LangGraph Server, sends messages via ``runs.wait``, and publishes
|
||||
outbound responses back through the bus.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
bus: MessageBus,
|
||||
store: ChannelStore,
|
||||
*,
|
||||
max_concurrency: int = 5,
|
||||
langgraph_url: str = DEFAULT_LANGGRAPH_URL,
|
||||
gateway_url: str = DEFAULT_GATEWAY_URL,
|
||||
assistant_id: str = DEFAULT_ASSISTANT_ID,
|
||||
default_session: dict[str, Any] | None = None,
|
||||
channel_sessions: dict[str, Any] | None = None,
|
||||
) -> None:
|
||||
self.bus = bus
|
||||
self.store = store
|
||||
self._max_concurrency = max_concurrency
|
||||
self._langgraph_url = langgraph_url
|
||||
self._gateway_url = gateway_url
|
||||
self._assistant_id = assistant_id
|
||||
self._default_session = _as_dict(default_session)
|
||||
self._channel_sessions = dict(channel_sessions or {})
|
||||
self._client = None # lazy init — langgraph_sdk async client
|
||||
self._semaphore: asyncio.Semaphore | None = None
|
||||
self._running = False
|
||||
self._task: asyncio.Task | None = None
|
||||
|
||||
@staticmethod
|
||||
def _channel_supports_streaming(channel_name: str) -> bool:
|
||||
return CHANNEL_CAPABILITIES.get(channel_name, {}).get("supports_streaming", False)
|
||||
|
||||
def _resolve_session_layer(self, msg: InboundMessage) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
channel_layer = _as_dict(self._channel_sessions.get(msg.channel_name))
|
||||
users_layer = _as_dict(channel_layer.get("users"))
|
||||
user_layer = _as_dict(users_layer.get(msg.user_id))
|
||||
return channel_layer, user_layer
|
||||
|
||||
def _resolve_run_params(self, msg: InboundMessage, thread_id: str) -> tuple[str, dict[str, Any], dict[str, Any]]:
|
||||
channel_layer, user_layer = self._resolve_session_layer(msg)
|
||||
|
||||
assistant_id = user_layer.get("assistant_id") or channel_layer.get("assistant_id") or self._default_session.get("assistant_id") or self._assistant_id
|
||||
if not isinstance(assistant_id, str) or not assistant_id.strip():
|
||||
assistant_id = self._assistant_id
|
||||
|
||||
run_config = _merge_dicts(
|
||||
DEFAULT_RUN_CONFIG,
|
||||
self._default_session.get("config"),
|
||||
channel_layer.get("config"),
|
||||
user_layer.get("config"),
|
||||
)
|
||||
|
||||
run_context = _merge_dicts(
|
||||
DEFAULT_RUN_CONTEXT,
|
||||
self._default_session.get("context"),
|
||||
channel_layer.get("context"),
|
||||
user_layer.get("context"),
|
||||
{"thread_id": thread_id},
|
||||
)
|
||||
|
||||
# Custom agents are implemented as lead_agent + agent_name context.
|
||||
# Keep backward compatibility for channel configs that set
|
||||
# assistant_id: <custom-agent-name> by routing through lead_agent.
|
||||
if assistant_id != DEFAULT_ASSISTANT_ID:
|
||||
run_context.setdefault("agent_name", _normalize_custom_agent_name(assistant_id))
|
||||
assistant_id = DEFAULT_ASSISTANT_ID
|
||||
|
||||
return assistant_id, run_config, run_context
|
||||
|
||||
# -- LangGraph SDK client (lazy) ----------------------------------------
|
||||
|
||||
def _get_client(self):
|
||||
"""Return the ``langgraph_sdk`` async client, creating it on first use."""
|
||||
if self._client is None:
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
self._client = get_client(url=self._langgraph_url)
|
||||
return self._client
|
||||
|
||||
# -- lifecycle ---------------------------------------------------------
|
||||
|
||||
async def start(self) -> None:
|
||||
"""Start the dispatch loop."""
|
||||
if self._running:
|
||||
return
|
||||
self._running = True
|
||||
self._semaphore = asyncio.Semaphore(self._max_concurrency)
|
||||
self._task = asyncio.create_task(self._dispatch_loop())
|
||||
logger.info("ChannelManager started (max_concurrency=%d)", self._max_concurrency)
|
||||
|
||||
async def stop(self) -> None:
|
||||
"""Stop the dispatch loop."""
|
||||
self._running = False
|
||||
if self._task:
|
||||
self._task.cancel()
|
||||
try:
|
||||
await self._task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
self._task = None
|
||||
logger.info("ChannelManager stopped")
|
||||
|
||||
# -- dispatch loop -----------------------------------------------------
|
||||
|
||||
async def _dispatch_loop(self) -> None:
|
||||
logger.info("[Manager] dispatch loop started, waiting for inbound messages")
|
||||
while self._running:
|
||||
try:
|
||||
msg = await asyncio.wait_for(self.bus.get_inbound(), timeout=1.0)
|
||||
except TimeoutError:
|
||||
continue
|
||||
except asyncio.CancelledError:
|
||||
break
|
||||
|
||||
logger.info(
|
||||
"[Manager] received inbound: channel=%s, chat_id=%s, type=%s, text=%r",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
msg.msg_type.value,
|
||||
msg.text[:100] if msg.text else "",
|
||||
)
|
||||
task = asyncio.create_task(self._handle_message(msg))
|
||||
task.add_done_callback(self._log_task_error)
|
||||
|
||||
@staticmethod
|
||||
def _log_task_error(task: asyncio.Task) -> None:
|
||||
"""Surface unhandled exceptions from background tasks."""
|
||||
if task.cancelled():
|
||||
return
|
||||
exc = task.exception()
|
||||
if exc:
|
||||
logger.error("[Manager] unhandled error in message task: %s", exc, exc_info=exc)
|
||||
|
||||
async def _handle_message(self, msg: InboundMessage) -> None:
|
||||
async with self._semaphore:
|
||||
try:
|
||||
if msg.msg_type == InboundMessageType.COMMAND:
|
||||
await self._handle_command(msg)
|
||||
else:
|
||||
await self._handle_chat(msg)
|
||||
except InvalidChannelSessionConfigError as exc:
|
||||
logger.warning(
|
||||
"Invalid channel session config for %s (chat=%s): %s",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
exc,
|
||||
)
|
||||
await self._send_error(msg, str(exc))
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"Error handling message from %s (chat=%s)",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
)
|
||||
await self._send_error(msg, "An internal error occurred. Please try again.")
|
||||
|
||||
# -- chat handling -----------------------------------------------------
|
||||
|
||||
async def _create_thread(self, client, msg: InboundMessage) -> str:
|
||||
"""Create a new thread on the LangGraph Server and store the mapping."""
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
self.store.set_thread_id(
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
thread_id,
|
||||
topic_id=msg.topic_id,
|
||||
user_id=msg.user_id,
|
||||
)
|
||||
logger.info("[Manager] new thread created on LangGraph Server: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
|
||||
return thread_id
|
||||
|
||||
async def _handle_chat(self, msg: InboundMessage, extra_context: dict[str, Any] | None = None) -> None:
|
||||
client = self._get_client()
|
||||
|
||||
# Look up existing DeerFlow thread.
|
||||
# topic_id may be None (e.g. Telegram private chats) — the store
|
||||
# handles this by using the "channel:chat_id" key without a topic suffix.
|
||||
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
|
||||
if thread_id:
|
||||
logger.info("[Manager] reusing thread: thread_id=%s for topic_id=%s", thread_id, msg.topic_id)
|
||||
|
||||
# No existing thread found — create a new one
|
||||
if thread_id is None:
|
||||
thread_id = await self._create_thread(client, msg)
|
||||
|
||||
assistant_id, run_config, run_context = self._resolve_run_params(msg, thread_id)
|
||||
|
||||
# If the inbound message contains file attachments, let the channel
|
||||
# materialize (download) them and update msg.text to include sandbox file paths.
|
||||
# This enables downstream models to access user-uploaded files by path.
|
||||
# Channels that do not support file download will simply return the original message.
|
||||
if msg.files:
|
||||
from .service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
channel = service.get_channel(msg.channel_name) if service else None
|
||||
logger.info("[Manager] preparing receive file context for %d attachments", len(msg.files))
|
||||
msg = await channel.receive_file(msg, thread_id) if channel else msg
|
||||
if extra_context:
|
||||
run_context.update(extra_context)
|
||||
|
||||
uploaded = await _ingest_inbound_files(thread_id, msg)
|
||||
if uploaded:
|
||||
msg.text = f"{_format_uploaded_files_block(uploaded)}\n\n{msg.text}".strip()
|
||||
|
||||
if self._channel_supports_streaming(msg.channel_name):
|
||||
await self._handle_streaming_chat(
|
||||
client,
|
||||
msg,
|
||||
thread_id,
|
||||
assistant_id,
|
||||
run_config,
|
||||
run_context,
|
||||
)
|
||||
return
|
||||
|
||||
logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "human", "content": msg.text}]},
|
||||
config=run_config,
|
||||
context=run_context,
|
||||
)
|
||||
|
||||
response_text = _extract_response_text(result)
|
||||
artifacts = _extract_artifacts(result)
|
||||
|
||||
logger.info(
|
||||
"[Manager] agent response received: thread_id=%s, response_len=%d, artifacts=%d",
|
||||
thread_id,
|
||||
len(response_text) if response_text else 0,
|
||||
len(artifacts),
|
||||
)
|
||||
|
||||
response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
|
||||
|
||||
if not response_text:
|
||||
if attachments:
|
||||
response_text = _format_artifact_text([a.virtual_path for a in attachments])
|
||||
else:
|
||||
response_text = "(No response from agent)"
|
||||
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=response_text,
|
||||
artifacts=artifacts,
|
||||
attachments=attachments,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
logger.info("[Manager] publishing outbound message to bus: channel=%s, chat_id=%s", msg.channel_name, msg.chat_id)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
|
||||
async def _handle_streaming_chat(
|
||||
self,
|
||||
client,
|
||||
msg: InboundMessage,
|
||||
thread_id: str,
|
||||
assistant_id: str,
|
||||
run_config: dict[str, Any],
|
||||
run_context: dict[str, Any],
|
||||
) -> None:
|
||||
logger.info("[Manager] invoking runs.stream(thread_id=%s, text=%r)", thread_id, msg.text[:100])
|
||||
|
||||
last_values: dict[str, Any] | list | None = None
|
||||
streamed_buffers: dict[str, str] = {}
|
||||
current_message_id: str | None = None
|
||||
latest_text = ""
|
||||
last_published_text = ""
|
||||
last_publish_at = 0.0
|
||||
stream_error: BaseException | None = None
|
||||
|
||||
try:
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "human", "content": msg.text}]},
|
||||
config=run_config,
|
||||
context=run_context,
|
||||
stream_mode=["messages-tuple", "values"],
|
||||
multitask_strategy="reject",
|
||||
):
|
||||
event = getattr(chunk, "event", "")
|
||||
data = getattr(chunk, "data", None)
|
||||
|
||||
if event == "messages-tuple":
|
||||
accumulated_text, current_message_id = _accumulate_stream_text(streamed_buffers, current_message_id, data)
|
||||
if accumulated_text:
|
||||
latest_text = accumulated_text
|
||||
elif event == "values" and isinstance(data, (dict, list)):
|
||||
last_values = data
|
||||
snapshot_text = _extract_response_text(data)
|
||||
if snapshot_text:
|
||||
latest_text = snapshot_text
|
||||
|
||||
if not latest_text or latest_text == last_published_text:
|
||||
continue
|
||||
|
||||
now = time.monotonic()
|
||||
if last_published_text and now - last_publish_at < STREAM_UPDATE_MIN_INTERVAL_SECONDS:
|
||||
continue
|
||||
|
||||
await self.bus.publish_outbound(
|
||||
OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=latest_text,
|
||||
is_final=False,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
)
|
||||
last_published_text = latest_text
|
||||
last_publish_at = now
|
||||
except Exception as exc:
|
||||
stream_error = exc
|
||||
if _is_thread_busy_error(exc):
|
||||
logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
|
||||
else:
|
||||
logger.exception("[Manager] streaming error: thread_id=%s", thread_id)
|
||||
finally:
|
||||
result = last_values if last_values is not None else {"messages": [{"type": "ai", "content": latest_text}]}
|
||||
response_text = _extract_response_text(result)
|
||||
artifacts = _extract_artifacts(result)
|
||||
response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
|
||||
|
||||
if not response_text:
|
||||
if attachments:
|
||||
response_text = _format_artifact_text([attachment.virtual_path for attachment in attachments])
|
||||
elif stream_error:
|
||||
if _is_thread_busy_error(stream_error):
|
||||
response_text = THREAD_BUSY_MESSAGE
|
||||
else:
|
||||
response_text = "An error occurred while processing your request. Please try again."
|
||||
else:
|
||||
response_text = latest_text or "(No response from agent)"
|
||||
|
||||
logger.info(
|
||||
"[Manager] streaming response completed: thread_id=%s, response_len=%d, artifacts=%d, error=%s",
|
||||
thread_id,
|
||||
len(response_text),
|
||||
len(artifacts),
|
||||
stream_error,
|
||||
)
|
||||
await self.bus.publish_outbound(
|
||||
OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=thread_id,
|
||||
text=response_text,
|
||||
artifacts=artifacts,
|
||||
attachments=attachments,
|
||||
is_final=True,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
)
|
||||
|
||||
# -- command handling --------------------------------------------------
|
||||
|
||||
async def _handle_command(self, msg: InboundMessage) -> None:
|
||||
text = msg.text.strip()
|
||||
parts = text.split(maxsplit=1)
|
||||
command = parts[0].lower().lstrip("/")
|
||||
|
||||
if command == "bootstrap":
|
||||
from dataclasses import replace as _dc_replace
|
||||
|
||||
chat_text = parts[1] if len(parts) > 1 else "Initialize workspace"
|
||||
chat_msg = _dc_replace(msg, text=chat_text, msg_type=InboundMessageType.CHAT)
|
||||
await self._handle_chat(chat_msg, extra_context={"is_bootstrap": True})
|
||||
return
|
||||
|
||||
if command == "new":
|
||||
# Create a new thread on the LangGraph Server
|
||||
client = self._get_client()
|
||||
thread = await client.threads.create()
|
||||
new_thread_id = thread["thread_id"]
|
||||
self.store.set_thread_id(
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
new_thread_id,
|
||||
topic_id=msg.topic_id,
|
||||
user_id=msg.user_id,
|
||||
)
|
||||
reply = "New conversation started."
|
||||
elif command == "status":
|
||||
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
|
||||
reply = f"Active thread: {thread_id}" if thread_id else "No active conversation."
|
||||
elif command == "models":
|
||||
reply = await self._fetch_gateway("/api/models", "models")
|
||||
elif command == "memory":
|
||||
reply = await self._fetch_gateway("/api/memory", "memory")
|
||||
elif command == "help":
|
||||
reply = (
|
||||
"Available commands:\n"
|
||||
"/bootstrap — Start a bootstrap session (enables agent setup)\n"
|
||||
"/new — Start a new conversation\n"
|
||||
"/status — Show current thread info\n"
|
||||
"/models — List available models\n"
|
||||
"/memory — Show memory status\n"
|
||||
"/help — Show this help"
|
||||
)
|
||||
else:
|
||||
available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
|
||||
reply = f"Unknown command: /{command}. Available commands: {available}"
|
||||
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
|
||||
text=reply,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
|
||||
async def _fetch_gateway(self, path: str, kind: str) -> str:
|
||||
"""Fetch data from the Gateway API for command responses."""
|
||||
import httpx
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient() as http:
|
||||
resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch %s from gateway", kind)
|
||||
return f"Failed to fetch {kind} information."
|
||||
|
||||
if kind == "models":
|
||||
names = [m["name"] for m in data.get("models", [])]
|
||||
return ("Available models:\n" + "\n".join(f"• {n}" for n in names)) if names else "No models configured."
|
||||
elif kind == "memory":
|
||||
facts = data.get("facts", [])
|
||||
return f"Memory contains {len(facts)} fact(s)."
|
||||
return str(data)
|
||||
|
||||
# -- error helper ------------------------------------------------------
|
||||
|
||||
async def _send_error(self, msg: InboundMessage, error_text: str) -> None:
|
||||
outbound = OutboundMessage(
|
||||
channel_name=msg.channel_name,
|
||||
chat_id=msg.chat_id,
|
||||
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
|
||||
text=error_text,
|
||||
thread_ts=msg.thread_ts,
|
||||
)
|
||||
await self.bus.publish_outbound(outbound)
|
||||
173
deer-flow/backend/app/channels/message_bus.py
Normal file
173
deer-flow/backend/app/channels/message_bus.py
Normal file
@@ -0,0 +1,173 @@
|
||||
"""MessageBus — async pub/sub hub that decouples channels from the agent dispatcher."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
from collections.abc import Callable, Coroutine
|
||||
from dataclasses import dataclass, field
|
||||
from enum import StrEnum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Message types
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class InboundMessageType(StrEnum):
|
||||
"""Types of messages arriving from IM channels."""
|
||||
|
||||
CHAT = "chat"
|
||||
COMMAND = "command"
|
||||
|
||||
|
||||
@dataclass
|
||||
class InboundMessage:
|
||||
"""A message arriving from an IM channel toward the agent dispatcher.
|
||||
|
||||
Attributes:
|
||||
channel_name: Name of the source channel (e.g. "feishu", "slack").
|
||||
chat_id: Platform-specific chat/conversation identifier.
|
||||
user_id: Platform-specific user identifier.
|
||||
text: The message text.
|
||||
msg_type: Whether this is a regular chat message or a command.
|
||||
thread_ts: Optional platform thread identifier (for threaded replies).
|
||||
topic_id: Conversation topic identifier used to map to a DeerFlow thread.
|
||||
Messages sharing the same ``topic_id`` within a ``chat_id`` will
|
||||
reuse the same DeerFlow thread. When ``None``, each message
|
||||
creates a new thread (one-shot Q&A).
|
||||
files: Optional list of file attachments (platform-specific dicts).
|
||||
metadata: Arbitrary extra data from the channel.
|
||||
created_at: Unix timestamp when the message was created.
|
||||
"""
|
||||
|
||||
channel_name: str
|
||||
chat_id: str
|
||||
user_id: str
|
||||
text: str
|
||||
msg_type: InboundMessageType = InboundMessageType.CHAT
|
||||
thread_ts: str | None = None
|
||||
topic_id: str | None = None
|
||||
files: list[dict[str, Any]] = field(default_factory=list)
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
created_at: float = field(default_factory=time.time)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ResolvedAttachment:
|
||||
"""A file attachment resolved to a host filesystem path, ready for upload.
|
||||
|
||||
Attributes:
|
||||
virtual_path: Original virtual path (e.g. /mnt/user-data/outputs/report.pdf).
|
||||
actual_path: Resolved host filesystem path.
|
||||
filename: Basename of the file.
|
||||
mime_type: MIME type (e.g. "application/pdf").
|
||||
size: File size in bytes.
|
||||
is_image: True for image/* MIME types (platforms may handle images differently).
|
||||
"""
|
||||
|
||||
virtual_path: str
|
||||
actual_path: Path
|
||||
filename: str
|
||||
mime_type: str
|
||||
size: int
|
||||
is_image: bool
|
||||
|
||||
|
||||
@dataclass
|
||||
class OutboundMessage:
|
||||
"""A message from the agent dispatcher back to a channel.
|
||||
|
||||
Attributes:
|
||||
channel_name: Target channel name (used for routing).
|
||||
chat_id: Target chat/conversation identifier.
|
||||
thread_id: DeerFlow thread ID that produced this response.
|
||||
text: The response text.
|
||||
artifacts: List of artifact paths produced by the agent.
|
||||
is_final: Whether this is the final message in the response stream.
|
||||
thread_ts: Optional platform thread identifier for threaded replies.
|
||||
metadata: Arbitrary extra data.
|
||||
created_at: Unix timestamp.
|
||||
"""
|
||||
|
||||
channel_name: str
|
||||
chat_id: str
|
||||
thread_id: str
|
||||
text: str
|
||||
artifacts: list[str] = field(default_factory=list)
|
||||
attachments: list[ResolvedAttachment] = field(default_factory=list)
|
||||
is_final: bool = True
|
||||
thread_ts: str | None = None
|
||||
metadata: dict[str, Any] = field(default_factory=dict)
|
||||
created_at: float = field(default_factory=time.time)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# MessageBus
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
OutboundCallback = Callable[[OutboundMessage], Coroutine[Any, Any, None]]
|
||||
|
||||
|
||||
class MessageBus:
|
||||
"""Async pub/sub hub connecting channels and the agent dispatcher.
|
||||
|
||||
Channels publish inbound messages; the dispatcher consumes them.
|
||||
The dispatcher publishes outbound messages; channels receive them
|
||||
via registered callbacks.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._inbound_queue: asyncio.Queue[InboundMessage] = asyncio.Queue()
|
||||
self._outbound_listeners: list[OutboundCallback] = []
|
||||
|
||||
# -- inbound -----------------------------------------------------------
|
||||
|
||||
async def publish_inbound(self, msg: InboundMessage) -> None:
|
||||
"""Enqueue an inbound message from a channel."""
|
||||
await self._inbound_queue.put(msg)
|
||||
logger.info(
|
||||
"[Bus] inbound enqueued: channel=%s, chat_id=%s, type=%s, queue_size=%d",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
msg.msg_type.value,
|
||||
self._inbound_queue.qsize(),
|
||||
)
|
||||
|
||||
async def get_inbound(self) -> InboundMessage:
|
||||
"""Block until the next inbound message is available."""
|
||||
return await self._inbound_queue.get()
|
||||
|
||||
@property
|
||||
def inbound_queue(self) -> asyncio.Queue[InboundMessage]:
|
||||
return self._inbound_queue
|
||||
|
||||
# -- outbound ----------------------------------------------------------
|
||||
|
||||
def subscribe_outbound(self, callback: OutboundCallback) -> None:
|
||||
"""Register an async callback for outbound messages."""
|
||||
self._outbound_listeners.append(callback)
|
||||
|
||||
def unsubscribe_outbound(self, callback: OutboundCallback) -> None:
|
||||
"""Remove a previously registered outbound callback."""
|
||||
self._outbound_listeners = [cb for cb in self._outbound_listeners if cb is not callback]
|
||||
|
||||
async def publish_outbound(self, msg: OutboundMessage) -> None:
|
||||
"""Dispatch an outbound message to all registered listeners."""
|
||||
logger.info(
|
||||
"[Bus] outbound dispatching: channel=%s, chat_id=%s, listeners=%d, text_len=%d",
|
||||
msg.channel_name,
|
||||
msg.chat_id,
|
||||
len(self._outbound_listeners),
|
||||
len(msg.text),
|
||||
)
|
||||
for callback in self._outbound_listeners:
|
||||
try:
|
||||
await callback(msg)
|
||||
except Exception:
|
||||
logger.exception("Error in outbound callback for channel=%s", msg.channel_name)
|
||||
200
deer-flow/backend/app/channels/service.py
Normal file
200
deer-flow/backend/app/channels/service.py
Normal file
@@ -0,0 +1,200 @@
|
||||
"""ChannelService — manages the lifecycle of all IM channels."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
|
||||
from app.channels.message_bus import MessageBus
|
||||
from app.channels.store import ChannelStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Channel name → import path for lazy loading
|
||||
_CHANNEL_REGISTRY: dict[str, str] = {
|
||||
"discord": "app.channels.discord:DiscordChannel",
|
||||
"feishu": "app.channels.feishu:FeishuChannel",
|
||||
"slack": "app.channels.slack:SlackChannel",
|
||||
"telegram": "app.channels.telegram:TelegramChannel",
|
||||
"wechat": "app.channels.wechat:WechatChannel",
|
||||
"wecom": "app.channels.wecom:WeComChannel",
|
||||
}
|
||||
|
||||
_CHANNELS_LANGGRAPH_URL_ENV = "DEER_FLOW_CHANNELS_LANGGRAPH_URL"
|
||||
_CHANNELS_GATEWAY_URL_ENV = "DEER_FLOW_CHANNELS_GATEWAY_URL"
|
||||
|
||||
|
||||
def _resolve_service_url(config: dict[str, Any], config_key: str, env_key: str, default: str) -> str:
|
||||
value = config.pop(config_key, None)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value
|
||||
env_value = os.getenv(env_key, "").strip()
|
||||
if env_value:
|
||||
return env_value
|
||||
return default
|
||||
|
||||
|
||||
class ChannelService:
|
||||
"""Manages the lifecycle of all configured IM channels.
|
||||
|
||||
Reads configuration from ``config.yaml`` under the ``channels`` key,
|
||||
instantiates enabled channels, and starts the ChannelManager dispatcher.
|
||||
"""
|
||||
|
||||
def __init__(self, channels_config: dict[str, Any] | None = None) -> None:
|
||||
self.bus = MessageBus()
|
||||
self.store = ChannelStore()
|
||||
config = dict(channels_config or {})
|
||||
langgraph_url = _resolve_service_url(config, "langgraph_url", _CHANNELS_LANGGRAPH_URL_ENV, DEFAULT_LANGGRAPH_URL)
|
||||
gateway_url = _resolve_service_url(config, "gateway_url", _CHANNELS_GATEWAY_URL_ENV, DEFAULT_GATEWAY_URL)
|
||||
default_session = config.pop("session", None)
|
||||
channel_sessions = {name: channel_config.get("session") for name, channel_config in config.items() if isinstance(channel_config, dict)}
|
||||
self.manager = ChannelManager(
|
||||
bus=self.bus,
|
||||
store=self.store,
|
||||
langgraph_url=langgraph_url,
|
||||
gateway_url=gateway_url,
|
||||
default_session=default_session if isinstance(default_session, dict) else None,
|
||||
channel_sessions=channel_sessions,
|
||||
)
|
||||
self._channels: dict[str, Any] = {} # name -> Channel instance
|
||||
self._config = config
|
||||
self._running = False
|
||||
|
||||
@classmethod
|
||||
def from_app_config(cls) -> ChannelService:
|
||||
"""Create a ChannelService from the application config."""
|
||||
from deerflow.config.app_config import get_app_config
|
||||
|
||||
config = get_app_config()
|
||||
channels_config = {}
|
||||
# extra fields are allowed by AppConfig (extra="allow")
|
||||
extra = config.model_extra or {}
|
||||
if "channels" in extra:
|
||||
channels_config = extra["channels"]
|
||||
return cls(channels_config=channels_config)
|
||||
|
||||
async def start(self) -> None:
|
||||
"""Start the manager and all enabled channels."""
|
||||
if self._running:
|
||||
return
|
||||
|
||||
await self.manager.start()
|
||||
|
||||
for name, channel_config in self._config.items():
|
||||
if not isinstance(channel_config, dict):
|
||||
continue
|
||||
if not channel_config.get("enabled", False):
|
||||
logger.info("Channel %s is disabled, skipping", name)
|
||||
continue
|
||||
|
||||
await self._start_channel(name, channel_config)
|
||||
|
||||
self._running = True
|
||||
logger.info("ChannelService started with channels: %s", list(self._channels.keys()))
|
||||
|
||||
async def stop(self) -> None:
|
||||
"""Stop all channels and the manager."""
|
||||
for name, channel in list(self._channels.items()):
|
||||
try:
|
||||
await channel.stop()
|
||||
logger.info("Channel %s stopped", name)
|
||||
except Exception:
|
||||
logger.exception("Error stopping channel %s", name)
|
||||
self._channels.clear()
|
||||
|
||||
await self.manager.stop()
|
||||
self._running = False
|
||||
logger.info("ChannelService stopped")
|
||||
|
||||
async def restart_channel(self, name: str) -> bool:
|
||||
"""Restart a specific channel. Returns True if successful."""
|
||||
if name in self._channels:
|
||||
try:
|
||||
await self._channels[name].stop()
|
||||
except Exception:
|
||||
logger.exception("Error stopping channel %s for restart", name)
|
||||
del self._channels[name]
|
||||
|
||||
config = self._config.get(name)
|
||||
if not config or not isinstance(config, dict):
|
||||
logger.warning("No config for channel %s", name)
|
||||
return False
|
||||
|
||||
return await self._start_channel(name, config)
|
||||
|
||||
async def _start_channel(self, name: str, config: dict[str, Any]) -> bool:
|
||||
"""Instantiate and start a single channel."""
|
||||
import_path = _CHANNEL_REGISTRY.get(name)
|
||||
if not import_path:
|
||||
logger.warning("Unknown channel type: %s", name)
|
||||
return False
|
||||
|
||||
try:
|
||||
from deerflow.reflection import resolve_class
|
||||
|
||||
channel_cls = resolve_class(import_path, base_class=None)
|
||||
except Exception:
|
||||
logger.exception("Failed to import channel class for %s", name)
|
||||
return False
|
||||
|
||||
try:
|
||||
channel = channel_cls(bus=self.bus, config=config)
|
||||
await channel.start()
|
||||
self._channels[name] = channel
|
||||
logger.info("Channel %s started", name)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("Failed to start channel %s", name)
|
||||
return False
|
||||
|
||||
def get_status(self) -> dict[str, Any]:
|
||||
"""Return status information for all channels."""
|
||||
channels_status = {}
|
||||
for name in _CHANNEL_REGISTRY:
|
||||
config = self._config.get(name, {})
|
||||
enabled = isinstance(config, dict) and config.get("enabled", False)
|
||||
running = name in self._channels and self._channels[name].is_running
|
||||
channels_status[name] = {
|
||||
"enabled": enabled,
|
||||
"running": running,
|
||||
}
|
||||
return {
|
||||
"service_running": self._running,
|
||||
"channels": channels_status,
|
||||
}
|
||||
|
||||
def get_channel(self, name: str) -> Channel | None:
|
||||
"""Return a running channel instance by name when available."""
|
||||
return self._channels.get(name)
|
||||
|
||||
|
||||
# -- singleton access -------------------------------------------------------
|
||||
|
||||
_channel_service: ChannelService | None = None
|
||||
|
||||
|
||||
def get_channel_service() -> ChannelService | None:
|
||||
"""Get the singleton ChannelService instance (if started)."""
|
||||
return _channel_service
|
||||
|
||||
|
||||
async def start_channel_service() -> ChannelService:
|
||||
"""Create and start the global ChannelService from app config."""
|
||||
global _channel_service
|
||||
if _channel_service is not None:
|
||||
return _channel_service
|
||||
_channel_service = ChannelService.from_app_config()
|
||||
await _channel_service.start()
|
||||
return _channel_service
|
||||
|
||||
|
||||
async def stop_channel_service() -> None:
|
||||
"""Stop the global ChannelService."""
|
||||
global _channel_service
|
||||
if _channel_service is not None:
|
||||
await _channel_service.stop()
|
||||
_channel_service = None
|
||||
246
deer-flow/backend/app/channels/slack.py
Normal file
246
deer-flow/backend/app/channels/slack.py
Normal file
@@ -0,0 +1,246 @@
|
||||
"""Slack channel — connects via Socket Mode (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from markdown_to_mrkdwn import SlackMarkdownConverter
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_slack_md_converter = SlackMarkdownConverter()
|
||||
|
||||
|
||||
class SlackChannel(Channel):
|
||||
"""Slack IM channel using Socket Mode (WebSocket, no public IP).
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.slack``):
|
||||
- ``bot_token``: Slack Bot User OAuth Token (xoxb-...).
|
||||
- ``app_token``: Slack App-Level Token (xapp-...) for Socket Mode.
|
||||
- ``allowed_users``: (optional) List of allowed Slack user IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="slack", bus=bus, config=config)
|
||||
self._socket_client = None
|
||||
self._web_client = None
|
||||
self._loop: asyncio.AbstractEventLoop | None = None
|
||||
self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
from slack_sdk import WebClient
|
||||
from slack_sdk.socket_mode import SocketModeClient
|
||||
from slack_sdk.socket_mode.response import SocketModeResponse
|
||||
except ImportError:
|
||||
logger.error("slack-sdk is not installed. Install it with: uv add slack-sdk")
|
||||
return
|
||||
|
||||
self._SocketModeResponse = SocketModeResponse
|
||||
|
||||
bot_token = self.config.get("bot_token", "")
|
||||
app_token = self.config.get("app_token", "")
|
||||
|
||||
if not bot_token or not app_token:
|
||||
logger.error("Slack channel requires bot_token and app_token")
|
||||
return
|
||||
|
||||
self._web_client = WebClient(token=bot_token)
|
||||
self._socket_client = SocketModeClient(
|
||||
app_token=app_token,
|
||||
web_client=self._web_client,
|
||||
)
|
||||
self._loop = asyncio.get_event_loop()
|
||||
|
||||
self._socket_client.socket_mode_request_listeners.append(self._on_socket_event)
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Start socket mode in background thread
|
||||
asyncio.get_event_loop().run_in_executor(None, self._socket_client.connect)
|
||||
logger.info("Slack channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._socket_client:
|
||||
self._socket_client.close()
|
||||
self._socket_client = None
|
||||
logger.info("Slack channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._web_client:
|
||||
return
|
||||
|
||||
kwargs: dict[str, Any] = {
|
||||
"channel": msg.chat_id,
|
||||
"text": _slack_md_converter.convert(msg.text),
|
||||
}
|
||||
if msg.thread_ts:
|
||||
kwargs["thread_ts"] = msg.thread_ts
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await asyncio.to_thread(self._web_client.chat_postMessage, **kwargs)
|
||||
# Add a completion reaction to the thread root
|
||||
if msg.thread_ts:
|
||||
await asyncio.to_thread(
|
||||
self._add_reaction,
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
"white_check_mark",
|
||||
)
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Slack] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Slack] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
# Add failure reaction on error
|
||||
if msg.thread_ts:
|
||||
try:
|
||||
await asyncio.to_thread(
|
||||
self._add_reaction,
|
||||
msg.chat_id,
|
||||
msg.thread_ts,
|
||||
"x",
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Slack send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._web_client:
|
||||
return False
|
||||
|
||||
try:
|
||||
kwargs: dict[str, Any] = {
|
||||
"channel": msg.chat_id,
|
||||
"file": str(attachment.actual_path),
|
||||
"filename": attachment.filename,
|
||||
"title": attachment.filename,
|
||||
}
|
||||
if msg.thread_ts:
|
||||
kwargs["thread_ts"] = msg.thread_ts
|
||||
|
||||
await asyncio.to_thread(self._web_client.files_upload_v2, **kwargs)
|
||||
logger.info("[Slack] file uploaded: %s to channel=%s", attachment.filename, msg.chat_id)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Slack] failed to upload file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
|
||||
def _add_reaction(self, channel_id: str, timestamp: str, emoji: str) -> None:
|
||||
"""Add an emoji reaction to a message (best-effort, non-blocking)."""
|
||||
if not self._web_client:
|
||||
return
|
||||
try:
|
||||
self._web_client.reactions_add(
|
||||
channel=channel_id,
|
||||
timestamp=timestamp,
|
||||
name=emoji,
|
||||
)
|
||||
except Exception as exc:
|
||||
if "already_reacted" not in str(exc):
|
||||
logger.warning("[Slack] failed to add reaction %s: %s", emoji, exc)
|
||||
|
||||
def _send_running_reply(self, channel_id: str, thread_ts: str) -> None:
|
||||
"""Send a 'Working on it......' reply in the thread (called from SDK thread)."""
|
||||
if not self._web_client:
|
||||
return
|
||||
try:
|
||||
self._web_client.chat_postMessage(
|
||||
channel=channel_id,
|
||||
text=":hourglass_flowing_sand: Working on it...",
|
||||
thread_ts=thread_ts,
|
||||
)
|
||||
logger.info("[Slack] 'Working on it...' reply sent in channel=%s, thread_ts=%s", channel_id, thread_ts)
|
||||
except Exception:
|
||||
logger.exception("[Slack] failed to send running reply in channel=%s", channel_id)
|
||||
|
||||
def _on_socket_event(self, client, req) -> None:
|
||||
"""Called by slack-sdk for each Socket Mode event."""
|
||||
try:
|
||||
# Acknowledge the event
|
||||
response = self._SocketModeResponse(envelope_id=req.envelope_id)
|
||||
client.send_socket_mode_response(response)
|
||||
|
||||
event_type = req.type
|
||||
if event_type != "events_api":
|
||||
return
|
||||
|
||||
event = req.payload.get("event", {})
|
||||
etype = event.get("type", "")
|
||||
|
||||
# Handle message events (DM or @mention)
|
||||
if etype in ("message", "app_mention"):
|
||||
self._handle_message_event(event)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error processing Slack event")
|
||||
|
||||
def _handle_message_event(self, event: dict) -> None:
|
||||
# Ignore bot messages
|
||||
if event.get("bot_id") or event.get("subtype"):
|
||||
return
|
||||
|
||||
user_id = event.get("user", "")
|
||||
|
||||
# Check allowed users
|
||||
if self._allowed_users and user_id not in self._allowed_users:
|
||||
logger.debug("Ignoring message from non-allowed user: %s", user_id)
|
||||
return
|
||||
|
||||
text = event.get("text", "").strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
channel_id = event.get("channel", "")
|
||||
thread_ts = event.get("thread_ts") or event.get("ts", "")
|
||||
|
||||
if text.startswith("/"):
|
||||
msg_type = InboundMessageType.COMMAND
|
||||
else:
|
||||
msg_type = InboundMessageType.CHAT
|
||||
|
||||
# topic_id: use thread_ts as the topic identifier.
|
||||
# For threaded messages, thread_ts is the root message ts (shared topic).
|
||||
# For non-threaded messages, thread_ts is the message's own ts (new topic).
|
||||
inbound = self._make_inbound(
|
||||
chat_id=channel_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=msg_type,
|
||||
thread_ts=thread_ts,
|
||||
)
|
||||
inbound.topic_id = thread_ts
|
||||
|
||||
if self._loop and self._loop.is_running():
|
||||
# Acknowledge with an eyes reaction
|
||||
self._add_reaction(channel_id, event.get("ts", thread_ts), "eyes")
|
||||
# Send "running" reply first (fire-and-forget from SDK thread)
|
||||
self._send_running_reply(channel_id, thread_ts)
|
||||
asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._loop)
|
||||
153
deer-flow/backend/app/channels/store.py
Normal file
153
deer-flow/backend/app/channels/store.py
Normal file
@@ -0,0 +1,153 @@
|
||||
"""ChannelStore — persists IM chat-to-DeerFlow thread mappings."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ChannelStore:
|
||||
"""JSON-file-backed store that maps IM conversations to DeerFlow threads.
|
||||
|
||||
Data layout (on disk)::
|
||||
|
||||
{
|
||||
"<channel_name>:<chat_id>": {
|
||||
"thread_id": "<uuid>",
|
||||
"user_id": "<platform_user>",
|
||||
"created_at": 1700000000.0,
|
||||
"updated_at": 1700000000.0
|
||||
},
|
||||
...
|
||||
}
|
||||
|
||||
The store is intentionally simple — a single JSON file that is atomically
|
||||
rewritten on every mutation. For production workloads with high concurrency,
|
||||
this can be swapped for a proper database backend.
|
||||
"""
|
||||
|
||||
def __init__(self, path: str | Path | None = None) -> None:
|
||||
if path is None:
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
path = Path(get_paths().base_dir) / "channels" / "store.json"
|
||||
self._path = Path(path)
|
||||
self._path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self._data: dict[str, dict[str, Any]] = self._load()
|
||||
self._lock = threading.Lock()
|
||||
|
||||
# -- persistence -------------------------------------------------------
|
||||
|
||||
def _load(self) -> dict[str, dict[str, Any]]:
|
||||
if self._path.exists():
|
||||
try:
|
||||
return json.loads(self._path.read_text(encoding="utf-8"))
|
||||
except (json.JSONDecodeError, OSError):
|
||||
logger.warning("Corrupt channel store at %s, starting fresh", self._path)
|
||||
return {}
|
||||
|
||||
def _save(self) -> None:
|
||||
fd = tempfile.NamedTemporaryFile(
|
||||
mode="w",
|
||||
dir=self._path.parent,
|
||||
suffix=".tmp",
|
||||
delete=False,
|
||||
)
|
||||
try:
|
||||
json.dump(self._data, fd, indent=2)
|
||||
fd.close()
|
||||
Path(fd.name).replace(self._path)
|
||||
except BaseException:
|
||||
fd.close()
|
||||
Path(fd.name).unlink(missing_ok=True)
|
||||
raise
|
||||
|
||||
# -- key helpers -------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _key(channel_name: str, chat_id: str, topic_id: str | None = None) -> str:
|
||||
if topic_id:
|
||||
return f"{channel_name}:{chat_id}:{topic_id}"
|
||||
return f"{channel_name}:{chat_id}"
|
||||
|
||||
# -- public API --------------------------------------------------------
|
||||
|
||||
def get_thread_id(self, channel_name: str, chat_id: str, topic_id: str | None = None) -> str | None:
|
||||
"""Look up the DeerFlow thread_id for a given IM conversation/topic."""
|
||||
entry = self._data.get(self._key(channel_name, chat_id, topic_id))
|
||||
return entry["thread_id"] if entry else None
|
||||
|
||||
def set_thread_id(
|
||||
self,
|
||||
channel_name: str,
|
||||
chat_id: str,
|
||||
thread_id: str,
|
||||
*,
|
||||
topic_id: str | None = None,
|
||||
user_id: str = "",
|
||||
) -> None:
|
||||
"""Create or update the mapping for an IM conversation/topic."""
|
||||
with self._lock:
|
||||
key = self._key(channel_name, chat_id, topic_id)
|
||||
now = time.time()
|
||||
existing = self._data.get(key)
|
||||
self._data[key] = {
|
||||
"thread_id": thread_id,
|
||||
"user_id": user_id,
|
||||
"created_at": existing["created_at"] if existing else now,
|
||||
"updated_at": now,
|
||||
}
|
||||
self._save()
|
||||
|
||||
def remove(self, channel_name: str, chat_id: str, topic_id: str | None = None) -> bool:
|
||||
"""Remove a mapping.
|
||||
|
||||
If ``topic_id`` is provided, only that specific conversation/topic mapping is removed.
|
||||
If ``topic_id`` is omitted, all mappings whose key starts with
|
||||
``"<channel_name>:<chat_id>"`` (including topic-specific ones) are removed.
|
||||
|
||||
Returns True if at least one mapping was removed.
|
||||
"""
|
||||
with self._lock:
|
||||
# Remove a specific conversation/topic mapping.
|
||||
if topic_id is not None:
|
||||
key = self._key(channel_name, chat_id, topic_id)
|
||||
if key in self._data:
|
||||
del self._data[key]
|
||||
self._save()
|
||||
return True
|
||||
return False
|
||||
|
||||
# Remove all mappings for this channel/chat_id (base and any topic-specific keys).
|
||||
prefix = self._key(channel_name, chat_id)
|
||||
keys_to_delete = [k for k in self._data if k == prefix or k.startswith(prefix + ":")]
|
||||
if not keys_to_delete:
|
||||
return False
|
||||
|
||||
for k in keys_to_delete:
|
||||
del self._data[k]
|
||||
self._save()
|
||||
return True
|
||||
|
||||
def list_entries(self, channel_name: str | None = None) -> list[dict[str, Any]]:
|
||||
"""List all stored mappings, optionally filtered by channel."""
|
||||
results = []
|
||||
for key, entry in self._data.items():
|
||||
parts = key.split(":", 2)
|
||||
ch = parts[0]
|
||||
chat = parts[1] if len(parts) > 1 else ""
|
||||
topic = parts[2] if len(parts) > 2 else None
|
||||
if channel_name and ch != channel_name:
|
||||
continue
|
||||
item: dict[str, Any] = {"channel_name": ch, "chat_id": chat, **entry}
|
||||
if topic is not None:
|
||||
item["topic_id"] = topic
|
||||
results.append(item)
|
||||
return results
|
||||
317
deer-flow/backend/app/channels/telegram.py
Normal file
317
deer-flow/backend/app/channels/telegram.py
Normal file
@@ -0,0 +1,317 @@
|
||||
"""Telegram channel — connects via long-polling (no public IP needed)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TelegramChannel(Channel):
|
||||
"""Telegram bot channel using long-polling.
|
||||
|
||||
Configuration keys (in ``config.yaml`` under ``channels.telegram``):
|
||||
- ``bot_token``: Telegram Bot API token (from @BotFather).
|
||||
- ``allowed_users``: (optional) List of allowed Telegram user IDs. Empty = allow all.
|
||||
"""
|
||||
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="telegram", bus=bus, config=config)
|
||||
self._application = None
|
||||
self._thread: threading.Thread | None = None
|
||||
self._tg_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._main_loop: asyncio.AbstractEventLoop | None = None
|
||||
self._allowed_users: set[int] = set()
|
||||
for uid in config.get("allowed_users", []):
|
||||
try:
|
||||
self._allowed_users.add(int(uid))
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
# chat_id -> last sent message_id for threaded replies
|
||||
self._last_bot_message: dict[str, int] = {}
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
try:
|
||||
from telegram.ext import ApplicationBuilder, CommandHandler, MessageHandler, filters
|
||||
except ImportError:
|
||||
logger.error("python-telegram-bot is not installed. Install it with: uv add python-telegram-bot")
|
||||
return
|
||||
|
||||
bot_token = self.config.get("bot_token", "")
|
||||
if not bot_token:
|
||||
logger.error("Telegram channel requires bot_token")
|
||||
return
|
||||
|
||||
self._main_loop = asyncio.get_event_loop()
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
|
||||
# Build the application
|
||||
app = ApplicationBuilder().token(bot_token).build()
|
||||
|
||||
# Command handlers
|
||||
app.add_handler(CommandHandler("start", self._cmd_start))
|
||||
app.add_handler(CommandHandler("new", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("status", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("models", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("memory", self._cmd_generic))
|
||||
app.add_handler(CommandHandler("help", self._cmd_generic))
|
||||
|
||||
# General message handler
|
||||
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self._on_text))
|
||||
|
||||
self._application = app
|
||||
|
||||
# Run polling in a dedicated thread with its own event loop
|
||||
self._thread = threading.Thread(target=self._run_polling, daemon=True)
|
||||
self._thread.start()
|
||||
logger.info("Telegram channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._tg_loop and self._tg_loop.is_running():
|
||||
self._tg_loop.call_soon_threadsafe(self._tg_loop.stop)
|
||||
if self._thread:
|
||||
self._thread.join(timeout=10)
|
||||
self._thread = None
|
||||
self._application = None
|
||||
logger.info("Telegram channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._application:
|
||||
return
|
||||
|
||||
try:
|
||||
chat_id = int(msg.chat_id)
|
||||
except (ValueError, TypeError):
|
||||
logger.error("Invalid Telegram chat_id: %s", msg.chat_id)
|
||||
return
|
||||
|
||||
kwargs: dict[str, Any] = {"chat_id": chat_id, "text": msg.text}
|
||||
|
||||
# Reply to the last bot message in this chat for threading
|
||||
reply_to = self._last_bot_message.get(msg.chat_id)
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
|
||||
bot = self._application.bot
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
sent = await bot.send_message(**kwargs)
|
||||
self._last_bot_message[msg.chat_id] = sent.message_id
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
delay = 2**attempt # 1s, 2s
|
||||
logger.warning(
|
||||
"[Telegram] send failed (attempt %d/%d), retrying in %ds: %s",
|
||||
attempt + 1,
|
||||
_max_retries,
|
||||
delay,
|
||||
exc,
|
||||
)
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
logger.error("[Telegram] send failed after %d attempts: %s", _max_retries, last_exc)
|
||||
if last_exc is None:
|
||||
raise RuntimeError("Telegram send failed without an exception from any attempt")
|
||||
raise last_exc
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not self._application:
|
||||
return False
|
||||
|
||||
try:
|
||||
chat_id = int(msg.chat_id)
|
||||
except (ValueError, TypeError):
|
||||
logger.error("[Telegram] Invalid chat_id: %s", msg.chat_id)
|
||||
return False
|
||||
|
||||
# Telegram limits: 10MB for photos, 50MB for documents
|
||||
if attachment.size > 50 * 1024 * 1024:
|
||||
logger.warning("[Telegram] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
|
||||
return False
|
||||
|
||||
bot = self._application.bot
|
||||
reply_to = self._last_bot_message.get(msg.chat_id)
|
||||
|
||||
try:
|
||||
if attachment.is_image and attachment.size <= 10 * 1024 * 1024:
|
||||
with open(attachment.actual_path, "rb") as f:
|
||||
kwargs: dict[str, Any] = {"chat_id": chat_id, "photo": f}
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
sent = await bot.send_photo(**kwargs)
|
||||
else:
|
||||
from telegram import InputFile
|
||||
|
||||
with open(attachment.actual_path, "rb") as f:
|
||||
input_file = InputFile(f, filename=attachment.filename)
|
||||
kwargs = {"chat_id": chat_id, "document": input_file}
|
||||
if reply_to:
|
||||
kwargs["reply_to_message_id"] = reply_to
|
||||
sent = await bot.send_document(**kwargs)
|
||||
|
||||
self._last_bot_message[msg.chat_id] = sent.message_id
|
||||
logger.info("[Telegram] file sent: %s to chat=%s", attachment.filename, msg.chat_id)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[Telegram] failed to send file: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
# -- helpers -----------------------------------------------------------
|
||||
|
||||
async def _send_running_reply(self, chat_id: str, reply_to_message_id: int) -> None:
|
||||
"""Send a 'Working on it...' reply to the user's message."""
|
||||
if not self._application:
|
||||
return
|
||||
try:
|
||||
bot = self._application.bot
|
||||
await bot.send_message(
|
||||
chat_id=int(chat_id),
|
||||
text="Working on it...",
|
||||
reply_to_message_id=reply_to_message_id,
|
||||
)
|
||||
logger.info("[Telegram] 'Working on it...' reply sent in chat=%s", chat_id)
|
||||
except Exception:
|
||||
logger.exception("[Telegram] failed to send running reply in chat=%s", chat_id)
|
||||
|
||||
# -- internal ----------------------------------------------------------
|
||||
@staticmethod
|
||||
def _log_future_error(fut, name: str, msg_id: str):
|
||||
try:
|
||||
exc = fut.exception()
|
||||
if exc:
|
||||
logger.error("[Telegram] %s failed for msg_id=%s: %s", name, msg_id, exc)
|
||||
except Exception:
|
||||
logger.exception("[Telegram] Failed to inspect future for %s (msg_id=%s)", name, msg_id)
|
||||
|
||||
def _run_polling(self) -> None:
|
||||
"""Run telegram polling in a dedicated thread."""
|
||||
self._tg_loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(self._tg_loop)
|
||||
try:
|
||||
# Cannot use run_polling() because it calls add_signal_handler(),
|
||||
# which only works in the main thread. Instead, manually
|
||||
# initialize the application and start the updater.
|
||||
self._tg_loop.run_until_complete(self._application.initialize())
|
||||
self._tg_loop.run_until_complete(self._application.start())
|
||||
self._tg_loop.run_until_complete(self._application.updater.start_polling())
|
||||
self._tg_loop.run_forever()
|
||||
except Exception:
|
||||
if self._running:
|
||||
logger.exception("Telegram polling error")
|
||||
finally:
|
||||
# Graceful shutdown
|
||||
try:
|
||||
if self._application.updater.running:
|
||||
self._tg_loop.run_until_complete(self._application.updater.stop())
|
||||
self._tg_loop.run_until_complete(self._application.stop())
|
||||
self._tg_loop.run_until_complete(self._application.shutdown())
|
||||
except Exception:
|
||||
logger.exception("Error during Telegram shutdown")
|
||||
|
||||
def _check_user(self, user_id: int) -> bool:
|
||||
if not self._allowed_users:
|
||||
return True
|
||||
return user_id in self._allowed_users
|
||||
|
||||
async def _cmd_start(self, update, context) -> None:
|
||||
"""Handle /start command."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
await update.message.reply_text("Welcome to DeerFlow! Send me a message to start a conversation.\nType /help for available commands.")
|
||||
|
||||
async def _process_incoming_with_reply(self, chat_id: str, msg_id: int, inbound: InboundMessage) -> None:
|
||||
await self._send_running_reply(chat_id, msg_id)
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
async def _cmd_generic(self, update, context) -> None:
|
||||
"""Forward slash commands to the channel manager."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
|
||||
text = update.message.text
|
||||
chat_id = str(update.effective_chat.id)
|
||||
user_id = str(update.effective_user.id)
|
||||
msg_id = str(update.message.message_id)
|
||||
|
||||
# Use the same topic_id logic as _on_text so that commands
|
||||
# like /new target the correct thread mapping.
|
||||
if update.effective_chat.type == "private":
|
||||
topic_id = None
|
||||
else:
|
||||
reply_to = update.message.reply_to_message
|
||||
if reply_to:
|
||||
topic_id = str(reply_to.message_id)
|
||||
else:
|
||||
topic_id = msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=InboundMessageType.COMMAND,
|
||||
thread_ts=msg_id,
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
fut = asyncio.run_coroutine_threadsafe(self._process_incoming_with_reply(chat_id, update.message.message_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f: self._log_future_error(f, "process_incoming_with_reply", update.message.message_id))
|
||||
else:
|
||||
logger.warning("[Telegram] Main loop not running. Cannot publish inbound message.")
|
||||
|
||||
async def _on_text(self, update, context) -> None:
|
||||
"""Handle regular text messages."""
|
||||
if not self._check_user(update.effective_user.id):
|
||||
return
|
||||
|
||||
text = update.message.text.strip()
|
||||
if not text:
|
||||
return
|
||||
|
||||
chat_id = str(update.effective_chat.id)
|
||||
user_id = str(update.effective_user.id)
|
||||
msg_id = str(update.message.message_id)
|
||||
|
||||
# topic_id determines which DeerFlow thread the message maps to.
|
||||
# In private chats, use None so that all messages share a single
|
||||
# thread (the store key becomes "channel:chat_id").
|
||||
# In group chats, use the reply-to message id or the current
|
||||
# message id to keep separate conversation threads.
|
||||
if update.effective_chat.type == "private":
|
||||
topic_id = None
|
||||
else:
|
||||
reply_to = update.message.reply_to_message
|
||||
if reply_to:
|
||||
topic_id = str(reply_to.message_id)
|
||||
else:
|
||||
topic_id = msg_id
|
||||
|
||||
inbound = self._make_inbound(
|
||||
chat_id=chat_id,
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=InboundMessageType.CHAT,
|
||||
thread_ts=msg_id,
|
||||
)
|
||||
inbound.topic_id = topic_id
|
||||
|
||||
if self._main_loop and self._main_loop.is_running():
|
||||
fut = asyncio.run_coroutine_threadsafe(self._process_incoming_with_reply(chat_id, update.message.message_id, inbound), self._main_loop)
|
||||
fut.add_done_callback(lambda f: self._log_future_error(f, "process_incoming_with_reply", update.message.message_id))
|
||||
else:
|
||||
logger.warning("[Telegram] Main loop not running. Cannot publish inbound message.")
|
||||
1370
deer-flow/backend/app/channels/wechat.py
Normal file
1370
deer-flow/backend/app/channels/wechat.py
Normal file
File diff suppressed because it is too large
Load Diff
394
deer-flow/backend/app/channels/wecom.py
Normal file
394
deer-flow/backend/app/channels/wecom.py
Normal file
@@ -0,0 +1,394 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import hashlib
|
||||
import logging
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import Any, cast
|
||||
|
||||
from app.channels.base import Channel
|
||||
from app.channels.message_bus import (
|
||||
InboundMessageType,
|
||||
MessageBus,
|
||||
OutboundMessage,
|
||||
ResolvedAttachment,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class WeComChannel(Channel):
|
||||
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
|
||||
super().__init__(name="wecom", bus=bus, config=config)
|
||||
self._bot_id: str | None = None
|
||||
self._bot_secret: str | None = None
|
||||
self._ws_client = None
|
||||
self._ws_task: asyncio.Task | None = None
|
||||
self._ws_frames: dict[str, dict[str, Any]] = {}
|
||||
self._ws_stream_ids: dict[str, str] = {}
|
||||
self._working_message = "Working on it..."
|
||||
|
||||
def _clear_ws_context(self, thread_ts: str | None) -> None:
|
||||
if not thread_ts:
|
||||
return
|
||||
self._ws_frames.pop(thread_ts, None)
|
||||
self._ws_stream_ids.pop(thread_ts, None)
|
||||
|
||||
async def _send_ws_upload_command(self, req_id: str, body: dict[str, Any], cmd: str) -> dict[str, Any]:
|
||||
if not self._ws_client:
|
||||
raise RuntimeError("WeCom WebSocket client is not available")
|
||||
|
||||
ws_manager = getattr(self._ws_client, "_ws_manager", None)
|
||||
send_reply = getattr(ws_manager, "send_reply", None)
|
||||
if not callable(send_reply):
|
||||
raise RuntimeError("Installed wecom-aibot-python-sdk does not expose the WebSocket media upload API expected by DeerFlow. Use wecom-aibot-python-sdk==0.1.6 or update the adapter.")
|
||||
|
||||
send_reply_async = cast(Callable[[str, dict[str, Any], str], Awaitable[dict[str, Any]]], send_reply)
|
||||
return await send_reply_async(req_id, body, cmd)
|
||||
|
||||
async def start(self) -> None:
|
||||
if self._running:
|
||||
return
|
||||
|
||||
bot_id = self.config.get("bot_id")
|
||||
bot_secret = self.config.get("bot_secret")
|
||||
working_message = self.config.get("working_message")
|
||||
|
||||
self._bot_id = bot_id if isinstance(bot_id, str) and bot_id else None
|
||||
self._bot_secret = bot_secret if isinstance(bot_secret, str) and bot_secret else None
|
||||
self._working_message = working_message if isinstance(working_message, str) and working_message else "Working on it..."
|
||||
|
||||
if not self._bot_id or not self._bot_secret:
|
||||
logger.error("WeCom channel requires bot_id and bot_secret")
|
||||
return
|
||||
|
||||
try:
|
||||
from aibot import WSClient, WSClientOptions
|
||||
except ImportError:
|
||||
logger.error("wecom-aibot-python-sdk is not installed. Install it with: uv add wecom-aibot-python-sdk")
|
||||
return
|
||||
else:
|
||||
self._ws_client = WSClient(WSClientOptions(bot_id=self._bot_id, secret=self._bot_secret, logger=logger))
|
||||
self._ws_client.on("message.text", self._on_ws_text)
|
||||
self._ws_client.on("message.mixed", self._on_ws_mixed)
|
||||
self._ws_client.on("message.image", self._on_ws_image)
|
||||
self._ws_client.on("message.file", self._on_ws_file)
|
||||
self._ws_task = asyncio.create_task(self._ws_client.connect())
|
||||
|
||||
self._running = True
|
||||
self.bus.subscribe_outbound(self._on_outbound)
|
||||
logger.info("WeCom channel started")
|
||||
|
||||
async def stop(self) -> None:
|
||||
self._running = False
|
||||
self.bus.unsubscribe_outbound(self._on_outbound)
|
||||
if self._ws_task:
|
||||
try:
|
||||
self._ws_task.cancel()
|
||||
except Exception:
|
||||
pass
|
||||
self._ws_task = None
|
||||
if self._ws_client:
|
||||
try:
|
||||
self._ws_client.disconnect()
|
||||
except Exception:
|
||||
pass
|
||||
self._ws_client = None
|
||||
self._ws_frames.clear()
|
||||
self._ws_stream_ids.clear()
|
||||
logger.info("WeCom channel stopped")
|
||||
|
||||
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if self._ws_client:
|
||||
await self._send_ws(msg, _max_retries=_max_retries)
|
||||
return
|
||||
logger.warning("[WeCom] send called but WebSocket client is not available")
|
||||
|
||||
async def _on_outbound(self, msg: OutboundMessage) -> None:
|
||||
if msg.channel_name != self.name:
|
||||
return
|
||||
|
||||
try:
|
||||
await self.send(msg)
|
||||
except Exception:
|
||||
logger.exception("Failed to send outbound message on channel %s", self.name)
|
||||
if msg.is_final:
|
||||
self._clear_ws_context(msg.thread_ts)
|
||||
return
|
||||
|
||||
for attachment in msg.attachments:
|
||||
try:
|
||||
success = await self.send_file(msg, attachment)
|
||||
if not success:
|
||||
logger.warning("[%s] file upload skipped for %s", self.name, attachment.filename)
|
||||
except Exception:
|
||||
logger.exception("[%s] failed to upload file %s", self.name, attachment.filename)
|
||||
|
||||
if msg.is_final:
|
||||
self._clear_ws_context(msg.thread_ts)
|
||||
|
||||
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
|
||||
if not msg.is_final:
|
||||
return True
|
||||
if not self._ws_client:
|
||||
return False
|
||||
if not msg.thread_ts:
|
||||
return False
|
||||
frame = self._ws_frames.get(msg.thread_ts)
|
||||
if not frame:
|
||||
return False
|
||||
|
||||
media_type = "image" if attachment.is_image else "file"
|
||||
size_limit = 2 * 1024 * 1024 if attachment.is_image else 20 * 1024 * 1024
|
||||
if attachment.size > size_limit:
|
||||
logger.warning(
|
||||
"[WeCom] %s too large (%d bytes), skipping: %s",
|
||||
media_type,
|
||||
attachment.size,
|
||||
attachment.filename,
|
||||
)
|
||||
return False
|
||||
|
||||
try:
|
||||
media_id = await self._upload_media_ws(
|
||||
media_type=media_type,
|
||||
filename=attachment.filename,
|
||||
path=str(attachment.actual_path),
|
||||
size=attachment.size,
|
||||
)
|
||||
if not media_id:
|
||||
return False
|
||||
|
||||
body = {media_type: {"media_id": media_id}, "msgtype": media_type}
|
||||
await self._ws_client.reply(frame, body)
|
||||
logger.debug("[WeCom] %s sent via ws: %s", media_type, attachment.filename)
|
||||
return True
|
||||
except Exception:
|
||||
logger.exception("[WeCom] failed to upload/send file via ws: %s", attachment.filename)
|
||||
return False
|
||||
|
||||
async def _on_ws_text(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
text = ((body.get("text") or {}).get("content") or "").strip()
|
||||
quote = body.get("quote", {}).get("text", {}).get("content", "").strip()
|
||||
if not text and not quote:
|
||||
return
|
||||
await self._publish_ws_inbound(frame, text + (f"\nQuote message: {quote}" if quote else ""))
|
||||
|
||||
async def _on_ws_mixed(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
mixed = body.get("mixed") or {}
|
||||
items = mixed.get("msg_item") or []
|
||||
parts: list[str] = []
|
||||
files: list[dict[str, Any]] = []
|
||||
for item in items:
|
||||
item_type = (item or {}).get("msgtype")
|
||||
if item_type == "text":
|
||||
content = (((item or {}).get("text") or {}).get("content") or "").strip()
|
||||
if content:
|
||||
parts.append(content)
|
||||
elif item_type in ("image", "file"):
|
||||
payload = (item or {}).get(item_type) or {}
|
||||
url = payload.get("url")
|
||||
aeskey = payload.get("aeskey")
|
||||
if isinstance(url, str) and url:
|
||||
files.append(
|
||||
{
|
||||
"type": item_type,
|
||||
"url": url,
|
||||
"aeskey": (aeskey if isinstance(aeskey, str) and aeskey else None),
|
||||
}
|
||||
)
|
||||
text = "\n\n".join(parts).strip()
|
||||
if not text and not files:
|
||||
return
|
||||
if not text:
|
||||
text = "（receive image/file）"
|
||||
await self._publish_ws_inbound(frame, text, files=files)
|
||||
|
||||
async def _on_ws_image(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
image = body.get("image") or {}
|
||||
url = image.get("url")
|
||||
aeskey = image.get("aeskey")
|
||||
if not isinstance(url, str) or not url:
|
||||
return
|
||||
await self._publish_ws_inbound(
|
||||
frame,
|
||||
"（receive image ）",
|
||||
files=[
|
||||
{
|
||||
"type": "image",
|
||||
"url": url,
|
||||
"aeskey": aeskey if isinstance(aeskey, str) and aeskey else None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
async def _on_ws_file(self, frame: dict[str, Any]) -> None:
|
||||
body = frame.get("body", {}) or {}
|
||||
file_obj = body.get("file") or {}
|
||||
url = file_obj.get("url")
|
||||
aeskey = file_obj.get("aeskey")
|
||||
if not isinstance(url, str) or not url:
|
||||
return
|
||||
await self._publish_ws_inbound(
|
||||
frame,
|
||||
"（receive file）",
|
||||
files=[
|
||||
{
|
||||
"type": "file",
|
||||
"url": url,
|
||||
"aeskey": aeskey if isinstance(aeskey, str) and aeskey else None,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
async def _publish_ws_inbound(
|
||||
self,
|
||||
frame: dict[str, Any],
|
||||
text: str,
|
||||
*,
|
||||
files: list[dict[str, Any]] | None = None,
|
||||
) -> None:
|
||||
if not self._ws_client:
|
||||
return
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
return
|
||||
|
||||
body = frame.get("body", {}) or {}
|
||||
msg_id = body.get("msgid")
|
||||
if not msg_id:
|
||||
return
|
||||
|
||||
user_id = (body.get("from") or {}).get("userid")
|
||||
|
||||
inbound_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
|
||||
inbound = self._make_inbound(
|
||||
chat_id=user_id, # keep user's conversation in memory
|
||||
user_id=user_id,
|
||||
text=text,
|
||||
msg_type=inbound_type,
|
||||
thread_ts=msg_id,
|
||||
files=files or [],
|
||||
metadata={"aibotid": body.get("aibotid"), "chattype": body.get("chattype")},
|
||||
)
|
||||
inbound.topic_id = user_id # keep the same thread
|
||||
|
||||
stream_id = generate_req_id("stream")
|
||||
self._ws_frames[msg_id] = frame
|
||||
self._ws_stream_ids[msg_id] = stream_id
|
||||
|
||||
try:
|
||||
await self._ws_client.reply_stream(frame, stream_id, self._working_message, False)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
await self.bus.publish_inbound(inbound)
|
||||
|
||||
async def _send_ws(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
|
||||
if not self._ws_client:
|
||||
return
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
generate_req_id = None
|
||||
|
||||
if msg.thread_ts and msg.thread_ts in self._ws_frames:
|
||||
frame = self._ws_frames[msg.thread_ts]
|
||||
stream_id = self._ws_stream_ids.get(msg.thread_ts)
|
||||
if not stream_id and generate_req_id:
|
||||
stream_id = generate_req_id("stream")
|
||||
self._ws_stream_ids[msg.thread_ts] = stream_id
|
||||
if not stream_id:
|
||||
return
|
||||
|
||||
last_exc: Exception | None = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._ws_client.reply_stream(frame, stream_id, msg.text, bool(msg.is_final))
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
await asyncio.sleep(2**attempt)
|
||||
if last_exc:
|
||||
raise last_exc
|
||||
|
||||
body = {"msgtype": "markdown", "markdown": {"content": msg.text}}
|
||||
last_exc = None
|
||||
for attempt in range(_max_retries):
|
||||
try:
|
||||
await self._ws_client.send_message(msg.chat_id, body)
|
||||
return
|
||||
except Exception as exc:
|
||||
last_exc = exc
|
||||
if attempt < _max_retries - 1:
|
||||
await asyncio.sleep(2**attempt)
|
||||
if last_exc:
|
||||
raise last_exc
|
||||
|
||||
async def _upload_media_ws(
|
||||
self,
|
||||
*,
|
||||
media_type: str,
|
||||
filename: str,
|
||||
path: str,
|
||||
size: int,
|
||||
) -> str | None:
|
||||
if not self._ws_client:
|
||||
return None
|
||||
try:
|
||||
from aibot import generate_req_id
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
chunk_size = 512 * 1024
|
||||
total_chunks = (size + chunk_size - 1) // chunk_size
|
||||
if total_chunks < 1 or total_chunks > 100:
|
||||
logger.warning("[WeCom] invalid total_chunks=%d for %s", total_chunks, filename)
|
||||
return None
|
||||
|
||||
md5_hasher = hashlib.md5()
|
||||
with open(path, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(1024 * 1024), b""):
|
||||
md5_hasher.update(chunk)
|
||||
md5 = md5_hasher.hexdigest()
|
||||
|
||||
init_req_id = generate_req_id("aibot_upload_media_init")
|
||||
init_body = {
|
||||
"type": media_type,
|
||||
"filename": filename,
|
||||
"total_size": int(size),
|
||||
"total_chunks": int(total_chunks),
|
||||
"md5": md5,
|
||||
}
|
||||
init_ack = await self._send_ws_upload_command(init_req_id, init_body, "aibot_upload_media_init")
|
||||
upload_id = (init_ack.get("body") or {}).get("upload_id")
|
||||
if not upload_id:
|
||||
logger.warning("[WeCom] upload init returned no upload_id: %s", init_ack)
|
||||
return None
|
||||
|
||||
with open(path, "rb") as f:
|
||||
for idx in range(total_chunks):
|
||||
data = f.read(chunk_size)
|
||||
if not data:
|
||||
break
|
||||
chunk_req_id = generate_req_id("aibot_upload_media_chunk")
|
||||
chunk_body = {
|
||||
"upload_id": upload_id,
|
||||
"chunk_index": int(idx),
|
||||
"base64_data": base64.b64encode(data).decode("utf-8"),
|
||||
}
|
||||
await self._send_ws_upload_command(chunk_req_id, chunk_body, "aibot_upload_media_chunk")
|
||||
|
||||
finish_req_id = generate_req_id("aibot_upload_media_finish")
|
||||
finish_ack = await self._send_ws_upload_command(finish_req_id, {"upload_id": upload_id}, "aibot_upload_media_finish")
|
||||
media_id = (finish_ack.get("body") or {}).get("media_id")
|
||||
if not media_id:
|
||||
logger.warning("[WeCom] upload finish returned no media_id: %s", finish_ack)
|
||||
return None
|
||||
return media_id
|
||||
4
deer-flow/backend/app/gateway/__init__.py
Normal file
4
deer-flow/backend/app/gateway/__init__.py
Normal file
@@ -0,0 +1,4 @@
|
||||
from .app import app, create_app
|
||||
from .config import GatewayConfig, get_gateway_config
|
||||
|
||||
__all__ = ["app", "create_app", "GatewayConfig", "get_gateway_config"]
|
||||
221
deer-flow/backend/app/gateway/app.py
Normal file
221
deer-flow/backend/app/gateway/app.py
Normal file
@@ -0,0 +1,221 @@
|
||||
import logging
|
||||
from collections.abc import AsyncGenerator
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI
|
||||
|
||||
from app.gateway.config import get_gateway_config
|
||||
from app.gateway.deps import langgraph_runtime
|
||||
from app.gateway.routers import (
|
||||
agents,
|
||||
artifacts,
|
||||
assistants_compat,
|
||||
channels,
|
||||
mcp,
|
||||
memory,
|
||||
models,
|
||||
runs,
|
||||
skills,
|
||||
suggestions,
|
||||
thread_runs,
|
||||
threads,
|
||||
uploads,
|
||||
)
|
||||
from deerflow.config.app_config import get_app_config
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
"""Application lifespan handler."""
|
||||
|
||||
# Load config and check necessary environment variables at startup
|
||||
try:
|
||||
get_app_config()
|
||||
logger.info("Configuration loaded successfully")
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to load configuration during gateway startup: {e}"
|
||||
logger.exception(error_msg)
|
||||
raise RuntimeError(error_msg) from e
|
||||
config = get_gateway_config()
|
||||
logger.info(f"Starting API Gateway on {config.host}:{config.port}")
|
||||
|
||||
# Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
|
||||
async with langgraph_runtime(app):
|
||||
logger.info("LangGraph runtime initialised")
|
||||
|
||||
# Start IM channel service if any channels are configured
|
||||
try:
|
||||
from app.channels.service import start_channel_service
|
||||
|
||||
channel_service = await start_channel_service()
|
||||
logger.info("Channel service started: %s", channel_service.get_status())
|
||||
except Exception:
|
||||
logger.exception("No IM channels configured or channel service failed to start")
|
||||
|
||||
yield
|
||||
|
||||
# Stop channel service on shutdown
|
||||
try:
|
||||
from app.channels.service import stop_channel_service
|
||||
|
||||
await stop_channel_service()
|
||||
except Exception:
|
||||
logger.exception("Failed to stop channel service")
|
||||
|
||||
logger.info("Shutting down API Gateway")
|
||||
|
||||
|
||||
def create_app() -> FastAPI:
|
||||
"""Create and configure the FastAPI application.
|
||||
|
||||
Returns:
|
||||
Configured FastAPI application instance.
|
||||
"""
|
||||
|
||||
app = FastAPI(
|
||||
title="DeerFlow API Gateway",
|
||||
description="""
|
||||
## DeerFlow API Gateway
|
||||
|
||||
API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execution capabilities.
|
||||
|
||||
### Features
|
||||
|
||||
- **Models Management**: Query and retrieve available AI models
|
||||
- **MCP Configuration**: Manage Model Context Protocol (MCP) server configurations
|
||||
- **Memory Management**: Access and manage global memory data for personalized conversations
|
||||
- **Skills Management**: Query and manage skills and their enabled status
|
||||
- **Artifacts**: Access thread artifacts and generated files
|
||||
- **Health Monitoring**: System health check endpoints
|
||||
|
||||
### Architecture
|
||||
|
||||
LangGraph requests are handled by nginx reverse proxy.
|
||||
This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
|
||||
""",
|
||||
version="0.1.0",
|
||||
lifespan=lifespan,
|
||||
docs_url="/docs",
|
||||
redoc_url="/redoc",
|
||||
openapi_url="/openapi.json",
|
||||
openapi_tags=[
|
||||
{
|
||||
"name": "models",
|
||||
"description": "Operations for querying available AI models and their configurations",
|
||||
},
|
||||
{
|
||||
"name": "mcp",
|
||||
"description": "Manage Model Context Protocol (MCP) server configurations",
|
||||
},
|
||||
{
|
||||
"name": "memory",
|
||||
"description": "Access and manage global memory data for personalized conversations",
|
||||
},
|
||||
{
|
||||
"name": "skills",
|
||||
"description": "Manage skills and their configurations",
|
||||
},
|
||||
{
|
||||
"name": "artifacts",
|
||||
"description": "Access and download thread artifacts and generated files",
|
||||
},
|
||||
{
|
||||
"name": "uploads",
|
||||
"description": "Upload and manage user files for threads",
|
||||
},
|
||||
{
|
||||
"name": "threads",
|
||||
"description": "Manage DeerFlow thread-local filesystem data",
|
||||
},
|
||||
{
|
||||
"name": "agents",
|
||||
"description": "Create and manage custom agents with per-agent config and prompts",
|
||||
},
|
||||
{
|
||||
"name": "suggestions",
|
||||
"description": "Generate follow-up question suggestions for conversations",
|
||||
},
|
||||
{
|
||||
"name": "channels",
|
||||
"description": "Manage IM channel integrations (Feishu, Slack, Telegram)",
|
||||
},
|
||||
{
|
||||
"name": "assistants-compat",
|
||||
"description": "LangGraph Platform-compatible assistants API (stub)",
|
||||
},
|
||||
{
|
||||
"name": "runs",
|
||||
"description": "LangGraph Platform-compatible runs lifecycle (create, stream, cancel)",
|
||||
},
|
||||
{
|
||||
"name": "health",
|
||||
"description": "Health check and system status endpoints",
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
# CORS is handled by nginx - no need for FastAPI middleware
|
||||
|
||||
# Include routers
|
||||
# Models API is mounted at /api/models
|
||||
app.include_router(models.router)
|
||||
|
||||
# MCP API is mounted at /api/mcp
|
||||
app.include_router(mcp.router)
|
||||
|
||||
# Memory API is mounted at /api/memory
|
||||
app.include_router(memory.router)
|
||||
|
||||
# Skills API is mounted at /api/skills
|
||||
app.include_router(skills.router)
|
||||
|
||||
# Artifacts API is mounted at /api/threads/{thread_id}/artifacts
|
||||
app.include_router(artifacts.router)
|
||||
|
||||
# Uploads API is mounted at /api/threads/{thread_id}/uploads
|
||||
app.include_router(uploads.router)
|
||||
|
||||
# Thread cleanup API is mounted at /api/threads/{thread_id}
|
||||
app.include_router(threads.router)
|
||||
|
||||
# Agents API is mounted at /api/agents
|
||||
app.include_router(agents.router)
|
||||
|
||||
# Suggestions API is mounted at /api/threads/{thread_id}/suggestions
|
||||
app.include_router(suggestions.router)
|
||||
|
||||
# Channels API is mounted at /api/channels
|
||||
app.include_router(channels.router)
|
||||
|
||||
# Assistants compatibility API (LangGraph Platform stub)
|
||||
app.include_router(assistants_compat.router)
|
||||
|
||||
# Thread Runs API (LangGraph Platform-compatible runs lifecycle)
|
||||
app.include_router(thread_runs.router)
|
||||
|
||||
# Stateless Runs API (stream/wait without a pre-existing thread)
|
||||
app.include_router(runs.router)
|
||||
|
||||
@app.get("/health", tags=["health"])
|
||||
async def health_check() -> dict:
|
||||
"""Health check endpoint.
|
||||
|
||||
Returns:
|
||||
Service health status information.
|
||||
"""
|
||||
return {"status": "healthy", "service": "deer-flow-gateway"}
|
||||
|
||||
return app
|
||||
|
||||
|
||||
# Create app instance for uvicorn
|
||||
app = create_app()
|
||||
27
deer-flow/backend/app/gateway/config.py
Normal file
27
deer-flow/backend/app/gateway/config.py
Normal file
@@ -0,0 +1,27 @@
|
||||
import os
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class GatewayConfig(BaseModel):
|
||||
"""Configuration for the API Gateway."""
|
||||
|
||||
host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
|
||||
port: int = Field(default=8001, description="Port to bind the gateway server")
|
||||
cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
|
||||
|
||||
|
||||
_gateway_config: GatewayConfig | None = None
|
||||
|
||||
|
||||
def get_gateway_config() -> GatewayConfig:
|
||||
"""Get gateway config, loading from environment if available."""
|
||||
global _gateway_config
|
||||
if _gateway_config is None:
|
||||
cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
|
||||
_gateway_config = GatewayConfig(
|
||||
host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
|
||||
port=int(os.getenv("GATEWAY_PORT", "8001")),
|
||||
cors_origins=cors_origins_str.split(","),
|
||||
)
|
||||
return _gateway_config
|
||||
70
deer-flow/backend/app/gateway/deps.py
Normal file
70
deer-flow/backend/app/gateway/deps.py
Normal file
@@ -0,0 +1,70 @@
|
||||
"""Centralized accessors for singleton objects stored on ``app.state``.
|
||||
|
||||
**Getters** (used by routers): raise 503 when a required dependency is
|
||||
missing, except ``get_store`` which returns ``None``.
|
||||
|
||||
Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import AsyncGenerator
|
||||
from contextlib import AsyncExitStack, asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Request
|
||||
|
||||
from deerflow.runtime import RunManager, StreamBridge
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
"""Bootstrap and tear down all LangGraph runtime singletons.
|
||||
|
||||
Usage in ``app.py``::
|
||||
|
||||
async with langgraph_runtime(app):
|
||||
yield
|
||||
"""
|
||||
from deerflow.agents.checkpointer.async_provider import make_checkpointer
|
||||
from deerflow.runtime import make_store, make_stream_bridge
|
||||
|
||||
async with AsyncExitStack() as stack:
|
||||
app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
|
||||
app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
|
||||
app.state.store = await stack.enter_async_context(make_store())
|
||||
app.state.run_manager = RunManager()
|
||||
yield
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Getters – called by routers per-request
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def get_stream_bridge(request: Request) -> StreamBridge:
|
||||
"""Return the global :class:`StreamBridge`, or 503."""
|
||||
bridge = getattr(request.app.state, "stream_bridge", None)
|
||||
if bridge is None:
|
||||
raise HTTPException(status_code=503, detail="Stream bridge not available")
|
||||
return bridge
|
||||
|
||||
|
||||
def get_run_manager(request: Request) -> RunManager:
|
||||
"""Return the global :class:`RunManager`, or 503."""
|
||||
mgr = getattr(request.app.state, "run_manager", None)
|
||||
if mgr is None:
|
||||
raise HTTPException(status_code=503, detail="Run manager not available")
|
||||
return mgr
|
||||
|
||||
|
||||
def get_checkpointer(request: Request):
|
||||
"""Return the global checkpointer, or 503."""
|
||||
cp = getattr(request.app.state, "checkpointer", None)
|
||||
if cp is None:
|
||||
raise HTTPException(status_code=503, detail="Checkpointer not available")
|
||||
return cp
|
||||
|
||||
|
||||
def get_store(request: Request):
|
||||
"""Return the global store (may be ``None`` if not configured)."""
|
||||
return getattr(request.app.state, "store", None)
|
||||
28
deer-flow/backend/app/gateway/path_utils.py
Normal file
28
deer-flow/backend/app/gateway/path_utils.py
Normal file
@@ -0,0 +1,28 @@
|
||||
"""Shared path resolution for thread virtual paths (e.g. mnt/user-data/outputs/...)."""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import HTTPException
|
||||
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
|
||||
def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
|
||||
"""Resolve a virtual path to the actual filesystem path under thread user-data.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
virtual_path: The virtual path as seen inside the sandbox
|
||||
(e.g., /mnt/user-data/outputs/file.txt).
|
||||
|
||||
Returns:
|
||||
The resolved filesystem path.
|
||||
|
||||
Raises:
|
||||
HTTPException: If the path is invalid or outside allowed directories.
|
||||
"""
|
||||
try:
|
||||
return get_paths().resolve_virtual_path(thread_id, virtual_path)
|
||||
except ValueError as e:
|
||||
status = 403 if "traversal" in str(e) else 400
|
||||
raise HTTPException(status_code=status, detail=str(e))
|
||||
3
deer-flow/backend/app/gateway/routers/__init__.py
Normal file
3
deer-flow/backend/app/gateway/routers/__init__.py
Normal file
@@ -0,0 +1,3 @@
|
||||
from . import artifacts, assistants_compat, mcp, models, skills, suggestions, thread_runs, threads, uploads
|
||||
|
||||
__all__ = ["artifacts", "assistants_compat", "mcp", "models", "skills", "suggestions", "threads", "thread_runs", "uploads"]
|
||||
383
deer-flow/backend/app/gateway/routers/agents.py
Normal file
383
deer-flow/backend/app/gateway/routers/agents.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""CRUD API for custom agents."""
|
||||
|
||||
import logging
|
||||
import re
|
||||
import shutil
|
||||
|
||||
import yaml
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api", tags=["agents"])
|
||||
|
||||
AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
|
||||
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
"""Response model for a custom agent."""
|
||||
|
||||
name: str = Field(..., description="Agent name (hyphen-case)")
|
||||
description: str = Field(default="", description="Agent description")
|
||||
model: str | None = Field(default=None, description="Optional model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
|
||||
soul: str | None = Field(default=None, description="SOUL.md content")
|
||||
|
||||
|
||||
class AgentsListResponse(BaseModel):
|
||||
"""Response model for listing all custom agents."""
|
||||
|
||||
agents: list[AgentResponse]
|
||||
|
||||
|
||||
class AgentCreateRequest(BaseModel):
|
||||
"""Request body for creating a custom agent."""
|
||||
|
||||
name: str = Field(..., description="Agent name (must match ^[A-Za-z0-9-]+$, stored as lowercase)")
|
||||
description: str = Field(default="", description="Agent description")
|
||||
model: str | None = Field(default=None, description="Optional model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
|
||||
soul: str = Field(default="", description="SOUL.md content — agent personality and behavioral guardrails")
|
||||
|
||||
|
||||
class AgentUpdateRequest(BaseModel):
|
||||
"""Request body for updating a custom agent."""
|
||||
|
||||
description: str | None = Field(default=None, description="Updated description")
|
||||
model: str | None = Field(default=None, description="Updated model override")
|
||||
tool_groups: list[str] | None = Field(default=None, description="Updated tool group whitelist")
|
||||
soul: str | None = Field(default=None, description="Updated SOUL.md content")
|
||||
|
||||
|
||||
def _validate_agent_name(name: str) -> None:
|
||||
"""Validate agent name against allowed pattern.
|
||||
|
||||
Args:
|
||||
name: The agent name to validate.
|
||||
|
||||
Raises:
|
||||
HTTPException: 422 if the name is invalid.
|
||||
"""
|
||||
if not AGENT_NAME_PATTERN.match(name):
|
||||
raise HTTPException(
|
||||
status_code=422,
|
||||
detail=f"Invalid agent name '{name}'. Must match ^[A-Za-z0-9-]+$ (letters, digits, and hyphens only).",
|
||||
)
|
||||
|
||||
|
||||
def _normalize_agent_name(name: str) -> str:
|
||||
"""Normalize agent name to lowercase for filesystem storage."""
|
||||
return name.lower()
|
||||
|
||||
|
||||
def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
|
||||
"""Convert AgentConfig to AgentResponse."""
|
||||
soul: str | None = None
|
||||
if include_soul:
|
||||
soul = load_agent_soul(agent_cfg.name) or ""
|
||||
|
||||
return AgentResponse(
|
||||
name=agent_cfg.name,
|
||||
description=agent_cfg.description,
|
||||
model=agent_cfg.model,
|
||||
tool_groups=agent_cfg.tool_groups,
|
||||
soul=soul,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents",
|
||||
response_model=AgentsListResponse,
|
||||
summary="List Custom Agents",
|
||||
description="List all custom agents available in the agents directory, including their soul content.",
|
||||
)
|
||||
async def list_agents() -> AgentsListResponse:
|
||||
"""List all custom agents.
|
||||
|
||||
Returns:
|
||||
List of all custom agents with their metadata and soul content.
|
||||
"""
|
||||
try:
|
||||
agents = list_custom_agents()
|
||||
return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to list agents: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list agents: {str(e)}")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents/check",
|
||||
summary="Check Agent Name",
|
||||
description="Validate an agent name and check if it is available (case-insensitive).",
|
||||
)
|
||||
async def check_agent_name(name: str) -> dict:
|
||||
"""Check whether an agent name is valid and not yet taken.
|
||||
|
||||
Args:
|
||||
name: The agent name to check.
|
||||
|
||||
Returns:
|
||||
``{"available": true/false, "name": "<normalized>"}``
|
||||
|
||||
Raises:
|
||||
HTTPException: 422 if the name is invalid.
|
||||
"""
|
||||
_validate_agent_name(name)
|
||||
normalized = _normalize_agent_name(name)
|
||||
available = not get_paths().agent_dir(normalized).exists()
|
||||
return {"available": available, "name": normalized}
|
||||
|
||||
|
||||
@router.get(
|
||||
"/agents/{name}",
|
||||
response_model=AgentResponse,
|
||||
summary="Get Custom Agent",
|
||||
description="Retrieve details and SOUL.md content for a specific custom agent.",
|
||||
)
|
||||
async def get_agent(name: str) -> AgentResponse:
|
||||
"""Get a specific custom agent by name.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
|
||||
Returns:
|
||||
Agent details including SOUL.md content.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
try:
|
||||
agent_cfg = load_agent_config(name)
|
||||
return _agent_config_to_response(agent_cfg, include_soul=True)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get agent: {str(e)}")
|
||||
|
||||
|
||||
@router.post(
|
||||
"/agents",
|
||||
response_model=AgentResponse,
|
||||
status_code=201,
|
||||
summary="Create Custom Agent",
|
||||
description="Create a new custom agent with its config and SOUL.md.",
|
||||
)
|
||||
async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
|
||||
"""Create a new custom agent.
|
||||
|
||||
Args:
|
||||
request: The agent creation request.
|
||||
|
||||
Returns:
|
||||
The created agent details.
|
||||
|
||||
Raises:
|
||||
HTTPException: 409 if agent already exists, 422 if name is invalid.
|
||||
"""
|
||||
_validate_agent_name(request.name)
|
||||
normalized_name = _normalize_agent_name(request.name)
|
||||
|
||||
agent_dir = get_paths().agent_dir(normalized_name)
|
||||
|
||||
if agent_dir.exists():
|
||||
raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
|
||||
|
||||
try:
|
||||
agent_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Write config.yaml
|
||||
config_data: dict = {"name": normalized_name}
|
||||
if request.description:
|
||||
config_data["description"] = request.description
|
||||
if request.model is not None:
|
||||
config_data["model"] = request.model
|
||||
if request.tool_groups is not None:
|
||||
config_data["tool_groups"] = request.tool_groups
|
||||
|
||||
config_file = agent_dir / "config.yaml"
|
||||
with open(config_file, "w", encoding="utf-8") as f:
|
||||
yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
|
||||
|
||||
# Write SOUL.md
|
||||
soul_file = agent_dir / "SOUL.md"
|
||||
soul_file.write_text(request.soul, encoding="utf-8")
|
||||
|
||||
logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
|
||||
|
||||
agent_cfg = load_agent_config(normalized_name)
|
||||
return _agent_config_to_response(agent_cfg, include_soul=True)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
# Clean up on failure
|
||||
if agent_dir.exists():
|
||||
shutil.rmtree(agent_dir)
|
||||
logger.error(f"Failed to create agent '{request.name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to create agent: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/agents/{name}",
|
||||
response_model=AgentResponse,
|
||||
summary="Update Custom Agent",
|
||||
description="Update an existing custom agent's config and/or SOUL.md.",
|
||||
)
|
||||
async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
|
||||
"""Update an existing custom agent.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
request: The update request (all fields optional).
|
||||
|
||||
Returns:
|
||||
The updated agent details.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
try:
|
||||
agent_cfg = load_agent_config(name)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
|
||||
agent_dir = get_paths().agent_dir(name)
|
||||
|
||||
try:
|
||||
# Update config if any config fields changed
|
||||
config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])
|
||||
|
||||
if config_changed:
|
||||
updated: dict = {
|
||||
"name": agent_cfg.name,
|
||||
"description": request.description if request.description is not None else agent_cfg.description,
|
||||
}
|
||||
new_model = request.model if request.model is not None else agent_cfg.model
|
||||
if new_model is not None:
|
||||
updated["model"] = new_model
|
||||
|
||||
new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
|
||||
if new_tool_groups is not None:
|
||||
updated["tool_groups"] = new_tool_groups
|
||||
|
||||
config_file = agent_dir / "config.yaml"
|
||||
with open(config_file, "w", encoding="utf-8") as f:
|
||||
yaml.dump(updated, f, default_flow_style=False, allow_unicode=True)
|
||||
|
||||
# Update SOUL.md if provided
|
||||
if request.soul is not None:
|
||||
soul_path = agent_dir / "SOUL.md"
|
||||
soul_path.write_text(request.soul, encoding="utf-8")
|
||||
|
||||
logger.info(f"Updated agent '{name}'")
|
||||
|
||||
refreshed_cfg = load_agent_config(name)
|
||||
return _agent_config_to_response(refreshed_cfg, include_soul=True)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update agent: {str(e)}")
|
||||
|
||||
|
||||
class UserProfileResponse(BaseModel):
|
||||
"""Response model for the global user profile (USER.md)."""
|
||||
|
||||
content: str | None = Field(default=None, description="USER.md content, or null if not yet created")
|
||||
|
||||
|
||||
class UserProfileUpdateRequest(BaseModel):
|
||||
"""Request body for setting the global user profile."""
|
||||
|
||||
content: str = Field(default="", description="USER.md content — describes the user's background and preferences")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/user-profile",
|
||||
response_model=UserProfileResponse,
|
||||
summary="Get User Profile",
|
||||
description="Read the global USER.md file that is injected into all custom agents.",
|
||||
)
|
||||
async def get_user_profile() -> UserProfileResponse:
|
||||
"""Return the current USER.md content.
|
||||
|
||||
Returns:
|
||||
UserProfileResponse with content=None if USER.md does not exist yet.
|
||||
"""
|
||||
try:
|
||||
user_md_path = get_paths().user_md_file
|
||||
if not user_md_path.exists():
|
||||
return UserProfileResponse(content=None)
|
||||
raw = user_md_path.read_text(encoding="utf-8").strip()
|
||||
return UserProfileResponse(content=raw or None)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to read user profile: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to read user profile: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/user-profile",
|
||||
response_model=UserProfileResponse,
|
||||
summary="Update User Profile",
|
||||
description="Write the global USER.md file that is injected into all custom agents.",
|
||||
)
|
||||
async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileResponse:
|
||||
"""Create or overwrite the global USER.md.
|
||||
|
||||
Args:
|
||||
request: The update request with the new USER.md content.
|
||||
|
||||
Returns:
|
||||
UserProfileResponse with the saved content.
|
||||
"""
|
||||
try:
|
||||
paths = get_paths()
|
||||
paths.base_dir.mkdir(parents=True, exist_ok=True)
|
||||
paths.user_md_file.write_text(request.content, encoding="utf-8")
|
||||
logger.info(f"Updated USER.md at {paths.user_md_file}")
|
||||
return UserProfileResponse(content=request.content or None)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update user profile: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update user profile: {str(e)}")
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/agents/{name}",
|
||||
status_code=204,
|
||||
summary="Delete Custom Agent",
|
||||
description="Delete a custom agent and all its files (config, SOUL.md, memory).",
|
||||
)
|
||||
async def delete_agent(name: str) -> None:
|
||||
"""Delete a custom agent.
|
||||
|
||||
Args:
|
||||
name: The agent name.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if agent not found.
|
||||
"""
|
||||
_validate_agent_name(name)
|
||||
name = _normalize_agent_name(name)
|
||||
|
||||
agent_dir = get_paths().agent_dir(name)
|
||||
|
||||
if not agent_dir.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
|
||||
|
||||
try:
|
||||
shutil.rmtree(agent_dir)
|
||||
logger.info(f"Deleted agent '{name}' from {agent_dir}")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete agent '{name}': {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete agent: {str(e)}")
|
||||
181
deer-flow/backend/app/gateway/routers/artifacts.py
Normal file
181
deer-flow/backend/app/gateway/routers/artifacts.py
Normal file
@@ -0,0 +1,181 @@
|
||||
import logging
|
||||
import mimetypes
|
||||
import zipfile
|
||||
from pathlib import Path
|
||||
from urllib.parse import quote
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from fastapi.responses import FileResponse, PlainTextResponse, Response
|
||||
|
||||
from app.gateway.path_utils import resolve_thread_virtual_path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["artifacts"])
|
||||
|
||||
ACTIVE_CONTENT_MIME_TYPES = {
|
||||
"text/html",
|
||||
"application/xhtml+xml",
|
||||
"image/svg+xml",
|
||||
}
|
||||
|
||||
|
||||
def _build_content_disposition(disposition_type: str, filename: str) -> str:
|
||||
"""Build an RFC 5987 encoded Content-Disposition header value."""
|
||||
return f"{disposition_type}; filename*=UTF-8''{quote(filename)}"
|
||||
|
||||
|
||||
def _build_attachment_headers(filename: str, extra_headers: dict[str, str] | None = None) -> dict[str, str]:
|
||||
headers = {"Content-Disposition": _build_content_disposition("attachment", filename)}
|
||||
if extra_headers:
|
||||
headers.update(extra_headers)
|
||||
return headers
|
||||
|
||||
|
||||
def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
|
||||
"""Check if file is text by examining content for null bytes."""
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
chunk = f.read(sample_size)
|
||||
# Text files shouldn't contain null bytes
|
||||
return b"\x00" not in chunk
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
|
||||
"""Extract a file from a .skill ZIP archive.
|
||||
|
||||
Args:
|
||||
zip_path: Path to the .skill file (ZIP archive).
|
||||
internal_path: Path to the file inside the archive (e.g., "SKILL.md").
|
||||
|
||||
Returns:
|
||||
The file content as bytes, or None if not found.
|
||||
"""
|
||||
if not zipfile.is_zipfile(zip_path):
|
||||
return None
|
||||
|
||||
try:
|
||||
with zipfile.ZipFile(zip_path, "r") as zip_ref:
|
||||
# List all files in the archive
|
||||
namelist = zip_ref.namelist()
|
||||
|
||||
# Try direct path first
|
||||
if internal_path in namelist:
|
||||
return zip_ref.read(internal_path)
|
||||
|
||||
# Try with any top-level directory prefix (e.g., "skill-name/SKILL.md")
|
||||
for name in namelist:
|
||||
if name.endswith("/" + internal_path) or name == internal_path:
|
||||
return zip_ref.read(name)
|
||||
|
||||
# Not found
|
||||
return None
|
||||
except (zipfile.BadZipFile, KeyError):
|
||||
return None
|
||||
|
||||
|
||||
@router.get(
|
||||
"/threads/{thread_id}/artifacts/{path:path}",
|
||||
summary="Get Artifact File",
|
||||
description="Retrieve an artifact file generated by the AI agent. Text and binary files can be viewed inline, while active web content is always downloaded.",
|
||||
)
|
||||
async def get_artifact(thread_id: str, path: str, request: Request, download: bool = False) -> Response:
|
||||
"""Get an artifact file by its path.
|
||||
|
||||
The endpoint automatically detects file types and returns appropriate content types.
|
||||
Use the `download` query parameter to force file download for non-active content.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
path: The artifact path with virtual prefix (e.g., mnt/user-data/outputs/file.txt).
|
||||
request: FastAPI request object (automatically injected).
|
||||
|
||||
Returns:
|
||||
The file content as a FileResponse with appropriate content type:
|
||||
- Active content (HTML/XHTML/SVG): Served as download attachment
|
||||
- Text files: Plain text with proper MIME type
|
||||
- Binary files: Inline display with download option
|
||||
|
||||
Raises:
|
||||
HTTPException:
|
||||
- 400 if path is invalid or not a file
|
||||
- 403 if access denied (path traversal detected)
|
||||
- 404 if file not found
|
||||
|
||||
Query Parameters:
|
||||
download (bool): If true, forces attachment download for file types that are
|
||||
otherwise returned inline or as plain text. Active HTML/XHTML/SVG content
|
||||
is always downloaded regardless of this flag.
|
||||
|
||||
Example:
|
||||
- Get text file inline: `/api/threads/abc123/artifacts/mnt/user-data/outputs/notes.txt`
|
||||
- Download file: `/api/threads/abc123/artifacts/mnt/user-data/outputs/data.csv?download=true`
|
||||
- Active web content such as `.html`, `.xhtml`, and `.svg` artifacts is always downloaded
|
||||
"""
|
||||
# Check if this is a request for a file inside a .skill archive (e.g., xxx.skill/SKILL.md)
|
||||
if ".skill/" in path:
|
||||
# Split the path at ".skill/" to get the ZIP file path and internal path
|
||||
skill_marker = ".skill/"
|
||||
marker_pos = path.find(skill_marker)
|
||||
skill_file_path = path[: marker_pos + len(".skill")] # e.g., "mnt/user-data/outputs/my-skill.skill"
|
||||
internal_path = path[marker_pos + len(skill_marker) :] # e.g., "SKILL.md"
|
||||
|
||||
actual_skill_path = resolve_thread_virtual_path(thread_id, skill_file_path)
|
||||
|
||||
if not actual_skill_path.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Skill file not found: {skill_file_path}")
|
||||
|
||||
if not actual_skill_path.is_file():
|
||||
raise HTTPException(status_code=400, detail=f"Path is not a file: {skill_file_path}")
|
||||
|
||||
# Extract the file from the .skill archive
|
||||
content = _extract_file_from_skill_archive(actual_skill_path, internal_path)
|
||||
if content is None:
|
||||
raise HTTPException(status_code=404, detail=f"File '{internal_path}' not found in skill archive")
|
||||
|
||||
# Determine MIME type based on the internal file
|
||||
mime_type, _ = mimetypes.guess_type(internal_path)
|
||||
# Add cache headers to avoid repeated ZIP extraction (cache for 5 minutes)
|
||||
cache_headers = {"Cache-Control": "private, max-age=300"}
|
||||
download_name = Path(internal_path).name or actual_skill_path.stem
|
||||
if download or mime_type in ACTIVE_CONTENT_MIME_TYPES:
|
||||
return Response(content=content, media_type=mime_type or "application/octet-stream", headers=_build_attachment_headers(download_name, cache_headers))
|
||||
|
||||
if mime_type and mime_type.startswith("text/"):
|
||||
return PlainTextResponse(content=content.decode("utf-8"), media_type=mime_type, headers=cache_headers)
|
||||
|
||||
# Default to plain text for unknown types that look like text
|
||||
try:
|
||||
return PlainTextResponse(content=content.decode("utf-8"), media_type="text/plain", headers=cache_headers)
|
||||
except UnicodeDecodeError:
|
||||
return Response(content=content, media_type=mime_type or "application/octet-stream", headers=cache_headers)
|
||||
|
||||
actual_path = resolve_thread_virtual_path(thread_id, path)
|
||||
|
||||
logger.info(f"Resolving artifact path: thread_id={thread_id}, requested_path={path}, actual_path={actual_path}")
|
||||
|
||||
if not actual_path.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Artifact not found: {path}")
|
||||
|
||||
if not actual_path.is_file():
|
||||
raise HTTPException(status_code=400, detail=f"Path is not a file: {path}")
|
||||
|
||||
mime_type, _ = mimetypes.guess_type(actual_path)
|
||||
|
||||
if download:
|
||||
return FileResponse(path=actual_path, filename=actual_path.name, media_type=mime_type, headers=_build_attachment_headers(actual_path.name))
|
||||
|
||||
# Always force download for active content types to prevent script execution
|
||||
# in the application origin when users open generated artifacts.
|
||||
if mime_type in ACTIVE_CONTENT_MIME_TYPES:
|
||||
return FileResponse(path=actual_path, filename=actual_path.name, media_type=mime_type, headers=_build_attachment_headers(actual_path.name))
|
||||
|
||||
if mime_type and mime_type.startswith("text/"):
|
||||
return PlainTextResponse(content=actual_path.read_text(encoding="utf-8"), media_type=mime_type)
|
||||
|
||||
if is_text_file_by_content(actual_path):
|
||||
return PlainTextResponse(content=actual_path.read_text(encoding="utf-8"), media_type=mime_type)
|
||||
|
||||
return Response(content=actual_path.read_bytes(), media_type=mime_type, headers={"Content-Disposition": _build_content_disposition("inline", actual_path.name)})
|
||||
149
deer-flow/backend/app/gateway/routers/assistants_compat.py
Normal file
149
deer-flow/backend/app/gateway/routers/assistants_compat.py
Normal file
@@ -0,0 +1,149 @@
|
||||
"""Assistants compatibility endpoints.
|
||||
|
||||
Provides LangGraph Platform-compatible assistants API backed by the
|
||||
``langgraph.json`` graph registry and ``config.yaml`` agent definitions.
|
||||
|
||||
This is a minimal stub that satisfies the ``useStream`` React hook's
|
||||
initialization requirements (``assistants.search()`` and ``assistants.get()``).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/assistants", tags=["assistants-compat"])
|
||||
|
||||
|
||||
class AssistantResponse(BaseModel):
|
||||
assistant_id: str
|
||||
graph_id: str
|
||||
name: str
|
||||
config: dict[str, Any] = Field(default_factory=dict)
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
description: str | None = None
|
||||
created_at: str = ""
|
||||
updated_at: str = ""
|
||||
version: int = 1
|
||||
|
||||
|
||||
class AssistantSearchRequest(BaseModel):
|
||||
graph_id: str | None = None
|
||||
name: str | None = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
limit: int = 10
|
||||
offset: int = 0
|
||||
|
||||
|
||||
def _get_default_assistant() -> AssistantResponse:
|
||||
"""Return the default lead_agent assistant."""
|
||||
now = datetime.now(UTC).isoformat()
|
||||
return AssistantResponse(
|
||||
assistant_id="lead_agent",
|
||||
graph_id="lead_agent",
|
||||
name="lead_agent",
|
||||
config={},
|
||||
metadata={"created_by": "system"},
|
||||
description="DeerFlow lead agent",
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
version=1,
|
||||
)
|
||||
|
||||
|
||||
def _list_assistants() -> list[AssistantResponse]:
|
||||
"""List all available assistants from config."""
|
||||
assistants = [_get_default_assistant()]
|
||||
|
||||
# Also include custom agents from config.yaml agents directory
|
||||
try:
|
||||
from deerflow.config.agents_config import list_custom_agents
|
||||
|
||||
for agent_cfg in list_custom_agents():
|
||||
now = datetime.now(UTC).isoformat()
|
||||
assistants.append(
|
||||
AssistantResponse(
|
||||
assistant_id=agent_cfg.name,
|
||||
graph_id="lead_agent", # All agents use the same graph
|
||||
name=agent_cfg.name,
|
||||
config={},
|
||||
metadata={"created_by": "user"},
|
||||
description=agent_cfg.description or "",
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
version=1,
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
logger.debug("Could not load custom agents for assistants list")
|
||||
|
||||
return assistants
|
||||
|
||||
|
||||
@router.post("/search", response_model=list[AssistantResponse])
|
||||
async def search_assistants(body: AssistantSearchRequest | None = None) -> list[AssistantResponse]:
|
||||
"""Search assistants.
|
||||
|
||||
Returns all registered assistants (lead_agent + custom agents from config).
|
||||
"""
|
||||
assistants = _list_assistants()
|
||||
|
||||
if body and body.graph_id:
|
||||
assistants = [a for a in assistants if a.graph_id == body.graph_id]
|
||||
if body and body.name:
|
||||
assistants = [a for a in assistants if body.name.lower() in a.name.lower()]
|
||||
|
||||
offset = body.offset if body else 0
|
||||
limit = body.limit if body else 10
|
||||
return assistants[offset : offset + limit]
|
||||
|
||||
|
||||
@router.get("/{assistant_id}", response_model=AssistantResponse)
|
||||
async def get_assistant_compat(assistant_id: str) -> AssistantResponse:
|
||||
"""Get an assistant by ID."""
|
||||
for a in _list_assistants():
|
||||
if a.assistant_id == assistant_id:
|
||||
return a
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
|
||||
@router.get("/{assistant_id}/graph")
|
||||
async def get_assistant_graph(assistant_id: str) -> dict:
|
||||
"""Get the graph structure for an assistant.
|
||||
|
||||
Returns a minimal graph description. Full graph introspection is
|
||||
not supported in the Gateway — this stub satisfies SDK validation.
|
||||
"""
|
||||
found = any(a.assistant_id == assistant_id for a in _list_assistants())
|
||||
if not found:
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
return {
|
||||
"graph_id": "lead_agent",
|
||||
"nodes": [],
|
||||
"edges": [],
|
||||
}
|
||||
|
||||
|
||||
@router.get("/{assistant_id}/schemas")
|
||||
async def get_assistant_schemas(assistant_id: str) -> dict:
|
||||
"""Get JSON schemas for an assistant's input/output/state.
|
||||
|
||||
Returns empty schemas — full introspection not supported in Gateway.
|
||||
"""
|
||||
found = any(a.assistant_id == assistant_id for a in _list_assistants())
|
||||
if not found:
|
||||
raise HTTPException(status_code=404, detail=f"Assistant {assistant_id} not found")
|
||||
|
||||
return {
|
||||
"graph_id": "lead_agent",
|
||||
"input_schema": {},
|
||||
"output_schema": {},
|
||||
"state_schema": {},
|
||||
"config_schema": {},
|
||||
}
|
||||
52
deer-flow/backend/app/gateway/routers/channels.py
Normal file
52
deer-flow/backend/app/gateway/routers/channels.py
Normal file
@@ -0,0 +1,52 @@
|
||||
"""Gateway router for IM channel management."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api/channels", tags=["channels"])
|
||||
|
||||
|
||||
class ChannelStatusResponse(BaseModel):
|
||||
service_running: bool
|
||||
channels: dict[str, dict]
|
||||
|
||||
|
||||
class ChannelRestartResponse(BaseModel):
|
||||
success: bool
|
||||
message: str
|
||||
|
||||
|
||||
@router.get("/", response_model=ChannelStatusResponse)
|
||||
async def get_channels_status() -> ChannelStatusResponse:
|
||||
"""Get the status of all IM channels."""
|
||||
from app.channels.service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
if service is None:
|
||||
return ChannelStatusResponse(service_running=False, channels={})
|
||||
status = service.get_status()
|
||||
return ChannelStatusResponse(**status)
|
||||
|
||||
|
||||
@router.post("/{name}/restart", response_model=ChannelRestartResponse)
|
||||
async def restart_channel(name: str) -> ChannelRestartResponse:
|
||||
"""Restart a specific IM channel."""
|
||||
from app.channels.service import get_channel_service
|
||||
|
||||
service = get_channel_service()
|
||||
if service is None:
|
||||
raise HTTPException(status_code=503, detail="Channel service is not running")
|
||||
|
||||
success = await service.restart_channel(name)
|
||||
if success:
|
||||
logger.info("Channel %s restarted successfully", name)
|
||||
return ChannelRestartResponse(success=True, message=f"Channel {name} restarted successfully")
|
||||
else:
|
||||
logger.warning("Failed to restart channel %s", name)
|
||||
return ChannelRestartResponse(success=False, message=f"Failed to restart channel {name}")
|
||||
169
deer-flow/backend/app/gateway/routers/mcp.py
Normal file
169
deer-flow/backend/app/gateway/routers/mcp.py
Normal file
@@ -0,0 +1,169 @@
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Literal
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api", tags=["mcp"])
|
||||
|
||||
|
||||
class McpOAuthConfigResponse(BaseModel):
|
||||
"""OAuth configuration for an MCP server."""
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
|
||||
token_url: str = Field(default="", description="OAuth token endpoint URL")
|
||||
grant_type: Literal["client_credentials", "refresh_token"] = Field(default="client_credentials", description="OAuth grant type")
|
||||
client_id: str | None = Field(default=None, description="OAuth client ID")
|
||||
client_secret: str | None = Field(default=None, description="OAuth client secret")
|
||||
refresh_token: str | None = Field(default=None, description="OAuth refresh token")
|
||||
scope: str | None = Field(default=None, description="OAuth scope")
|
||||
audience: str | None = Field(default=None, description="OAuth audience")
|
||||
token_field: str = Field(default="access_token", description="Token response field containing access token")
|
||||
token_type_field: str = Field(default="token_type", description="Token response field containing token type")
|
||||
expires_in_field: str = Field(default="expires_in", description="Token response field containing expires-in seconds")
|
||||
default_token_type: str = Field(default="Bearer", description="Default token type when response omits token_type")
|
||||
refresh_skew_seconds: int = Field(default=60, description="Refresh this many seconds before expiry")
|
||||
extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
|
||||
|
||||
|
||||
class McpServerConfigResponse(BaseModel):
|
||||
"""Response model for MCP server configuration."""
|
||||
|
||||
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
|
||||
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
|
||||
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
|
||||
args: list[str] = Field(default_factory=list, description="Arguments to pass to the command (for stdio type)")
|
||||
env: dict[str, str] = Field(default_factory=dict, description="Environment variables for the MCP server")
|
||||
url: str | None = Field(default=None, description="URL of the MCP server (for sse or http type)")
|
||||
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
|
||||
oauth: McpOAuthConfigResponse | None = Field(default=None, description="OAuth configuration for MCP HTTP/SSE servers")
|
||||
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
|
||||
|
||||
|
||||
class McpConfigResponse(BaseModel):
|
||||
"""Response model for MCP configuration."""
|
||||
|
||||
mcp_servers: dict[str, McpServerConfigResponse] = Field(
|
||||
default_factory=dict,
|
||||
description="Map of MCP server name to configuration",
|
||||
)
|
||||
|
||||
|
||||
class McpConfigUpdateRequest(BaseModel):
|
||||
"""Request model for updating MCP configuration."""
|
||||
|
||||
mcp_servers: dict[str, McpServerConfigResponse] = Field(
|
||||
...,
|
||||
description="Map of MCP server name to configuration",
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/mcp/config",
|
||||
response_model=McpConfigResponse,
|
||||
summary="Get MCP Configuration",
|
||||
description="Retrieve the current Model Context Protocol (MCP) server configurations.",
|
||||
)
|
||||
async def get_mcp_configuration() -> McpConfigResponse:
|
||||
"""Get the current MCP configuration.
|
||||
|
||||
Returns:
|
||||
The current MCP configuration with all servers.
|
||||
|
||||
Example:
|
||||
```json
|
||||
{
|
||||
"mcp_servers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "ghp_xxx"},
|
||||
"description": "GitHub MCP server for repository operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_extensions_config()
|
||||
|
||||
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
|
||||
|
||||
|
||||
@router.put(
|
||||
"/mcp/config",
|
||||
response_model=McpConfigResponse,
|
||||
summary="Update MCP Configuration",
|
||||
description="Update Model Context Protocol (MCP) server configurations and save to file.",
|
||||
)
|
||||
async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
|
||||
"""Update the MCP configuration.
|
||||
|
||||
This will:
|
||||
1. Save the new configuration to the mcp_config.json file
|
||||
2. Reload the configuration cache
|
||||
3. Reset MCP tools cache to trigger reinitialization
|
||||
|
||||
Args:
|
||||
request: The new MCP configuration to save.
|
||||
|
||||
Returns:
|
||||
The updated MCP configuration.
|
||||
|
||||
Raises:
|
||||
HTTPException: 500 if the configuration file cannot be written.
|
||||
|
||||
Example Request:
|
||||
```json
|
||||
{
|
||||
"mcp_servers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"},
|
||||
"description": "GitHub MCP server for repository operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
"""
|
||||
try:
|
||||
# Get the current config path (or determine where to save it)
|
||||
config_path = ExtensionsConfig.resolve_config_path()
|
||||
|
||||
# If no config file exists, create one in the parent directory (project root)
|
||||
if config_path is None:
|
||||
config_path = Path.cwd().parent / "extensions_config.json"
|
||||
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
|
||||
|
||||
# Load current config to preserve skills configuration
|
||||
current_config = get_extensions_config()
|
||||
|
||||
# Convert request to dict format for JSON serialization
|
||||
config_data = {
|
||||
"mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
|
||||
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
|
||||
}
|
||||
|
||||
# Write the configuration to file
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
json.dump(config_data, f, indent=2)
|
||||
|
||||
logger.info(f"MCP configuration updated and saved to: {config_path}")
|
||||
|
||||
# NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
|
||||
# will detect config file changes via mtime and reinitialize MCP tools automatically
|
||||
|
||||
# Reload the configuration and update the global cache
|
||||
reloaded_config = reload_extensions_config()
|
||||
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update MCP configuration: {str(e)}")
|
||||
353
deer-flow/backend/app/gateway/routers/memory.py
Normal file
353
deer-flow/backend/app/gateway/routers/memory.py
Normal file
@@ -0,0 +1,353 @@
|
||||
"""Memory API router for retrieving and managing global memory data."""
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.agents.memory.updater import (
|
||||
clear_memory_data,
|
||||
create_memory_fact,
|
||||
delete_memory_fact,
|
||||
get_memory_data,
|
||||
import_memory_data,
|
||||
reload_memory_data,
|
||||
update_memory_fact,
|
||||
)
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["memory"])
|
||||
|
||||
|
||||
class ContextSection(BaseModel):
|
||||
"""Model for context sections (user and history)."""
|
||||
|
||||
summary: str = Field(default="", description="Summary content")
|
||||
updatedAt: str = Field(default="", description="Last update timestamp")
|
||||
|
||||
|
||||
class UserContext(BaseModel):
|
||||
"""Model for user context."""
|
||||
|
||||
workContext: ContextSection = Field(default_factory=ContextSection)
|
||||
personalContext: ContextSection = Field(default_factory=ContextSection)
|
||||
topOfMind: ContextSection = Field(default_factory=ContextSection)
|
||||
|
||||
|
||||
class HistoryContext(BaseModel):
|
||||
"""Model for history context."""
|
||||
|
||||
recentMonths: ContextSection = Field(default_factory=ContextSection)
|
||||
earlierContext: ContextSection = Field(default_factory=ContextSection)
|
||||
longTermBackground: ContextSection = Field(default_factory=ContextSection)
|
||||
|
||||
|
||||
class Fact(BaseModel):
|
||||
"""Model for a memory fact."""
|
||||
|
||||
id: str = Field(..., description="Unique identifier for the fact")
|
||||
content: str = Field(..., description="Fact content")
|
||||
category: str = Field(default="context", description="Fact category")
|
||||
confidence: float = Field(default=0.5, description="Confidence score (0-1)")
|
||||
createdAt: str = Field(default="", description="Creation timestamp")
|
||||
source: str = Field(default="unknown", description="Source thread ID")
|
||||
sourceError: str | None = Field(default=None, description="Optional description of the prior mistake or wrong approach")
|
||||
|
||||
|
||||
class MemoryResponse(BaseModel):
|
||||
"""Response model for memory data."""
|
||||
|
||||
version: str = Field(default="1.0", description="Memory schema version")
|
||||
lastUpdated: str = Field(default="", description="Last update timestamp")
|
||||
user: UserContext = Field(default_factory=UserContext)
|
||||
history: HistoryContext = Field(default_factory=HistoryContext)
|
||||
facts: list[Fact] = Field(default_factory=list)
|
||||
|
||||
|
||||
def _map_memory_fact_value_error(exc: ValueError) -> HTTPException:
|
||||
"""Convert updater validation errors into stable API responses."""
|
||||
if exc.args and exc.args[0] == "confidence":
|
||||
detail = "Invalid confidence value; must be between 0 and 1."
|
||||
else:
|
||||
detail = "Memory fact content cannot be empty."
|
||||
return HTTPException(status_code=400, detail=detail)
|
||||
|
||||
|
||||
class FactCreateRequest(BaseModel):
|
||||
"""Request model for creating a memory fact."""
|
||||
|
||||
content: str = Field(..., min_length=1, description="Fact content")
|
||||
category: str = Field(default="context", description="Fact category")
|
||||
confidence: float = Field(default=0.5, ge=0.0, le=1.0, description="Confidence score (0-1)")
|
||||
|
||||
|
||||
class FactPatchRequest(BaseModel):
|
||||
"""PATCH request model that preserves existing values for omitted fields."""
|
||||
|
||||
content: str | None = Field(default=None, min_length=1, description="Fact content")
|
||||
category: str | None = Field(default=None, description="Fact category")
|
||||
confidence: float | None = Field(default=None, ge=0.0, le=1.0, description="Confidence score (0-1)")
|
||||
|
||||
|
||||
class MemoryConfigResponse(BaseModel):
|
||||
"""Response model for memory configuration."""
|
||||
|
||||
enabled: bool = Field(..., description="Whether memory is enabled")
|
||||
storage_path: str = Field(..., description="Path to memory storage file")
|
||||
debounce_seconds: int = Field(..., description="Debounce time for memory updates")
|
||||
max_facts: int = Field(..., description="Maximum number of facts to store")
|
||||
fact_confidence_threshold: float = Field(..., description="Minimum confidence threshold for facts")
|
||||
injection_enabled: bool = Field(..., description="Whether memory injection is enabled")
|
||||
max_injection_tokens: int = Field(..., description="Maximum tokens for memory injection")
|
||||
|
||||
|
||||
class MemoryStatusResponse(BaseModel):
|
||||
"""Response model for memory status."""
|
||||
|
||||
config: MemoryConfigResponse
|
||||
data: MemoryResponse
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Get Memory Data",
|
||||
description="Retrieve the current global memory data including user context, history, and facts.",
|
||||
)
|
||||
async def get_memory() -> MemoryResponse:
|
||||
"""Get the current global memory data.
|
||||
|
||||
Returns:
|
||||
The current memory data with user context, history, and facts.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"version": "1.0",
|
||||
"lastUpdated": "2024-01-15T10:30:00Z",
|
||||
"user": {
|
||||
"workContext": {"summary": "Working on DeerFlow project", "updatedAt": "..."},
|
||||
"personalContext": {"summary": "Prefers concise responses", "updatedAt": "..."},
|
||||
"topOfMind": {"summary": "Building memory API", "updatedAt": "..."}
|
||||
},
|
||||
"history": {
|
||||
"recentMonths": {"summary": "Recent development activities", "updatedAt": "..."},
|
||||
"earlierContext": {"summary": "", "updatedAt": ""},
|
||||
"longTermBackground": {"summary": "", "updatedAt": ""}
|
||||
},
|
||||
"facts": [
|
||||
{
|
||||
"id": "fact_abc123",
|
||||
"content": "User prefers TypeScript over JavaScript",
|
||||
"category": "preference",
|
||||
"confidence": 0.9,
|
||||
"createdAt": "2024-01-15T10:30:00Z",
|
||||
"source": "thread_xyz"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
"""
|
||||
memory_data = get_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/reload",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Reload Memory Data",
|
||||
description="Reload memory data from the storage file, refreshing the in-memory cache.",
|
||||
)
|
||||
async def reload_memory() -> MemoryResponse:
|
||||
"""Reload memory data from file.
|
||||
|
||||
This forces a reload of the memory data from the storage file,
|
||||
useful when the file has been modified externally.
|
||||
|
||||
Returns:
|
||||
The reloaded memory data.
|
||||
"""
|
||||
memory_data = reload_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/memory",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Clear All Memory Data",
|
||||
description="Delete all saved memory data and reset the memory structure to an empty state.",
|
||||
)
|
||||
async def clear_memory() -> MemoryResponse:
|
||||
"""Clear all persisted memory data."""
|
||||
try:
|
||||
memory_data = clear_memory_data()
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/facts",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Create Memory Fact",
|
||||
description="Create a single saved memory fact manually.",
|
||||
)
|
||||
async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
|
||||
"""Create a single fact manually."""
|
||||
try:
|
||||
memory_data = create_memory_fact(
|
||||
content=request.content,
|
||||
category=request.category,
|
||||
confidence=request.confidence,
|
||||
)
|
||||
except ValueError as exc:
|
||||
raise _map_memory_fact_value_error(exc) from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to create memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/memory/facts/{fact_id}",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Delete Memory Fact",
|
||||
description="Delete a single saved memory fact by its fact id.",
|
||||
)
|
||||
async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
|
||||
"""Delete a single fact from memory by fact id."""
|
||||
try:
|
||||
memory_data = delete_memory_fact(fact_id)
|
||||
except KeyError as exc:
|
||||
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to delete memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.patch(
|
||||
"/memory/facts/{fact_id}",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Patch Memory Fact",
|
||||
description="Partially update a single saved memory fact by its fact id while preserving omitted fields.",
|
||||
)
|
||||
async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -> MemoryResponse:
|
||||
"""Partially update a single fact manually."""
|
||||
try:
|
||||
memory_data = update_memory_fact(
|
||||
fact_id=fact_id,
|
||||
content=request.content,
|
||||
category=request.category,
|
||||
confidence=request.confidence,
|
||||
)
|
||||
except ValueError as exc:
|
||||
raise _map_memory_fact_value_error(exc) from exc
|
||||
except KeyError as exc:
|
||||
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to update memory fact.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/export",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Export Memory Data",
|
||||
description="Export the current global memory data as JSON for backup or transfer.",
|
||||
)
|
||||
async def export_memory() -> MemoryResponse:
|
||||
"""Export the current memory data."""
|
||||
memory_data = get_memory_data()
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/memory/import",
|
||||
response_model=MemoryResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Import Memory Data",
|
||||
description="Import and overwrite the current global memory data from a JSON payload.",
|
||||
)
|
||||
async def import_memory(request: MemoryResponse) -> MemoryResponse:
|
||||
"""Import and persist memory data."""
|
||||
try:
|
||||
memory_data = import_memory_data(request.model_dump())
|
||||
except OSError as exc:
|
||||
raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
|
||||
|
||||
return MemoryResponse(**memory_data)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/config",
|
||||
response_model=MemoryConfigResponse,
|
||||
summary="Get Memory Configuration",
|
||||
description="Retrieve the current memory system configuration.",
|
||||
)
|
||||
async def get_memory_config_endpoint() -> MemoryConfigResponse:
|
||||
"""Get the memory system configuration.
|
||||
|
||||
Returns:
|
||||
The current memory configuration settings.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"enabled": true,
|
||||
"storage_path": ".deer-flow/memory.json",
|
||||
"debounce_seconds": 30,
|
||||
"max_facts": 100,
|
||||
"fact_confidence_threshold": 0.7,
|
||||
"injection_enabled": true,
|
||||
"max_injection_tokens": 2000
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_memory_config()
|
||||
return MemoryConfigResponse(
|
||||
enabled=config.enabled,
|
||||
storage_path=config.storage_path,
|
||||
debounce_seconds=config.debounce_seconds,
|
||||
max_facts=config.max_facts,
|
||||
fact_confidence_threshold=config.fact_confidence_threshold,
|
||||
injection_enabled=config.injection_enabled,
|
||||
max_injection_tokens=config.max_injection_tokens,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/memory/status",
|
||||
response_model=MemoryStatusResponse,
|
||||
response_model_exclude_none=True,
|
||||
summary="Get Memory Status",
|
||||
description="Retrieve both memory configuration and current data in a single request.",
|
||||
)
|
||||
async def get_memory_status() -> MemoryStatusResponse:
|
||||
"""Get the memory system status including configuration and data.
|
||||
|
||||
Returns:
|
||||
Combined memory configuration and current data.
|
||||
"""
|
||||
config = get_memory_config()
|
||||
memory_data = get_memory_data()
|
||||
|
||||
return MemoryStatusResponse(
|
||||
config=MemoryConfigResponse(
|
||||
enabled=config.enabled,
|
||||
storage_path=config.storage_path,
|
||||
debounce_seconds=config.debounce_seconds,
|
||||
max_facts=config.max_facts,
|
||||
fact_confidence_threshold=config.fact_confidence_threshold,
|
||||
injection_enabled=config.injection_enabled,
|
||||
max_injection_tokens=config.max_injection_tokens,
|
||||
),
|
||||
data=MemoryResponse(**memory_data),
|
||||
)
|
||||
116
deer-flow/backend/app/gateway/routers/models.py
Normal file
116
deer-flow/backend/app/gateway/routers/models.py
Normal file
@@ -0,0 +1,116 @@
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["models"])
|
||||
|
||||
|
||||
class ModelResponse(BaseModel):
|
||||
"""Response model for model information."""
|
||||
|
||||
name: str = Field(..., description="Unique identifier for the model")
|
||||
model: str = Field(..., description="Actual provider model identifier")
|
||||
display_name: str | None = Field(None, description="Human-readable name")
|
||||
description: str | None = Field(None, description="Model description")
|
||||
supports_thinking: bool = Field(default=False, description="Whether model supports thinking mode")
|
||||
supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")
|
||||
|
||||
|
||||
class ModelsListResponse(BaseModel):
|
||||
"""Response model for listing all models."""
|
||||
|
||||
models: list[ModelResponse]
|
||||
|
||||
|
||||
@router.get(
|
||||
"/models",
|
||||
response_model=ModelsListResponse,
|
||||
summary="List All Models",
|
||||
description="Retrieve a list of all available AI models configured in the system.",
|
||||
)
|
||||
async def list_models() -> ModelsListResponse:
|
||||
"""List all available models from configuration.
|
||||
|
||||
Returns model information suitable for frontend display,
|
||||
excluding sensitive fields like API keys and internal configuration.
|
||||
|
||||
Returns:
|
||||
A list of all configured models with their metadata.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"models": [
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"description": "OpenAI GPT-4 model",
|
||||
"supports_thinking": false
|
||||
},
|
||||
{
|
||||
"name": "claude-3-opus",
|
||||
"display_name": "Claude 3 Opus",
|
||||
"description": "Anthropic Claude 3 Opus model",
|
||||
"supports_thinking": true
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_app_config()
|
||||
models = [
|
||||
ModelResponse(
|
||||
name=model.name,
|
||||
model=model.model,
|
||||
display_name=model.display_name,
|
||||
description=model.description,
|
||||
supports_thinking=model.supports_thinking,
|
||||
supports_reasoning_effort=model.supports_reasoning_effort,
|
||||
)
|
||||
for model in config.models
|
||||
]
|
||||
return ModelsListResponse(models=models)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/models/{model_name}",
|
||||
response_model=ModelResponse,
|
||||
summary="Get Model Details",
|
||||
description="Retrieve detailed information about a specific AI model by its name.",
|
||||
)
|
||||
async def get_model(model_name: str) -> ModelResponse:
|
||||
"""Get a specific model by name.
|
||||
|
||||
Args:
|
||||
model_name: The unique name of the model to retrieve.
|
||||
|
||||
Returns:
|
||||
Model information if found.
|
||||
|
||||
Raises:
|
||||
HTTPException: 404 if model not found.
|
||||
|
||||
Example Response:
|
||||
```json
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"description": "OpenAI GPT-4 model",
|
||||
"supports_thinking": false
|
||||
}
|
||||
```
|
||||
"""
|
||||
config = get_app_config()
|
||||
model = config.get_model_config(model_name)
|
||||
if model is None:
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
|
||||
|
||||
return ModelResponse(
|
||||
name=model.name,
|
||||
model=model.model,
|
||||
display_name=model.display_name,
|
||||
description=model.description,
|
||||
supports_thinking=model.supports_thinking,
|
||||
supports_reasoning_effort=model.supports_reasoning_effort,
|
||||
)
|
||||
87
deer-flow/backend/app/gateway/routers/runs.py
Normal file
87
deer-flow/backend/app/gateway/routers/runs.py
Normal file
@@ -0,0 +1,87 @@
|
||||
"""Stateless runs endpoints -- stream and wait without a pre-existing thread.
|
||||
|
||||
These endpoints auto-create a temporary thread when no ``thread_id`` is
|
||||
supplied in the request body. When a ``thread_id`` **is** provided, it
|
||||
is reused so that conversation history is preserved across calls.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import uuid
|
||||
|
||||
from fastapi import APIRouter, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
|
||||
from app.gateway.routers.thread_runs import RunCreateRequest
|
||||
from app.gateway.services import sse_consumer, start_run
|
||||
from deerflow.runtime import serialize_channel_values
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/runs", tags=["runs"])
|
||||
|
||||
|
||||
def _resolve_thread_id(body: RunCreateRequest) -> str:
|
||||
"""Return the thread_id from the request body, or generate a new one."""
|
||||
thread_id = (body.config or {}).get("configurable", {}).get("thread_id")
|
||||
if thread_id:
|
||||
return str(thread_id)
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
@router.post("/stream")
|
||||
async def stateless_stream(body: RunCreateRequest, request: Request) -> StreamingResponse:
|
||||
"""Create a run and stream events via SSE.
|
||||
|
||||
If ``config.configurable.thread_id`` is provided, the run is created
|
||||
on the given thread so that conversation history is preserved.
|
||||
Otherwise a new temporary thread is created.
|
||||
"""
|
||||
thread_id = _resolve_thread_id(body)
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
"Content-Location": f"/api/threads/{thread_id}/runs/{record.run_id}",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/wait", response_model=dict)
|
||||
async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
|
||||
"""Create a run and block until completion.
|
||||
|
||||
If ``config.configurable.thread_id`` is provided, the run is created
|
||||
on the given thread so that conversation history is preserved.
|
||||
Otherwise a new temporary thread is created.
|
||||
"""
|
||||
thread_id = _resolve_thread_id(body)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
if record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
checkpointer = get_checkpointer(request)
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
if checkpoint_tuple is not None:
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
return serialize_channel_values(channel_values)
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch final state for run %s", record.run_id)
|
||||
|
||||
return {"status": record.status.value, "error": record.error}
|
||||
356
deer-flow/backend/app/gateway/routers/skills.py
Normal file
356
deer-flow/backend/app/gateway/routers/skills.py
Normal file
@@ -0,0 +1,356 @@
|
||||
import json
|
||||
import logging
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.path_utils import resolve_thread_virtual_path
|
||||
from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
|
||||
from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
|
||||
from deerflow.skills import Skill, load_skills
|
||||
from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
|
||||
from deerflow.skills.manager import (
|
||||
append_history,
|
||||
atomic_write,
|
||||
custom_skill_exists,
|
||||
ensure_custom_skill_is_editable,
|
||||
get_custom_skill_dir,
|
||||
get_custom_skill_file,
|
||||
get_skill_history_file,
|
||||
read_custom_skill_content,
|
||||
read_history,
|
||||
validate_skill_markdown_content,
|
||||
)
|
||||
from deerflow.skills.security_scanner import scan_skill_content
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["skills"])
|
||||
|
||||
|
||||
class SkillResponse(BaseModel):
|
||||
"""Response model for skill information."""
|
||||
|
||||
name: str = Field(..., description="Name of the skill")
|
||||
description: str = Field(..., description="Description of what the skill does")
|
||||
license: str | None = Field(None, description="License information")
|
||||
category: str = Field(..., description="Category of the skill (public or custom)")
|
||||
enabled: bool = Field(default=True, description="Whether this skill is enabled")
|
||||
|
||||
|
||||
class SkillsListResponse(BaseModel):
|
||||
"""Response model for listing all skills."""
|
||||
|
||||
skills: list[SkillResponse]
|
||||
|
||||
|
||||
class SkillUpdateRequest(BaseModel):
|
||||
"""Request model for updating a skill."""
|
||||
|
||||
enabled: bool = Field(..., description="Whether to enable or disable the skill")
|
||||
|
||||
|
||||
class SkillInstallRequest(BaseModel):
|
||||
"""Request model for installing a skill from a .skill file."""
|
||||
|
||||
thread_id: str = Field(..., description="The thread ID where the .skill file is located")
|
||||
path: str = Field(..., description="Virtual path to the .skill file (e.g., mnt/user-data/outputs/my-skill.skill)")
|
||||
|
||||
|
||||
class SkillInstallResponse(BaseModel):
|
||||
"""Response model for skill installation."""
|
||||
|
||||
success: bool = Field(..., description="Whether the installation was successful")
|
||||
skill_name: str = Field(..., description="Name of the installed skill")
|
||||
message: str = Field(..., description="Installation result message")
|
||||
|
||||
|
||||
class CustomSkillContentResponse(SkillResponse):
|
||||
content: str = Field(..., description="Raw SKILL.md content")
|
||||
|
||||
|
||||
class CustomSkillUpdateRequest(BaseModel):
|
||||
content: str = Field(..., description="Replacement SKILL.md content")
|
||||
|
||||
|
||||
class CustomSkillHistoryResponse(BaseModel):
|
||||
history: list[dict]
|
||||
|
||||
|
||||
class SkillRollbackRequest(BaseModel):
|
||||
history_index: int = Field(default=-1, description="History entry index to restore from, defaulting to the latest change.")
|
||||
|
||||
|
||||
def _skill_to_response(skill: Skill) -> SkillResponse:
|
||||
"""Convert a Skill object to a SkillResponse."""
|
||||
return SkillResponse(
|
||||
name=skill.name,
|
||||
description=skill.description,
|
||||
license=skill.license,
|
||||
category=skill.category,
|
||||
enabled=skill.enabled,
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/skills",
|
||||
response_model=SkillsListResponse,
|
||||
summary="List All Skills",
|
||||
description="Retrieve a list of all available skills from both public and custom directories.",
|
||||
)
|
||||
async def list_skills() -> SkillsListResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load skills: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to load skills: {str(e)}")
|
||||
|
||||
|
||||
@router.post(
|
||||
"/skills/install",
|
||||
response_model=SkillInstallResponse,
|
||||
summary="Install Skill",
|
||||
description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
|
||||
)
|
||||
async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
|
||||
try:
|
||||
skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
|
||||
result = install_skill_from_archive(skill_file_path)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return SkillInstallResponse(**result)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except SkillAlreadyExistsError as e:
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to install skill: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to install skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
|
||||
async def list_custom_skills() -> SkillsListResponse:
|
||||
try:
|
||||
skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
|
||||
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
|
||||
except Exception as e:
|
||||
logger.error("Failed to list custom skills: %s", e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to list custom skills: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
|
||||
async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error("Failed to get custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
|
||||
async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
|
||||
try:
|
||||
ensure_custom_skill_is_editable(skill_name)
|
||||
validate_skill_markdown_content(skill_name, request.content)
|
||||
scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
|
||||
if scan.decision == "block":
|
||||
raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
|
||||
skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
|
||||
prev_content = skill_file.read_text(encoding="utf-8")
|
||||
atomic_write(skill_file, request.content)
|
||||
append_history(
|
||||
skill_name,
|
||||
{
|
||||
"action": "human_edit",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": prev_content,
|
||||
"new_content": request.content,
|
||||
"scanner": {"decision": scan.decision, "reason": scan.reason},
|
||||
},
|
||||
)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return await get_custom_skill(skill_name)
|
||||
except HTTPException:
|
||||
raise
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to update custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
|
||||
async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
|
||||
try:
|
||||
ensure_custom_skill_is_editable(skill_name)
|
||||
skill_dir = get_custom_skill_dir(skill_name)
|
||||
prev_content = read_custom_skill_content(skill_name)
|
||||
append_history(
|
||||
skill_name,
|
||||
{
|
||||
"action": "human_delete",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": prev_content,
|
||||
"new_content": None,
|
||||
"scanner": {"decision": "allow", "reason": "Deletion requested."},
|
||||
},
|
||||
)
|
||||
shutil.rmtree(skill_dir)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return {"success": True}
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to delete custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
|
||||
async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
|
||||
try:
|
||||
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
return CustomSkillHistoryResponse(history=read_history(skill_name))
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error("Failed to read history for %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to read history: {str(e)}")
|
||||
|
||||
|
||||
@router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
|
||||
async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
|
||||
try:
|
||||
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
|
||||
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
|
||||
history = read_history(skill_name)
|
||||
if not history:
|
||||
raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
|
||||
record = history[request.history_index]
|
||||
target_content = record.get("prev_content")
|
||||
if target_content is None:
|
||||
raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
|
||||
validate_skill_markdown_content(skill_name, target_content)
|
||||
scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
|
||||
skill_file = get_custom_skill_file(skill_name)
|
||||
current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
|
||||
history_entry = {
|
||||
"action": "rollback",
|
||||
"author": "human",
|
||||
"thread_id": None,
|
||||
"file_path": "SKILL.md",
|
||||
"prev_content": current_content,
|
||||
"new_content": target_content,
|
||||
"rollback_from_ts": record.get("ts"),
|
||||
"scanner": {"decision": scan.decision, "reason": scan.reason},
|
||||
}
|
||||
if scan.decision == "block":
|
||||
append_history(skill_name, history_entry)
|
||||
raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
|
||||
atomic_write(skill_file, target_content)
|
||||
append_history(skill_name, history_entry)
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
return await get_custom_skill(skill_name)
|
||||
except HTTPException:
|
||||
raise
|
||||
except IndexError:
|
||||
raise HTTPException(status_code=400, detail="history_index is out of range")
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error("Failed to roll back custom skill %s: %s", skill_name, e, exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to roll back custom skill: {str(e)}")
|
||||
|
||||
|
||||
@router.get(
|
||||
"/skills/{skill_name}",
|
||||
response_model=SkillResponse,
|
||||
summary="Get Skill Details",
|
||||
description="Retrieve detailed information about a specific skill by its name.",
|
||||
)
|
||||
async def get_skill(skill_name: str) -> SkillResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
|
||||
|
||||
return _skill_to_response(skill)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get skill {skill_name}: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to get skill: {str(e)}")
|
||||
|
||||
|
||||
@router.put(
|
||||
"/skills/{skill_name}",
|
||||
response_model=SkillResponse,
|
||||
summary="Update Skill",
|
||||
description="Update a skill's enabled status by modifying the extensions_config.json file.",
|
||||
)
|
||||
async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
|
||||
try:
|
||||
skills = load_skills(enabled_only=False)
|
||||
skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if skill is None:
|
||||
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
|
||||
|
||||
config_path = ExtensionsConfig.resolve_config_path()
|
||||
if config_path is None:
|
||||
config_path = Path.cwd().parent / "extensions_config.json"
|
||||
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
|
||||
|
||||
extensions_config = get_extensions_config()
|
||||
extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)
|
||||
|
||||
config_data = {
|
||||
"mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
|
||||
"skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
|
||||
}
|
||||
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
json.dump(config_data, f, indent=2)
|
||||
|
||||
logger.info(f"Skills configuration updated and saved to: {config_path}")
|
||||
reload_extensions_config()
|
||||
await refresh_skills_system_prompt_cache_async()
|
||||
|
||||
skills = load_skills(enabled_only=False)
|
||||
updated_skill = next((s for s in skills if s.name == skill_name), None)
|
||||
|
||||
if updated_skill is None:
|
||||
raise HTTPException(status_code=500, detail=f"Failed to reload skill '{skill_name}' after update")
|
||||
|
||||
logger.info(f"Skill '{skill_name}' enabled status updated to {request.enabled}")
|
||||
return _skill_to_response(updated_skill)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update skill {skill_name}: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=f"Failed to update skill: {str(e)}")
|
||||
132
deer-flow/backend/app/gateway/routers/suggestions.py
Normal file
132
deer-flow/backend/app/gateway/routers/suggestions.py
Normal file
@@ -0,0 +1,132 @@
|
||||
import json
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api", tags=["suggestions"])
|
||||
|
||||
|
||||
class SuggestionMessage(BaseModel):
|
||||
role: str = Field(..., description="Message role: user|assistant")
|
||||
content: str = Field(..., description="Message content as plain text")
|
||||
|
||||
|
||||
class SuggestionsRequest(BaseModel):
|
||||
messages: list[SuggestionMessage] = Field(..., description="Recent conversation messages")
|
||||
n: int = Field(default=3, ge=1, le=5, description="Number of suggestions to generate")
|
||||
model_name: str | None = Field(default=None, description="Optional model override")
|
||||
|
||||
|
||||
class SuggestionsResponse(BaseModel):
|
||||
suggestions: list[str] = Field(default_factory=list, description="Suggested follow-up questions")
|
||||
|
||||
|
||||
def _strip_markdown_code_fence(text: str) -> str:
|
||||
stripped = text.strip()
|
||||
if not stripped.startswith("```"):
|
||||
return stripped
|
||||
lines = stripped.splitlines()
|
||||
if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
|
||||
return "\n".join(lines[1:-1]).strip()
|
||||
return stripped
|
||||
|
||||
|
||||
def _parse_json_string_list(text: str) -> list[str] | None:
|
||||
candidate = _strip_markdown_code_fence(text)
|
||||
start = candidate.find("[")
|
||||
end = candidate.rfind("]")
|
||||
if start == -1 or end == -1 or end <= start:
|
||||
return None
|
||||
candidate = candidate[start : end + 1]
|
||||
try:
|
||||
data = json.loads(candidate)
|
||||
except Exception:
|
||||
return None
|
||||
if not isinstance(data, list):
|
||||
return None
|
||||
out: list[str] = []
|
||||
for item in data:
|
||||
if not isinstance(item, str):
|
||||
continue
|
||||
s = item.strip()
|
||||
if not s:
|
||||
continue
|
||||
out.append(s)
|
||||
return out
|
||||
|
||||
|
||||
def _extract_response_text(content: object) -> str:
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts: list[str] = []
|
||||
for block in content:
|
||||
if isinstance(block, str):
|
||||
parts.append(block)
|
||||
elif isinstance(block, dict) and block.get("type") in {"text", "output_text"}:
|
||||
text = block.get("text")
|
||||
if isinstance(text, str):
|
||||
parts.append(text)
|
||||
return "\n".join(parts) if parts else ""
|
||||
if content is None:
|
||||
return ""
|
||||
return str(content)
|
||||
|
||||
|
||||
def _format_conversation(messages: list[SuggestionMessage]) -> str:
|
||||
parts: list[str] = []
|
||||
for m in messages:
|
||||
role = m.role.strip().lower()
|
||||
if role in ("user", "human"):
|
||||
parts.append(f"User: {m.content.strip()}")
|
||||
elif role in ("assistant", "ai"):
|
||||
parts.append(f"Assistant: {m.content.strip()}")
|
||||
else:
|
||||
parts.append(f"{m.role}: {m.content.strip()}")
|
||||
return "\n".join(parts).strip()
|
||||
|
||||
|
||||
@router.post(
|
||||
"/threads/{thread_id}/suggestions",
|
||||
response_model=SuggestionsResponse,
|
||||
summary="Generate Follow-up Questions",
|
||||
description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
|
||||
)
|
||||
async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> SuggestionsResponse:
|
||||
if not request.messages:
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
|
||||
n = request.n
|
||||
conversation = _format_conversation(request.messages)
|
||||
if not conversation:
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
|
||||
system_instruction = (
|
||||
"You are generating follow-up questions to help the user continue the conversation.\n"
|
||||
f"Based on the conversation below, produce EXACTLY {n} short questions the user might ask next.\n"
|
||||
"Requirements:\n"
|
||||
"- Questions must be relevant to the preceding conversation.\n"
|
||||
"- Questions must be written in the same language as the user.\n"
|
||||
"- Keep each question concise (ideally <= 20 words / <= 40 Chinese characters).\n"
|
||||
"- Do NOT include numbering, markdown, or any extra text.\n"
|
||||
"- Output MUST be a JSON array of strings only.\n"
|
||||
)
|
||||
user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
|
||||
|
||||
try:
|
||||
model = create_chat_model(name=request.model_name, thinking_enabled=False)
|
||||
response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
|
||||
raw = _extract_response_text(response.content)
|
||||
suggestions = _parse_json_string_list(raw) or []
|
||||
cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]
|
||||
cleaned = cleaned[:n]
|
||||
return SuggestionsResponse(suggestions=cleaned)
|
||||
except Exception as exc:
|
||||
logger.exception("Failed to generate suggestions: thread_id=%s err=%s", thread_id, exc)
|
||||
return SuggestionsResponse(suggestions=[])
|
||||
267
deer-flow/backend/app/gateway/routers/thread_runs.py
Normal file
267
deer-flow/backend/app/gateway/routers/thread_runs.py
Normal file
@@ -0,0 +1,267 @@
|
||||
"""Runs endpoints — create, stream, wait, cancel.
|
||||
|
||||
Implements the LangGraph Platform runs API on top of
|
||||
:class:`deerflow.agents.runs.RunManager` and
|
||||
:class:`deerflow.agents.stream_bridge.StreamBridge`.
|
||||
|
||||
SSE format is aligned with the LangGraph Platform protocol so that
|
||||
the ``useStream`` React hook from ``@langchain/langgraph-sdk/react``
|
||||
works without modification.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any, Literal
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Query, Request
|
||||
from fastapi.responses import Response, StreamingResponse
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
|
||||
from app.gateway.services import sse_consumer, start_run
|
||||
from deerflow.runtime import RunRecord, serialize_channel_values
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/threads", tags=["runs"])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Request / response models
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class RunCreateRequest(BaseModel):
|
||||
assistant_id: str | None = Field(default=None, description="Agent / assistant to use")
|
||||
input: dict[str, Any] | None = Field(default=None, description="Graph input (e.g. {messages: [...]})")
|
||||
command: dict[str, Any] | None = Field(default=None, description="LangGraph Command")
|
||||
metadata: dict[str, Any] | None = Field(default=None, description="Run metadata")
|
||||
config: dict[str, Any] | None = Field(default=None, description="RunnableConfig overrides")
|
||||
context: dict[str, Any] | None = Field(default=None, description="DeerFlow context overrides (model_name, thinking_enabled, etc.)")
|
||||
webhook: str | None = Field(default=None, description="Completion callback URL")
|
||||
checkpoint_id: str | None = Field(default=None, description="Resume from checkpoint")
|
||||
checkpoint: dict[str, Any] | None = Field(default=None, description="Full checkpoint object")
|
||||
interrupt_before: list[str] | Literal["*"] | None = Field(default=None, description="Nodes to interrupt before")
|
||||
interrupt_after: list[str] | Literal["*"] | None = Field(default=None, description="Nodes to interrupt after")
|
||||
stream_mode: list[str] | str | None = Field(default=None, description="Stream mode(s)")
|
||||
stream_subgraphs: bool = Field(default=False, description="Include subgraph events")
|
||||
stream_resumable: bool | None = Field(default=None, description="SSE resumable mode")
|
||||
on_disconnect: Literal["cancel", "continue"] = Field(default="cancel", description="Behaviour on SSE disconnect")
|
||||
on_completion: Literal["delete", "keep"] = Field(default="keep", description="Delete temp thread on completion")
|
||||
multitask_strategy: Literal["reject", "rollback", "interrupt", "enqueue"] = Field(default="reject", description="Concurrency strategy")
|
||||
after_seconds: float | None = Field(default=None, description="Delayed execution")
|
||||
if_not_exists: Literal["reject", "create"] = Field(default="create", description="Thread creation policy")
|
||||
feedback_keys: list[str] | None = Field(default=None, description="LangSmith feedback keys")
|
||||
|
||||
|
||||
class RunResponse(BaseModel):
|
||||
run_id: str
|
||||
thread_id: str
|
||||
assistant_id: str | None = None
|
||||
status: str
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
kwargs: dict[str, Any] = Field(default_factory=dict)
|
||||
multitask_strategy: str = "reject"
|
||||
created_at: str = ""
|
||||
updated_at: str = ""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _record_to_response(record: RunRecord) -> RunResponse:
|
||||
return RunResponse(
|
||||
run_id=record.run_id,
|
||||
thread_id=record.thread_id,
|
||||
assistant_id=record.assistant_id,
|
||||
status=record.status.value,
|
||||
metadata=record.metadata,
|
||||
kwargs=record.kwargs,
|
||||
multitask_strategy=record.multitask_strategy,
|
||||
created_at=record.created_at,
|
||||
updated_at=record.updated_at,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Endpoints
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs", response_model=RunResponse)
|
||||
async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -> RunResponse:
|
||||
"""Create a background run (returns immediately)."""
|
||||
record = await start_run(body, thread_id, request)
|
||||
return _record_to_response(record)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/stream")
|
||||
async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -> StreamingResponse:
|
||||
"""Create a run and stream events via SSE.
|
||||
|
||||
The response includes a ``Content-Location`` header with the run's
|
||||
resource URL, matching the LangGraph Platform protocol. The
|
||||
``useStream`` React hook uses this to extract run metadata.
|
||||
"""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
# LangGraph Platform includes run metadata in this header.
|
||||
# The SDK uses a greedy regex to extract the run id from this path,
|
||||
# so it must point at the canonical run resource without extra suffixes.
|
||||
"Content-Location": f"/api/threads/{thread_id}/runs/{record.run_id}",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/wait", response_model=dict)
|
||||
async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
|
||||
"""Create a run and block until it completes, returning the final state."""
|
||||
record = await start_run(body, thread_id, request)
|
||||
|
||||
if record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
checkpointer = get_checkpointer(request)
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
if checkpoint_tuple is not None:
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
return serialize_channel_values(channel_values)
|
||||
except Exception:
|
||||
logger.exception("Failed to fetch final state for run %s", record.run_id)
|
||||
|
||||
return {"status": record.status.value, "error": record.error}
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs", response_model=list[RunResponse])
|
||||
async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
|
||||
"""List all runs for a thread."""
|
||||
run_mgr = get_run_manager(request)
|
||||
records = await run_mgr.list_by_thread(thread_id)
|
||||
return [_record_to_response(r) for r in records]
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs/{run_id}", response_model=RunResponse)
|
||||
async def get_run(thread_id: str, run_id: str, request: Request) -> RunResponse:
|
||||
"""Get details of a specific run."""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
return _record_to_response(record)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/runs/{run_id}/cancel")
|
||||
async def cancel_run(
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
request: Request,
|
||||
wait: bool = Query(default=False, description="Block until run completes after cancel"),
|
||||
action: Literal["interrupt", "rollback"] = Query(default="interrupt", description="Cancel action"),
|
||||
) -> Response:
|
||||
"""Cancel a running or pending run.
|
||||
|
||||
- action=interrupt: Stop execution, keep current checkpoint (can be resumed)
|
||||
- action=rollback: Stop execution, revert to pre-run checkpoint state
|
||||
- wait=true: Block until the run fully stops, return 204
|
||||
- wait=false: Return immediately with 202
|
||||
"""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
cancelled = await run_mgr.cancel(run_id, action=action)
|
||||
if not cancelled:
|
||||
raise HTTPException(
|
||||
status_code=409,
|
||||
detail=f"Run {run_id} is not cancellable (status: {record.status.value})",
|
||||
)
|
||||
|
||||
if wait and record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
return Response(status_code=204)
|
||||
|
||||
return Response(status_code=202)
|
||||
|
||||
|
||||
@router.get("/{thread_id}/runs/{run_id}/join")
|
||||
async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingResponse:
|
||||
"""Join an existing run's SSE stream."""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
|
||||
async def stream_existing_run(
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
request: Request,
|
||||
action: Literal["interrupt", "rollback"] | None = Query(default=None, description="Cancel action"),
|
||||
wait: int = Query(default=0, description="Block until cancelled (1) or return immediately (0)"),
|
||||
):
|
||||
"""Join an existing run's SSE stream (GET), or cancel-then-stream (POST).
|
||||
|
||||
The LangGraph SDK's ``joinStream`` and ``useStream`` stop button both use
|
||||
``POST`` to this endpoint. When ``action=interrupt`` or ``action=rollback``
|
||||
is present the run is cancelled first; the response then streams any
|
||||
remaining buffered events so the client observes a clean shutdown.
|
||||
"""
|
||||
run_mgr = get_run_manager(request)
|
||||
record = run_mgr.get(run_id)
|
||||
if record is None or record.thread_id != thread_id:
|
||||
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
|
||||
|
||||
# Cancel if an action was requested (stop-button / interrupt flow)
|
||||
if action is not None:
|
||||
cancelled = await run_mgr.cancel(run_id, action=action)
|
||||
if cancelled and wait and record.task is not None:
|
||||
try:
|
||||
await record.task
|
||||
except (asyncio.CancelledError, Exception):
|
||||
pass
|
||||
return Response(status_code=204)
|
||||
|
||||
bridge = get_stream_bridge(request)
|
||||
return StreamingResponse(
|
||||
sse_consumer(bridge, record, request, run_mgr),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
682
deer-flow/backend/app/gateway/routers/threads.py
Normal file
682
deer-flow/backend/app/gateway/routers/threads.py
Normal file
@@ -0,0 +1,682 @@
|
||||
"""Thread CRUD, state, and history endpoints.
|
||||
|
||||
Combines the existing thread-local filesystem cleanup with LangGraph
|
||||
Platform-compatible thread management backed by the checkpointer.
|
||||
|
||||
Channel values returned in state responses are serialized through
|
||||
:func:`deerflow.runtime.serialization.serialize_channel_values` to
|
||||
ensure LangChain message objects are converted to JSON-safe dicts
|
||||
matching the LangGraph Platform wire format expected by the
|
||||
``useStream`` React hook.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_store
|
||||
from deerflow.config.paths import Paths, get_paths
|
||||
from deerflow.runtime import serialize_channel_values
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Store namespace
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
THREADS_NS: tuple[str, ...] = ("threads",)
|
||||
"""Namespace used by the Store for thread metadata records."""
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/threads", tags=["threads"])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Response / request models
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ThreadDeleteResponse(BaseModel):
|
||||
"""Response model for thread cleanup."""
|
||||
|
||||
success: bool
|
||||
message: str
|
||||
|
||||
|
||||
class ThreadResponse(BaseModel):
|
||||
"""Response model for a single thread."""
|
||||
|
||||
thread_id: str = Field(description="Unique thread identifier")
|
||||
status: str = Field(default="idle", description="Thread status: idle, busy, interrupted, error")
|
||||
created_at: str = Field(default="", description="ISO timestamp")
|
||||
updated_at: str = Field(default="", description="ISO timestamp")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Thread metadata")
|
||||
values: dict[str, Any] = Field(default_factory=dict, description="Current state channel values")
|
||||
interrupts: dict[str, Any] = Field(default_factory=dict, description="Pending interrupts")
|
||||
|
||||
|
||||
class ThreadCreateRequest(BaseModel):
|
||||
"""Request body for creating a thread."""
|
||||
|
||||
thread_id: str | None = Field(default=None, description="Optional thread ID (auto-generated if omitted)")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Initial metadata")
|
||||
|
||||
|
||||
class ThreadSearchRequest(BaseModel):
|
||||
"""Request body for searching threads."""
|
||||
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata filter (exact match)")
|
||||
limit: int = Field(default=100, ge=1, le=1000, description="Maximum results")
|
||||
offset: int = Field(default=0, ge=0, description="Pagination offset")
|
||||
status: str | None = Field(default=None, description="Filter by thread status")
|
||||
|
||||
|
||||
class ThreadStateResponse(BaseModel):
|
||||
"""Response model for thread state."""
|
||||
|
||||
values: dict[str, Any] = Field(default_factory=dict, description="Current channel values")
|
||||
next: list[str] = Field(default_factory=list, description="Next tasks to execute")
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Checkpoint metadata")
|
||||
checkpoint: dict[str, Any] = Field(default_factory=dict, description="Checkpoint info")
|
||||
checkpoint_id: str | None = Field(default=None, description="Current checkpoint ID")
|
||||
parent_checkpoint_id: str | None = Field(default=None, description="Parent checkpoint ID")
|
||||
created_at: str | None = Field(default=None, description="Checkpoint timestamp")
|
||||
tasks: list[dict[str, Any]] = Field(default_factory=list, description="Interrupted task details")
|
||||
|
||||
|
||||
class ThreadPatchRequest(BaseModel):
|
||||
"""Request body for patching thread metadata."""
|
||||
|
||||
metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata to merge")
|
||||
|
||||
|
||||
class ThreadStateUpdateRequest(BaseModel):
|
||||
"""Request body for updating thread state (human-in-the-loop resume)."""
|
||||
|
||||
values: dict[str, Any] | None = Field(default=None, description="Channel values to merge")
|
||||
checkpoint_id: str | None = Field(default=None, description="Checkpoint to branch from")
|
||||
checkpoint: dict[str, Any] | None = Field(default=None, description="Full checkpoint object")
|
||||
as_node: str | None = Field(default=None, description="Node identity for the update")
|
||||
|
||||
|
||||
class HistoryEntry(BaseModel):
|
||||
"""Single checkpoint history entry."""
|
||||
|
||||
checkpoint_id: str
|
||||
parent_checkpoint_id: str | None = None
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
values: dict[str, Any] = Field(default_factory=dict)
|
||||
created_at: str | None = None
|
||||
next: list[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class ThreadHistoryRequest(BaseModel):
|
||||
"""Request body for checkpoint history."""
|
||||
|
||||
limit: int = Field(default=10, ge=1, le=100, description="Maximum entries")
|
||||
before: str | None = Field(default=None, description="Cursor for pagination")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _delete_thread_data(thread_id: str, paths: Paths | None = None) -> ThreadDeleteResponse:
|
||||
"""Delete local persisted filesystem data for a thread."""
|
||||
path_manager = paths or get_paths()
|
||||
try:
|
||||
path_manager.delete_thread_dir(thread_id)
|
||||
except ValueError as exc:
|
||||
raise HTTPException(status_code=422, detail=str(exc)) from exc
|
||||
except FileNotFoundError:
|
||||
# Not critical — thread data may not exist on disk
|
||||
logger.debug("No local thread data to delete for %s", thread_id)
|
||||
return ThreadDeleteResponse(success=True, message=f"No local data for {thread_id}")
|
||||
except Exception as exc:
|
||||
logger.exception("Failed to delete thread data for %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to delete local thread data.") from exc
|
||||
|
||||
logger.info("Deleted local thread data for %s", thread_id)
|
||||
return ThreadDeleteResponse(success=True, message=f"Deleted local thread data for {thread_id}")
|
||||
|
||||
|
||||
async def _store_get(store, thread_id: str) -> dict | None:
|
||||
"""Fetch a thread record from the Store; returns ``None`` if absent."""
|
||||
item = await store.aget(THREADS_NS, thread_id)
|
||||
return item.value if item is not None else None
|
||||
|
||||
|
||||
async def _store_put(store, record: dict) -> None:
|
||||
"""Write a thread record to the Store."""
|
||||
await store.aput(THREADS_NS, record["thread_id"], record)
|
||||
|
||||
|
||||
async def _store_upsert(store, thread_id: str, *, metadata: dict | None = None, values: dict | None = None) -> None:
|
||||
"""Create or refresh a thread record in the Store.
|
||||
|
||||
On creation the record is written with ``status="idle"``. On update only
|
||||
``updated_at`` (and optionally ``metadata`` / ``values``) are changed so
|
||||
that existing fields are preserved.
|
||||
|
||||
``values`` carries the agent-state snapshot exposed to the frontend
|
||||
(currently just ``{"title": "..."}``).
|
||||
"""
|
||||
now = time.time()
|
||||
existing = await _store_get(store, thread_id)
|
||||
if existing is None:
|
||||
await _store_put(
|
||||
store,
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
"metadata": metadata or {},
|
||||
"values": values or {},
|
||||
},
|
||||
)
|
||||
else:
|
||||
val = dict(existing)
|
||||
val["updated_at"] = now
|
||||
if metadata:
|
||||
val.setdefault("metadata", {}).update(metadata)
|
||||
if values:
|
||||
val.setdefault("values", {}).update(values)
|
||||
await _store_put(store, val)
|
||||
|
||||
|
||||
def _derive_thread_status(checkpoint_tuple) -> str:
|
||||
"""Derive thread status from checkpoint metadata."""
|
||||
if checkpoint_tuple is None:
|
||||
return "idle"
|
||||
pending_writes = getattr(checkpoint_tuple, "pending_writes", None) or []
|
||||
|
||||
# Check for error in pending writes
|
||||
for pw in pending_writes:
|
||||
if len(pw) >= 2 and pw[1] == "__error__":
|
||||
return "error"
|
||||
|
||||
# Check for pending next tasks (indicates interrupt)
|
||||
tasks = getattr(checkpoint_tuple, "tasks", None)
|
||||
if tasks:
|
||||
return "interrupted"
|
||||
|
||||
return "idle"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Endpoints
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@router.delete("/{thread_id}", response_model=ThreadDeleteResponse)
|
||||
async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteResponse:
|
||||
"""Delete local persisted filesystem data for a thread.
|
||||
|
||||
Cleans DeerFlow-managed thread directories, removes checkpoint data,
|
||||
and removes the thread record from the Store.
|
||||
"""
|
||||
# Clean local filesystem
|
||||
response = _delete_thread_data(thread_id)
|
||||
|
||||
# Remove from Store (best-effort)
|
||||
store = get_store(request)
|
||||
if store is not None:
|
||||
try:
|
||||
await store.adelete(THREADS_NS, thread_id)
|
||||
except Exception:
|
||||
logger.debug("Could not delete store record for thread %s (not critical)", thread_id)
|
||||
|
||||
# Remove checkpoints (best-effort)
|
||||
checkpointer = getattr(request.app.state, "checkpointer", None)
|
||||
if checkpointer is not None:
|
||||
try:
|
||||
if hasattr(checkpointer, "adelete_thread"):
|
||||
await checkpointer.adelete_thread(thread_id)
|
||||
except Exception:
|
||||
logger.debug("Could not delete checkpoints for thread %s (not critical)", thread_id)
|
||||
|
||||
return response
|
||||
|
||||
|
||||
@router.post("", response_model=ThreadResponse)
|
||||
async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadResponse:
|
||||
"""Create a new thread.
|
||||
|
||||
The thread record is written to the Store (for fast listing) and an
|
||||
empty checkpoint is written to the checkpointer (for state reads).
|
||||
Idempotent: returns the existing record when ``thread_id`` already exists.
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
thread_id = body.thread_id or str(uuid.uuid4())
|
||||
now = time.time()
|
||||
|
||||
# Idempotency: return existing record from Store when already present
|
||||
if store is not None:
|
||||
existing_record = await _store_get(store, thread_id)
|
||||
if existing_record is not None:
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=existing_record.get("status", "idle"),
|
||||
created_at=str(existing_record.get("created_at", "")),
|
||||
updated_at=str(existing_record.get("updated_at", "")),
|
||||
metadata=existing_record.get("metadata", {}),
|
||||
)
|
||||
|
||||
# Write thread record to Store
|
||||
if store is not None:
|
||||
try:
|
||||
await _store_put(
|
||||
store,
|
||||
{
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": now,
|
||||
"updated_at": now,
|
||||
"metadata": body.metadata,
|
||||
},
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Failed to write thread %s to store", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to create thread")
|
||||
|
||||
# Write an empty checkpoint so state endpoints work immediately
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
from langgraph.checkpoint.base import empty_checkpoint
|
||||
|
||||
ckpt_metadata = {
|
||||
"step": -1,
|
||||
"source": "input",
|
||||
"writes": None,
|
||||
"parents": {},
|
||||
**body.metadata,
|
||||
"created_at": now,
|
||||
}
|
||||
await checkpointer.aput(config, empty_checkpoint(), ckpt_metadata, {})
|
||||
except Exception:
|
||||
logger.exception("Failed to create checkpoint for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to create thread")
|
||||
|
||||
logger.info("Thread created: %s", thread_id)
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status="idle",
|
||||
created_at=str(now),
|
||||
updated_at=str(now),
|
||||
metadata=body.metadata,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/search", response_model=list[ThreadResponse])
|
||||
async def search_threads(body: ThreadSearchRequest, request: Request) -> list[ThreadResponse]:
|
||||
"""Search and list threads.
|
||||
|
||||
Two-phase approach:
|
||||
|
||||
**Phase 1 — Store (fast path, O(threads))**: returns threads that were
|
||||
created or run through this Gateway. Store records are tiny metadata
|
||||
dicts so fetching all of them at once is cheap.
|
||||
|
||||
**Phase 2 — Checkpointer supplement (lazy migration)**: threads that
|
||||
were created directly by LangGraph Server (and therefore absent from the
|
||||
Store) are discovered here by iterating the shared checkpointer. Any
|
||||
newly found thread is immediately written to the Store so that the next
|
||||
search skips Phase 2 for that thread — the Store converges to a full
|
||||
index over time without a one-shot migration job.
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 1: Store
|
||||
# -----------------------------------------------------------------------
|
||||
merged: dict[str, ThreadResponse] = {}
|
||||
|
||||
if store is not None:
|
||||
try:
|
||||
items = await store.asearch(THREADS_NS, limit=10_000)
|
||||
except Exception:
|
||||
logger.warning("Store search failed — falling back to checkpointer only", exc_info=True)
|
||||
items = []
|
||||
|
||||
for item in items:
|
||||
val = item.value
|
||||
merged[val["thread_id"]] = ThreadResponse(
|
||||
thread_id=val["thread_id"],
|
||||
status=val.get("status", "idle"),
|
||||
created_at=str(val.get("created_at", "")),
|
||||
updated_at=str(val.get("updated_at", "")),
|
||||
metadata=val.get("metadata", {}),
|
||||
values=val.get("values", {}),
|
||||
)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 2: Checkpointer supplement
|
||||
# Discovers threads not yet in the Store (e.g. created by LangGraph
|
||||
# Server) and lazily migrates them so future searches skip this phase.
|
||||
# -----------------------------------------------------------------------
|
||||
try:
|
||||
async for checkpoint_tuple in checkpointer.alist(None):
|
||||
cfg = getattr(checkpoint_tuple, "config", {})
|
||||
thread_id = cfg.get("configurable", {}).get("thread_id")
|
||||
if not thread_id or thread_id in merged:
|
||||
continue
|
||||
|
||||
# Skip sub-graph checkpoints (checkpoint_ns is non-empty for those)
|
||||
if cfg.get("configurable", {}).get("checkpoint_ns", ""):
|
||||
continue
|
||||
|
||||
ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
# Strip LangGraph internal keys from the user-visible metadata dict
|
||||
user_meta = {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")}
|
||||
|
||||
# Extract state values (title) from the checkpoint's channel_values
|
||||
checkpoint_data = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
channel_values = checkpoint_data.get("channel_values", {})
|
||||
ckpt_values = {}
|
||||
if title := channel_values.get("title"):
|
||||
ckpt_values["title"] = title
|
||||
|
||||
thread_resp = ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=_derive_thread_status(checkpoint_tuple),
|
||||
created_at=str(ckpt_meta.get("created_at", "")),
|
||||
updated_at=str(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
|
||||
metadata=user_meta,
|
||||
values=ckpt_values,
|
||||
)
|
||||
merged[thread_id] = thread_resp
|
||||
|
||||
# Lazy migration — write to Store so the next search finds it there
|
||||
if store is not None:
|
||||
try:
|
||||
await _store_upsert(store, thread_id, metadata=user_meta, values=ckpt_values or None)
|
||||
except Exception:
|
||||
logger.debug("Failed to migrate thread %s to store (non-fatal)", thread_id)
|
||||
except Exception:
|
||||
logger.exception("Checkpointer scan failed during thread search")
|
||||
# Don't raise — return whatever was collected from Store + partial scan
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Phase 3: Filter → sort → paginate
|
||||
# -----------------------------------------------------------------------
|
||||
results = list(merged.values())
|
||||
|
||||
if body.metadata:
|
||||
results = [r for r in results if all(r.metadata.get(k) == v for k, v in body.metadata.items())]
|
||||
|
||||
if body.status:
|
||||
results = [r for r in results if r.status == body.status]
|
||||
|
||||
results.sort(key=lambda r: r.updated_at, reverse=True)
|
||||
return results[body.offset : body.offset + body.limit]
|
||||
|
||||
|
||||
@router.patch("/{thread_id}", response_model=ThreadResponse)
|
||||
async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Request) -> ThreadResponse:
|
||||
"""Merge metadata into a thread record."""
|
||||
store = get_store(request)
|
||||
if store is None:
|
||||
raise HTTPException(status_code=503, detail="Store not available")
|
||||
|
||||
record = await _store_get(store, thread_id)
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
now = time.time()
|
||||
updated = dict(record)
|
||||
updated.setdefault("metadata", {}).update(body.metadata)
|
||||
updated["updated_at"] = now
|
||||
|
||||
try:
|
||||
await _store_put(store, updated)
|
||||
except Exception:
|
||||
logger.exception("Failed to patch thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to update thread")
|
||||
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=updated.get("status", "idle"),
|
||||
created_at=str(updated.get("created_at", "")),
|
||||
updated_at=str(now),
|
||||
metadata=updated.get("metadata", {}),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/{thread_id}", response_model=ThreadResponse)
|
||||
async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
|
||||
"""Get thread info.
|
||||
|
||||
Reads metadata from the Store and derives the accurate execution
|
||||
status from the checkpointer. Falls back to the checkpointer alone
|
||||
for threads that pre-date Store adoption (backward compat).
|
||||
"""
|
||||
store = get_store(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
record: dict | None = None
|
||||
if store is not None:
|
||||
record = await _store_get(store, thread_id)
|
||||
|
||||
# Derive accurate status from the checkpointer
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get checkpoint for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread")
|
||||
|
||||
if record is None and checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
# If the thread exists in the checkpointer but not the store (e.g. legacy
|
||||
# data), synthesize a minimal store record from the checkpoint metadata.
|
||||
if record is None and checkpoint_tuple is not None:
|
||||
ckpt_meta = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
record = {
|
||||
"thread_id": thread_id,
|
||||
"status": "idle",
|
||||
"created_at": ckpt_meta.get("created_at", ""),
|
||||
"updated_at": ckpt_meta.get("updated_at", ckpt_meta.get("created_at", "")),
|
||||
"metadata": {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")},
|
||||
}
|
||||
|
||||
if record is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
status = _derive_thread_status(checkpoint_tuple) if checkpoint_tuple is not None else record.get("status", "idle")
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {} if checkpoint_tuple is not None else {}
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
return ThreadResponse(
|
||||
thread_id=thread_id,
|
||||
status=status,
|
||||
created_at=str(record.get("created_at", "")),
|
||||
updated_at=str(record.get("updated_at", "")),
|
||||
metadata=record.get("metadata", {}),
|
||||
values=serialize_channel_values(channel_values),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/{thread_id}/state", response_model=ThreadStateResponse)
|
||||
async def get_thread_state(thread_id: str, request: Request) -> ThreadStateResponse:
|
||||
"""Get the latest state snapshot for a thread.
|
||||
|
||||
Channel values are serialized to ensure LangChain message objects
|
||||
are converted to JSON-safe dicts.
|
||||
"""
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread state")
|
||||
|
||||
if checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
metadata = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
checkpoint_id = None
|
||||
ckpt_config = getattr(checkpoint_tuple, "config", {})
|
||||
if ckpt_config:
|
||||
checkpoint_id = ckpt_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
parent_config = getattr(checkpoint_tuple, "parent_config", None)
|
||||
parent_checkpoint_id = None
|
||||
if parent_config:
|
||||
parent_checkpoint_id = parent_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
tasks_raw = getattr(checkpoint_tuple, "tasks", []) or []
|
||||
next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
|
||||
tasks = [{"id": getattr(t, "id", ""), "name": getattr(t, "name", "")} for t in tasks_raw]
|
||||
|
||||
return ThreadStateResponse(
|
||||
values=serialize_channel_values(channel_values),
|
||||
next=next_tasks,
|
||||
metadata=metadata,
|
||||
checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
|
||||
checkpoint_id=checkpoint_id,
|
||||
parent_checkpoint_id=parent_checkpoint_id,
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
tasks=tasks,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/state", response_model=ThreadStateResponse)
|
||||
async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, request: Request) -> ThreadStateResponse:
|
||||
"""Update thread state (e.g. for human-in-the-loop resume or title rename).
|
||||
|
||||
Writes a new checkpoint that merges *body.values* into the latest
|
||||
channel values, then syncs any updated ``title`` field back to the Store
|
||||
so that ``/threads/search`` reflects the change immediately.
|
||||
"""
|
||||
checkpointer = get_checkpointer(request)
|
||||
store = get_store(request)
|
||||
|
||||
# checkpoint_ns must be present in the config for aput — default to ""
|
||||
# (the root graph namespace). checkpoint_id is optional; omitting it
|
||||
# fetches the latest checkpoint for the thread.
|
||||
read_config: dict[str, Any] = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"checkpoint_ns": "",
|
||||
}
|
||||
}
|
||||
if body.checkpoint_id:
|
||||
read_config["configurable"]["checkpoint_id"] = body.checkpoint_id
|
||||
|
||||
try:
|
||||
checkpoint_tuple = await checkpointer.aget_tuple(read_config)
|
||||
except Exception:
|
||||
logger.exception("Failed to get state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread state")
|
||||
|
||||
if checkpoint_tuple is None:
|
||||
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
|
||||
|
||||
# Work on mutable copies so we don't accidentally mutate cached objects.
|
||||
checkpoint: dict[str, Any] = dict(getattr(checkpoint_tuple, "checkpoint", {}) or {})
|
||||
metadata: dict[str, Any] = dict(getattr(checkpoint_tuple, "metadata", {}) or {})
|
||||
channel_values: dict[str, Any] = dict(checkpoint.get("channel_values", {}))
|
||||
|
||||
if body.values:
|
||||
channel_values.update(body.values)
|
||||
|
||||
checkpoint["channel_values"] = channel_values
|
||||
metadata["updated_at"] = time.time()
|
||||
|
||||
if body.as_node:
|
||||
metadata["source"] = "update"
|
||||
metadata["step"] = metadata.get("step", 0) + 1
|
||||
metadata["writes"] = {body.as_node: body.values}
|
||||
|
||||
# aput requires checkpoint_ns in the config — use the same config used for the
|
||||
# read (which always includes checkpoint_ns=""). Do NOT include checkpoint_id
|
||||
# so that aput generates a fresh checkpoint ID for the new snapshot.
|
||||
write_config: dict[str, Any] = {
|
||||
"configurable": {
|
||||
"thread_id": thread_id,
|
||||
"checkpoint_ns": "",
|
||||
}
|
||||
}
|
||||
try:
|
||||
new_config = await checkpointer.aput(write_config, checkpoint, metadata, {})
|
||||
except Exception:
|
||||
logger.exception("Failed to update state for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to update thread state")
|
||||
|
||||
new_checkpoint_id: str | None = None
|
||||
if isinstance(new_config, dict):
|
||||
new_checkpoint_id = new_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
# Sync title changes to the Store so /threads/search reflects them immediately.
|
||||
if store is not None and body.values and "title" in body.values:
|
||||
try:
|
||||
await _store_upsert(store, thread_id, values={"title": body.values["title"]})
|
||||
except Exception:
|
||||
logger.debug("Failed to sync title to store for thread %s (non-fatal)", thread_id)
|
||||
|
||||
return ThreadStateResponse(
|
||||
values=serialize_channel_values(channel_values),
|
||||
next=[],
|
||||
metadata=metadata,
|
||||
checkpoint_id=new_checkpoint_id,
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/{thread_id}/history", response_model=list[HistoryEntry])
|
||||
async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request: Request) -> list[HistoryEntry]:
|
||||
"""Get checkpoint history for a thread."""
|
||||
checkpointer = get_checkpointer(request)
|
||||
|
||||
config: dict[str, Any] = {"configurable": {"thread_id": thread_id}}
|
||||
if body.before:
|
||||
config["configurable"]["checkpoint_id"] = body.before
|
||||
|
||||
entries: list[HistoryEntry] = []
|
||||
try:
|
||||
async for checkpoint_tuple in checkpointer.alist(config, limit=body.limit):
|
||||
ckpt_config = getattr(checkpoint_tuple, "config", {})
|
||||
parent_config = getattr(checkpoint_tuple, "parent_config", None)
|
||||
metadata = getattr(checkpoint_tuple, "metadata", {}) or {}
|
||||
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
||||
|
||||
checkpoint_id = ckpt_config.get("configurable", {}).get("checkpoint_id", "")
|
||||
parent_id = None
|
||||
if parent_config:
|
||||
parent_id = parent_config.get("configurable", {}).get("checkpoint_id")
|
||||
|
||||
channel_values = checkpoint.get("channel_values", {})
|
||||
|
||||
# Derive next tasks
|
||||
tasks_raw = getattr(checkpoint_tuple, "tasks", []) or []
|
||||
next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
|
||||
|
||||
entries.append(
|
||||
HistoryEntry(
|
||||
checkpoint_id=checkpoint_id,
|
||||
parent_checkpoint_id=parent_id,
|
||||
metadata=metadata,
|
||||
values=serialize_channel_values(channel_values),
|
||||
created_at=str(metadata.get("created_at", "")),
|
||||
next=next_tasks,
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
logger.exception("Failed to get history for thread %s", thread_id)
|
||||
raise HTTPException(status_code=500, detail="Failed to get thread history")
|
||||
|
||||
return entries
|
||||
168
deer-flow/backend/app/gateway/routers/uploads.py
Normal file
168
deer-flow/backend/app/gateway/routers/uploads.py
Normal file
@@ -0,0 +1,168 @@
|
||||
"""Upload router for handling file uploads."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import stat
|
||||
|
||||
from fastapi import APIRouter, File, HTTPException, UploadFile
|
||||
from pydantic import BaseModel
|
||||
|
||||
from deerflow.config.paths import get_paths
|
||||
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
|
||||
from deerflow.uploads.manager import (
|
||||
PathTraversalError,
|
||||
delete_file_safe,
|
||||
enrich_file_listing,
|
||||
ensure_uploads_dir,
|
||||
get_uploads_dir,
|
||||
list_files_in_dir,
|
||||
normalize_filename,
|
||||
upload_artifact_url,
|
||||
upload_virtual_path,
|
||||
)
|
||||
from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
|
||||
|
||||
|
||||
class UploadResponse(BaseModel):
|
||||
"""Response model for file upload."""
|
||||
|
||||
success: bool
|
||||
files: list[dict[str, str]]
|
||||
message: str
|
||||
|
||||
|
||||
def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
|
||||
"""Ensure uploaded files remain writable when mounted into non-local sandboxes.
|
||||
|
||||
In AIO sandbox mode, the gateway writes the authoritative host-side file
|
||||
first, then the sandbox runtime may rewrite the same mounted path. Granting
|
||||
world-writable access here prevents permission mismatches between the
|
||||
gateway user and the sandbox runtime user.
|
||||
"""
|
||||
file_stat = os.lstat(file_path)
|
||||
if stat.S_ISLNK(file_stat.st_mode):
|
||||
logger.warning("Skipping sandbox chmod for symlinked upload path: %s", file_path)
|
||||
return
|
||||
|
||||
writable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IWUSR | stat.S_IWGRP | stat.S_IWOTH
|
||||
chmod_kwargs = {"follow_symlinks": False} if os.chmod in os.supports_follow_symlinks else {}
|
||||
os.chmod(file_path, writable_mode, **chmod_kwargs)
|
||||
|
||||
|
||||
@router.post("", response_model=UploadResponse)
|
||||
async def upload_files(
|
||||
thread_id: str,
|
||||
files: list[UploadFile] = File(...),
|
||||
) -> UploadResponse:
|
||||
"""Upload multiple files to a thread's uploads directory."""
|
||||
if not files:
|
||||
raise HTTPException(status_code=400, detail="No files provided")
|
||||
|
||||
try:
|
||||
uploads_dir = ensure_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
|
||||
uploaded_files = []
|
||||
|
||||
sandbox_provider = get_sandbox_provider()
|
||||
sandbox_id = sandbox_provider.acquire(thread_id)
|
||||
sandbox = sandbox_provider.get(sandbox_id)
|
||||
|
||||
for file in files:
|
||||
if not file.filename:
|
||||
continue
|
||||
|
||||
try:
|
||||
safe_filename = normalize_filename(file.filename)
|
||||
except ValueError:
|
||||
logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
|
||||
continue
|
||||
|
||||
try:
|
||||
content = await file.read()
|
||||
file_path = uploads_dir / safe_filename
|
||||
file_path.write_bytes(content)
|
||||
|
||||
virtual_path = upload_virtual_path(safe_filename)
|
||||
|
||||
if sandbox_id != "local":
|
||||
_make_file_sandbox_writable(file_path)
|
||||
sandbox.update_file(virtual_path, content)
|
||||
|
||||
file_info = {
|
||||
"filename": safe_filename,
|
||||
"size": str(len(content)),
|
||||
"path": str(sandbox_uploads / safe_filename),
|
||||
"virtual_path": virtual_path,
|
||||
"artifact_url": upload_artifact_url(thread_id, safe_filename),
|
||||
}
|
||||
|
||||
logger.info(f"Saved file: {safe_filename} ({len(content)} bytes) to {file_info['path']}")
|
||||
|
||||
file_ext = file_path.suffix.lower()
|
||||
if file_ext in CONVERTIBLE_EXTENSIONS:
|
||||
md_path = await convert_file_to_markdown(file_path)
|
||||
if md_path:
|
||||
md_virtual_path = upload_virtual_path(md_path.name)
|
||||
|
||||
if sandbox_id != "local":
|
||||
_make_file_sandbox_writable(md_path)
|
||||
sandbox.update_file(md_virtual_path, md_path.read_bytes())
|
||||
|
||||
file_info["markdown_file"] = md_path.name
|
||||
file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
|
||||
file_info["markdown_virtual_path"] = md_virtual_path
|
||||
file_info["markdown_artifact_url"] = upload_artifact_url(thread_id, md_path.name)
|
||||
|
||||
uploaded_files.append(file_info)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to upload {file.filename}: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
|
||||
|
||||
return UploadResponse(
|
||||
success=True,
|
||||
files=uploaded_files,
|
||||
message=f"Successfully uploaded {len(uploaded_files)} file(s)",
|
||||
)
|
||||
|
||||
|
||||
@router.get("/list", response_model=dict)
|
||||
async def list_uploaded_files(thread_id: str) -> dict:
|
||||
"""List all files in a thread's uploads directory."""
|
||||
try:
|
||||
uploads_dir = get_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
result = list_files_in_dir(uploads_dir)
|
||||
enrich_file_listing(result, thread_id)
|
||||
|
||||
# Gateway additionally includes the sandbox-relative path.
|
||||
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
|
||||
for f in result["files"]:
|
||||
f["path"] = str(sandbox_uploads / f["filename"])
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@router.delete("/{filename}")
|
||||
async def delete_uploaded_file(thread_id: str, filename: str) -> dict:
|
||||
"""Delete a file from a thread's uploads directory."""
|
||||
try:
|
||||
uploads_dir = get_uploads_dir(thread_id)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
try:
|
||||
return delete_file_safe(uploads_dir, filename, convertible_extensions=CONVERTIBLE_EXTENSIONS)
|
||||
except FileNotFoundError:
|
||||
raise HTTPException(status_code=404, detail=f"File not found: {filename}")
|
||||
except PathTraversalError:
|
||||
raise HTTPException(status_code=400, detail="Invalid path")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete {filename}: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to delete {filename}: {str(e)}")
|
||||
367
deer-flow/backend/app/gateway/services.py
Normal file
367
deer-flow/backend/app/gateway/services.py
Normal file
@@ -0,0 +1,367 @@
|
||||
"""Run lifecycle service layer.
|
||||
|
||||
Centralizes the business logic for creating runs, formatting SSE
|
||||
frames, and consuming stream bridge events. Router modules
|
||||
(``thread_runs``, ``runs``) are thin HTTP handlers that delegate here.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
from fastapi import HTTPException, Request
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from app.gateway.deps import get_checkpointer, get_run_manager, get_store, get_stream_bridge
|
||||
from deerflow.runtime import (
|
||||
END_SENTINEL,
|
||||
HEARTBEAT_SENTINEL,
|
||||
ConflictError,
|
||||
DisconnectMode,
|
||||
RunManager,
|
||||
RunRecord,
|
||||
RunStatus,
|
||||
StreamBridge,
|
||||
UnsupportedStrategyError,
|
||||
run_agent,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SSE formatting
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def format_sse(event: str, data: Any, *, event_id: str | None = None) -> str:
|
||||
"""Format a single SSE frame.
|
||||
|
||||
Field order: ``event:`` -> ``data:`` -> ``id:`` (optional) -> blank line.
|
||||
This matches the LangGraph Platform wire format consumed by the
|
||||
``useStream`` React hook and the Python ``langgraph-sdk`` SSE decoder.
|
||||
"""
|
||||
payload = json.dumps(data, default=str, ensure_ascii=False)
|
||||
parts = [f"event: {event}", f"data: {payload}"]
|
||||
if event_id:
|
||||
parts.append(f"id: {event_id}")
|
||||
parts.append("")
|
||||
parts.append("")
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Input / config helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def normalize_stream_modes(raw: list[str] | str | None) -> list[str]:
|
||||
"""Normalize the stream_mode parameter to a list.
|
||||
|
||||
Default matches what ``useStream`` expects: values + messages-tuple.
|
||||
"""
|
||||
if raw is None:
|
||||
return ["values"]
|
||||
if isinstance(raw, str):
|
||||
return [raw]
|
||||
return raw if raw else ["values"]
|
||||
|
||||
|
||||
def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
|
||||
"""Convert LangGraph Platform input format to LangChain state dict."""
|
||||
if raw_input is None:
|
||||
return {}
|
||||
messages = raw_input.get("messages")
|
||||
if messages and isinstance(messages, list):
|
||||
converted = []
|
||||
for msg in messages:
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get("role", msg.get("type", "user"))
|
||||
content = msg.get("content", "")
|
||||
if role in ("user", "human"):
|
||||
converted.append(HumanMessage(content=content))
|
||||
else:
|
||||
# TODO: handle other message types (system, ai, tool)
|
||||
converted.append(HumanMessage(content=content))
|
||||
else:
|
||||
converted.append(msg)
|
||||
return {**raw_input, "messages": converted}
|
||||
return raw_input
|
||||
|
||||
|
||||
_DEFAULT_ASSISTANT_ID = "lead_agent"
|
||||
|
||||
|
||||
def resolve_agent_factory(assistant_id: str | None):
|
||||
"""Resolve the agent factory callable from config.
|
||||
|
||||
Custom agents are implemented as ``lead_agent`` + an ``agent_name``
|
||||
injected into ``configurable`` — see :func:`build_run_config`. All
|
||||
``assistant_id`` values therefore map to the same factory; the routing
|
||||
happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
|
||||
"""
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
|
||||
return make_lead_agent
|
||||
|
||||
|
||||
def build_run_config(
|
||||
thread_id: str,
|
||||
request_config: dict[str, Any] | None,
|
||||
metadata: dict[str, Any] | None,
|
||||
*,
|
||||
assistant_id: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Build a RunnableConfig dict for the agent.
|
||||
|
||||
When *assistant_id* refers to a custom agent (anything other than
|
||||
``"lead_agent"`` / ``None``), the name is forwarded as
|
||||
``configurable["agent_name"]``. ``make_lead_agent`` reads this key to
|
||||
load the matching ``agents/<name>/SOUL.md`` and per-agent config —
|
||||
without it the agent silently runs as the default lead agent.
|
||||
|
||||
This mirrors the channel manager's ``_resolve_run_params`` logic so that
|
||||
the LangGraph Platform-compatible HTTP API and the IM channel path behave
|
||||
identically.
|
||||
"""
|
||||
config: dict[str, Any] = {"recursion_limit": 100}
|
||||
if request_config:
|
||||
# LangGraph >= 0.6.0 introduced ``context`` as the preferred way to
|
||||
# pass thread-level data and rejects requests that include both
|
||||
# ``configurable`` and ``context``. If the caller already sends
|
||||
# ``context``, honour it and skip our own ``configurable`` dict.
|
||||
if "context" in request_config:
|
||||
if "configurable" in request_config:
|
||||
logger.warning(
|
||||
"build_run_config: client sent both 'context' and 'configurable'; preferring 'context' (LangGraph >= 0.6.0). thread_id=%s, caller_configurable keys=%s",
|
||||
thread_id,
|
||||
list(request_config.get("configurable", {}).keys()),
|
||||
)
|
||||
config["context"] = request_config["context"]
|
||||
else:
|
||||
configurable = {"thread_id": thread_id}
|
||||
configurable.update(request_config.get("configurable", {}))
|
||||
config["configurable"] = configurable
|
||||
for k, v in request_config.items():
|
||||
if k not in ("configurable", "context"):
|
||||
config[k] = v
|
||||
else:
|
||||
config["configurable"] = {"thread_id": thread_id}
|
||||
|
||||
# Inject custom agent name when the caller specified a non-default assistant.
|
||||
# Honour an explicit configurable["agent_name"] in the request if already set.
|
||||
if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
|
||||
if "agent_name" not in config["configurable"]:
|
||||
normalized = assistant_id.strip().lower().replace("_", "-")
|
||||
if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
|
||||
raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
|
||||
config["configurable"]["agent_name"] = normalized
|
||||
if metadata:
|
||||
config.setdefault("metadata", {}).update(metadata)
|
||||
return config
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Run lifecycle
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _upsert_thread_in_store(store, thread_id: str, metadata: dict | None) -> None:
|
||||
"""Create or refresh the thread record in the Store.
|
||||
|
||||
Called from :func:`start_run` so that threads created via the stateless
|
||||
``/runs/stream`` endpoint (which never calls ``POST /threads``) still
|
||||
appear in ``/threads/search`` results.
|
||||
"""
|
||||
# Deferred import to avoid circular import with the threads router module.
|
||||
from app.gateway.routers.threads import _store_upsert
|
||||
|
||||
try:
|
||||
await _store_upsert(store, thread_id, metadata=metadata)
|
||||
except Exception:
|
||||
logger.warning("Failed to upsert thread %s in store (non-fatal)", thread_id)
|
||||
|
||||
|
||||
async def _sync_thread_title_after_run(
|
||||
run_task: asyncio.Task,
|
||||
thread_id: str,
|
||||
checkpointer: Any,
|
||||
store: Any,
|
||||
) -> None:
|
||||
"""Wait for *run_task* to finish, then persist the generated title to the Store.
|
||||
|
||||
TitleMiddleware writes the generated title to the LangGraph agent state
|
||||
(checkpointer) but the Gateway's Store record is not updated automatically.
|
||||
This coroutine closes that gap by reading the final checkpoint after the
|
||||
run completes and syncing ``values.title`` into the Store record so that
|
||||
subsequent ``/threads/search`` responses include the correct title.
|
||||
|
||||
Runs as a fire-and-forget :func:`asyncio.create_task`; failures are
|
||||
logged at DEBUG level and never propagate.
|
||||
"""
|
||||
# Wait for the background run task to complete (any outcome).
|
||||
# asyncio.wait does not propagate task exceptions — it just returns
|
||||
# when the task is done, cancelled, or failed.
|
||||
await asyncio.wait({run_task})
|
||||
|
||||
# Deferred import to avoid circular import with the threads router module.
|
||||
from app.gateway.routers.threads import _store_get, _store_put
|
||||
|
||||
try:
|
||||
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
ckpt_tuple = await checkpointer.aget_tuple(ckpt_config)
|
||||
if ckpt_tuple is None:
|
||||
return
|
||||
|
||||
channel_values = ckpt_tuple.checkpoint.get("channel_values", {})
|
||||
title = channel_values.get("title")
|
||||
if not title:
|
||||
return
|
||||
|
||||
existing = await _store_get(store, thread_id)
|
||||
if existing is None:
|
||||
return
|
||||
|
||||
updated = dict(existing)
|
||||
updated.setdefault("values", {})["title"] = title
|
||||
updated["updated_at"] = time.time()
|
||||
await _store_put(store, updated)
|
||||
logger.debug("Synced title %r for thread %s", title, thread_id)
|
||||
except Exception:
|
||||
logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id, exc_info=True)
|
||||
|
||||
|
||||
async def start_run(
|
||||
body: Any,
|
||||
thread_id: str,
|
||||
request: Request,
|
||||
) -> RunRecord:
|
||||
"""Create a RunRecord and launch the background agent task.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
body : RunCreateRequest
|
||||
The validated request body (typed as Any to avoid circular import
|
||||
with the router module that defines the Pydantic model).
|
||||
thread_id : str
|
||||
Target thread.
|
||||
request : Request
|
||||
FastAPI request — used to retrieve singletons from ``app.state``.
|
||||
"""
|
||||
bridge = get_stream_bridge(request)
|
||||
run_mgr = get_run_manager(request)
|
||||
checkpointer = get_checkpointer(request)
|
||||
store = get_store(request)
|
||||
|
||||
disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
|
||||
|
||||
try:
|
||||
record = await run_mgr.create_or_reject(
|
||||
thread_id,
|
||||
body.assistant_id,
|
||||
on_disconnect=disconnect,
|
||||
metadata=body.metadata or {},
|
||||
kwargs={"input": body.input, "config": body.config},
|
||||
multitask_strategy=body.multitask_strategy,
|
||||
)
|
||||
except ConflictError as exc:
|
||||
raise HTTPException(status_code=409, detail=str(exc)) from exc
|
||||
except UnsupportedStrategyError as exc:
|
||||
raise HTTPException(status_code=501, detail=str(exc)) from exc
|
||||
|
||||
# Ensure the thread is visible in /threads/search, even for threads that
|
||||
# were never explicitly created via POST /threads (e.g. stateless runs).
|
||||
store = get_store(request)
|
||||
if store is not None:
|
||||
await _upsert_thread_in_store(store, thread_id, body.metadata)
|
||||
|
||||
agent_factory = resolve_agent_factory(body.assistant_id)
|
||||
graph_input = normalize_input(body.input)
|
||||
config = build_run_config(thread_id, body.config, body.metadata, assistant_id=body.assistant_id)
|
||||
|
||||
# Merge DeerFlow-specific context overrides into configurable.
|
||||
# The ``context`` field is a custom extension for the langgraph-compat layer
|
||||
# that carries agent configuration (model_name, thinking_enabled, etc.).
|
||||
# Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
|
||||
context = getattr(body, "context", None)
|
||||
if context:
|
||||
_CONTEXT_CONFIGURABLE_KEYS = {
|
||||
"model_name",
|
||||
"mode",
|
||||
"thinking_enabled",
|
||||
"reasoning_effort",
|
||||
"is_plan_mode",
|
||||
"subagent_enabled",
|
||||
"max_concurrent_subagents",
|
||||
}
|
||||
configurable = config.setdefault("configurable", {})
|
||||
for key in _CONTEXT_CONFIGURABLE_KEYS:
|
||||
if key in context:
|
||||
configurable.setdefault(key, context[key])
|
||||
|
||||
stream_modes = normalize_stream_modes(body.stream_mode)
|
||||
|
||||
task = asyncio.create_task(
|
||||
run_agent(
|
||||
bridge,
|
||||
run_mgr,
|
||||
record,
|
||||
checkpointer=checkpointer,
|
||||
store=store,
|
||||
agent_factory=agent_factory,
|
||||
graph_input=graph_input,
|
||||
config=config,
|
||||
stream_modes=stream_modes,
|
||||
stream_subgraphs=body.stream_subgraphs,
|
||||
interrupt_before=body.interrupt_before,
|
||||
interrupt_after=body.interrupt_after,
|
||||
)
|
||||
)
|
||||
record.task = task
|
||||
|
||||
# After the run completes, sync the title generated by TitleMiddleware from
|
||||
# the checkpointer into the Store record so that /threads/search returns the
|
||||
# correct title instead of an empty values dict.
|
||||
if store is not None:
|
||||
asyncio.create_task(_sync_thread_title_after_run(task, thread_id, checkpointer, store))
|
||||
|
||||
return record
|
||||
|
||||
|
||||
async def sse_consumer(
|
||||
bridge: StreamBridge,
|
||||
record: RunRecord,
|
||||
request: Request,
|
||||
run_mgr: RunManager,
|
||||
):
|
||||
"""Async generator that yields SSE frames from the bridge.
|
||||
|
||||
The ``finally`` block implements ``on_disconnect`` semantics:
|
||||
- ``cancel``: abort the background task on client disconnect.
|
||||
- ``continue``: let the task run; events are discarded.
|
||||
"""
|
||||
last_event_id = request.headers.get("Last-Event-ID")
|
||||
try:
|
||||
async for entry in bridge.subscribe(record.run_id, last_event_id=last_event_id):
|
||||
if await request.is_disconnected():
|
||||
break
|
||||
|
||||
if entry is HEARTBEAT_SENTINEL:
|
||||
yield ": heartbeat\n\n"
|
||||
continue
|
||||
|
||||
if entry is END_SENTINEL:
|
||||
yield format_sse("end", None, event_id=entry.id or None)
|
||||
return
|
||||
|
||||
yield format_sse(entry.event, entry.data, event_id=entry.id or None)
|
||||
|
||||
finally:
|
||||
if record.status in (RunStatus.pending, RunStatus.running):
|
||||
if record.on_disconnect == DisconnectMode.cancel:
|
||||
await run_mgr.cancel(record.run_id)
|
||||
91
deer-flow/backend/debug.py
Normal file
91
deer-flow/backend/debug.py
Normal file
@@ -0,0 +1,91 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
Debug script for lead_agent.
|
||||
Run this file directly in VS Code with breakpoints.
|
||||
|
||||
Requirements:
|
||||
Run with `uv run` from the backend/ directory so that the uv workspace
|
||||
resolves deerflow-harness and app packages correctly:
|
||||
|
||||
cd backend && PYTHONPATH=. uv run python debug.py
|
||||
|
||||
Usage:
|
||||
1. Set breakpoints in agent.py or other files
|
||||
2. Press F5 or use "Run and Debug" panel
|
||||
3. Input messages in the terminal to interact with the agent
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from deerflow.agents import make_lead_agent
|
||||
|
||||
load_dotenv()
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
# Initialize MCP tools at startup
|
||||
try:
|
||||
from deerflow.mcp import initialize_mcp_tools
|
||||
|
||||
await initialize_mcp_tools()
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to initialize MCP tools: {e}")
|
||||
|
||||
# Create agent with default config
|
||||
config = {
|
||||
"configurable": {
|
||||
"thread_id": "debug-thread-001",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": True,
|
||||
# Uncomment to use a specific model
|
||||
"model_name": "kimi-k2.5",
|
||||
}
|
||||
}
|
||||
|
||||
agent = make_lead_agent(config)
|
||||
|
||||
print("=" * 50)
|
||||
print("Lead Agent Debug Mode")
|
||||
print("Type 'quit' or 'exit' to stop")
|
||||
print("=" * 50)
|
||||
|
||||
while True:
|
||||
try:
|
||||
user_input = input("\nYou: ").strip()
|
||||
if not user_input:
|
||||
continue
|
||||
if user_input.lower() in ("quit", "exit"):
|
||||
print("Goodbye!")
|
||||
break
|
||||
|
||||
# Invoke the agent
|
||||
state = {"messages": [HumanMessage(content=user_input)]}
|
||||
result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
|
||||
|
||||
# Print the response
|
||||
if result.get("messages"):
|
||||
last_message = result["messages"][-1]
|
||||
print(f"\nAgent: {last_message.content}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\nInterrupted. Goodbye!")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"\nError: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
655
deer-flow/backend/docs/API.md
Normal file
655
deer-flow/backend/docs/API.md
Normal file
@@ -0,0 +1,655 @@
|
||||
# API Reference
|
||||
|
||||
This document provides a complete reference for the DeerFlow backend APIs.
|
||||
|
||||
## Overview
|
||||
|
||||
DeerFlow backend exposes two sets of APIs:
|
||||
|
||||
1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
|
||||
2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)
|
||||
|
||||
All APIs are accessed through the Nginx reverse proxy at port 2026.
|
||||
|
||||
## LangGraph API
|
||||
|
||||
Base URL: `/api/langgraph`
|
||||
|
||||
The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
|
||||
|
||||
### Threads
|
||||
|
||||
#### Create Thread
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"metadata": {}
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"thread_id": "abc123",
|
||||
"created_at": "2024-01-15T10:30:00Z",
|
||||
"metadata": {}
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Thread State
|
||||
|
||||
```http
|
||||
GET /api/langgraph/threads/{thread_id}/state
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"values": {
|
||||
"messages": [...],
|
||||
"sandbox": {...},
|
||||
"artifacts": [...],
|
||||
"thread_data": {...},
|
||||
"title": "Conversation Title"
|
||||
},
|
||||
"next": [],
|
||||
"config": {...}
|
||||
}
|
||||
```
|
||||
|
||||
### Runs
|
||||
|
||||
#### Create Run
|
||||
|
||||
Execute the agent with input.
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads/{thread_id}/runs
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hello, can you help me?"
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {
|
||||
"recursion_limit": 100,
|
||||
"configurable": {
|
||||
"model_name": "gpt-4",
|
||||
"thinking_enabled": false,
|
||||
"is_plan_mode": false
|
||||
}
|
||||
},
|
||||
"stream_mode": ["values", "messages-tuple", "custom"]
|
||||
}
|
||||
```
|
||||
|
||||
**Stream Mode Compatibility:**
|
||||
- Use: `values`, `messages-tuple`, `custom`, `updates`, `events`, `debug`, `tasks`, `checkpoints`
|
||||
- Do not use: `tools` (deprecated/invalid in current `langgraph-api` and will trigger schema validation errors)
|
||||
|
||||
**Recursion Limit:**
|
||||
|
||||
`config.recursion_limit` caps the number of graph steps LangGraph will execute
|
||||
in a single run. The `/api/langgraph/*` endpoints go straight to the LangGraph
|
||||
server and therefore inherit LangGraph's native default of **25**, which is
|
||||
too low for plan-mode or subagent-heavy runs — the agent typically errors out
|
||||
with `GraphRecursionError` after the first round of subagent results comes
|
||||
back, before the lead agent can synthesize the final answer.
|
||||
|
||||
DeerFlow's own Gateway and IM-channel paths mitigate this by defaulting to
|
||||
`100` in `build_run_config` (see `backend/app/gateway/services.py`), but
|
||||
clients calling the LangGraph API directly must set `recursion_limit`
|
||||
explicitly in the request body. `100` matches the Gateway default and is a
|
||||
safe starting point; increase it if you run deeply nested subagent graphs.
|
||||
|
||||
**Configurable Options:**
|
||||
- `model_name` (string): Override the default model
|
||||
- `thinking_enabled` (boolean): Enable extended thinking for supported models
|
||||
- `is_plan_mode` (boolean): Enable TodoList middleware for task tracking
|
||||
|
||||
**Response:** Server-Sent Events (SSE) stream
|
||||
|
||||
```
|
||||
event: values
|
||||
data: {"messages": [...], "title": "..."}
|
||||
|
||||
event: messages
|
||||
data: {"content": "Hello! I'd be happy to help.", "role": "assistant"}
|
||||
|
||||
event: end
|
||||
data: {}
|
||||
```
|
||||
|
||||
#### Get Run History
|
||||
|
||||
```http
|
||||
GET /api/langgraph/threads/{thread_id}/runs
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"runs": [
|
||||
{
|
||||
"run_id": "run123",
|
||||
"status": "success",
|
||||
"created_at": "2024-01-15T10:30:00Z"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Stream Run
|
||||
|
||||
Stream responses in real-time.
|
||||
|
||||
```http
|
||||
POST /api/langgraph/threads/{thread_id}/runs/stream
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
Same request body as Create Run. Returns SSE stream.
|
||||
|
||||
---
|
||||
|
||||
## Gateway API
|
||||
|
||||
Base URL: `/api`
|
||||
|
||||
### Models
|
||||
|
||||
#### List Models
|
||||
|
||||
Get all available LLM models from configuration.
|
||||
|
||||
```http
|
||||
GET /api/models
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"models": [
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
},
|
||||
{
|
||||
"name": "claude-3-opus",
|
||||
"display_name": "Claude 3 Opus",
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
},
|
||||
{
|
||||
"name": "deepseek-v3",
|
||||
"display_name": "DeepSeek V3",
|
||||
"supports_thinking": true,
|
||||
"supports_vision": false
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Model Details
|
||||
|
||||
```http
|
||||
GET /api/models/{model_name}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"name": "gpt-4",
|
||||
"display_name": "GPT-4",
|
||||
"model": "gpt-4",
|
||||
"max_tokens": 4096,
|
||||
"supports_thinking": false,
|
||||
"supports_vision": true
|
||||
}
|
||||
```
|
||||
|
||||
### MCP Configuration
|
||||
|
||||
#### Get MCP Config
|
||||
|
||||
Get current MCP server configurations.
|
||||
|
||||
```http
|
||||
GET /api/mcp/config
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {
|
||||
"GITHUB_TOKEN": "***"
|
||||
},
|
||||
"description": "GitHub operations"
|
||||
},
|
||||
"filesystem": {
|
||||
"enabled": false,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-filesystem"],
|
||||
"description": "File system access"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Update MCP Config
|
||||
|
||||
Update MCP server configurations.
|
||||
|
||||
```http
|
||||
PUT /api/mcp/config
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"github": {
|
||||
"enabled": true,
|
||||
"type": "stdio",
|
||||
"command": "npx",
|
||||
"args": ["-y", "@modelcontextprotocol/server-github"],
|
||||
"env": {
|
||||
"GITHUB_TOKEN": "$GITHUB_TOKEN"
|
||||
},
|
||||
"description": "GitHub operations"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "MCP configuration updated"
|
||||
}
|
||||
```
|
||||
|
||||
### Skills
|
||||
|
||||
#### List Skills
|
||||
|
||||
Get all available skills.
|
||||
|
||||
```http
|
||||
GET /api/skills
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"skills": [
|
||||
{
|
||||
"name": "pdf-processing",
|
||||
"display_name": "PDF Processing",
|
||||
"description": "Handle PDF documents efficiently",
|
||||
"enabled": true,
|
||||
"license": "MIT",
|
||||
"path": "public/pdf-processing"
|
||||
},
|
||||
{
|
||||
"name": "frontend-design",
|
||||
"display_name": "Frontend Design",
|
||||
"description": "Design and build frontend interfaces",
|
||||
"enabled": false,
|
||||
"license": "MIT",
|
||||
"path": "public/frontend-design"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### Get Skill Details
|
||||
|
||||
```http
|
||||
GET /api/skills/{skill_name}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"name": "pdf-processing",
|
||||
"display_name": "PDF Processing",
|
||||
"description": "Handle PDF documents efficiently",
|
||||
"enabled": true,
|
||||
"license": "MIT",
|
||||
"path": "public/pdf-processing",
|
||||
"allowed_tools": ["read_file", "write_file", "bash"],
|
||||
"content": "# PDF Processing\n\nInstructions for the agent..."
|
||||
}
|
||||
```
|
||||
|
||||
#### Enable Skill
|
||||
|
||||
```http
|
||||
POST /api/skills/{skill_name}/enable
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'pdf-processing' enabled"
|
||||
}
|
||||
```
|
||||
|
||||
#### Disable Skill
|
||||
|
||||
```http
|
||||
POST /api/skills/{skill_name}/disable
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'pdf-processing' disabled"
|
||||
}
|
||||
```
|
||||
|
||||
#### Install Skill
|
||||
|
||||
Install a skill from a `.skill` file.
|
||||
|
||||
```http
|
||||
POST /api/skills/install
|
||||
Content-Type: multipart/form-data
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
- `file`: The `.skill` file to install
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Skill 'my-skill' installed successfully",
|
||||
"skill": {
|
||||
"name": "my-skill",
|
||||
"display_name": "My Skill",
|
||||
"path": "custom/my-skill"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### File Uploads
|
||||
|
||||
#### Upload Files
|
||||
|
||||
Upload one or more files to a thread.
|
||||
|
||||
```http
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
Content-Type: multipart/form-data
|
||||
```
|
||||
|
||||
**Request Body:**
|
||||
- `files`: One or more files to upload
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"markdown_file": "document.md",
|
||||
"markdown_path": ".deer-flow/threads/abc123/user-data/uploads/document.md",
|
||||
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
|
||||
"markdown_artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.md"
|
||||
}
|
||||
],
|
||||
"message": "Successfully uploaded 1 file(s)"
|
||||
}
|
||||
```
|
||||
|
||||
**Supported Document Formats** (auto-converted to Markdown):
|
||||
- PDF (`.pdf`)
|
||||
- PowerPoint (`.ppt`, `.pptx`)
|
||||
- Excel (`.xls`, `.xlsx`)
|
||||
- Word (`.doc`, `.docx`)
|
||||
|
||||
#### List Uploaded Files
|
||||
|
||||
```http
|
||||
GET /api/threads/{thread_id}/uploads/list
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"extension": ".pdf",
|
||||
"modified": 1705997600.0
|
||||
}
|
||||
],
|
||||
"count": 1
|
||||
}
|
||||
```
|
||||
|
||||
#### Delete File
|
||||
|
||||
```http
|
||||
DELETE /api/threads/{thread_id}/uploads/{filename}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted document.pdf"
|
||||
}
|
||||
```
|
||||
|
||||
### Thread Cleanup
|
||||
|
||||
Remove DeerFlow-managed local thread files under `.deer-flow/threads/{thread_id}` after the LangGraph thread itself has been deleted.
|
||||
|
||||
```http
|
||||
DELETE /api/threads/{thread_id}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted local thread data for abc123"
|
||||
}
|
||||
```
|
||||
|
||||
**Error behavior:**
|
||||
- `422` for invalid thread IDs
|
||||
- `500` returns a generic `{"detail": "Failed to delete local thread data."}` response while full exception details stay in server logs
|
||||
|
||||
### Artifacts
|
||||
|
||||
#### Get Artifact
|
||||
|
||||
Download or view an artifact generated by the agent.
|
||||
|
||||
```http
|
||||
GET /api/threads/{thread_id}/artifacts/{path}
|
||||
```
|
||||
|
||||
**Path Examples:**
|
||||
- `/api/threads/abc123/artifacts/mnt/user-data/outputs/result.txt`
|
||||
- `/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf`
|
||||
|
||||
**Query Parameters:**
|
||||
- `download` (boolean): If `true`, force download with Content-Disposition header
|
||||
|
||||
**Response:** File content with appropriate Content-Type
|
||||
|
||||
---
|
||||
|
||||
## Error Responses
|
||||
|
||||
All APIs return errors in a consistent format:
|
||||
|
||||
```json
|
||||
{
|
||||
"detail": "Error message describing what went wrong"
|
||||
}
|
||||
```
|
||||
|
||||
**HTTP Status Codes:**
|
||||
- `400` - Bad Request: Invalid input
|
||||
- `404` - Not Found: Resource not found
|
||||
- `422` - Validation Error: Request validation failed
|
||||
- `500` - Internal Server Error: Server-side error
|
||||
|
||||
---
|
||||
|
||||
## Authentication
|
||||
|
||||
Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
|
||||
|
||||
Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
|
||||
|
||||
For production deployments, it is recommended to:
|
||||
1. Use Nginx for basic auth or OAuth integration
|
||||
2. Deploy behind a VPN or private network
|
||||
3. Implement custom authentication middleware
|
||||
|
||||
---
|
||||
|
||||
## Rate Limiting
|
||||
|
||||
No rate limiting is implemented by default. For production deployments, configure rate limiting in Nginx:
|
||||
|
||||
```nginx
|
||||
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
|
||||
|
||||
location /api/ {
|
||||
limit_req zone=api burst=20 nodelay;
|
||||
proxy_pass http://backend;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## WebSocket Support
|
||||
|
||||
The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
|
||||
|
||||
```
|
||||
ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## SDK Usage
|
||||
|
||||
### Python (LangGraph SDK)
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url="http://localhost:2026/api/langgraph")
|
||||
|
||||
# Create thread
|
||||
thread = await client.threads.create()
|
||||
|
||||
# Run agent
|
||||
async for event in client.runs.stream(
|
||||
thread["thread_id"],
|
||||
"lead_agent",
|
||||
input={"messages": [{"role": "user", "content": "Hello"}]},
|
||||
config={"configurable": {"model_name": "gpt-4"}},
|
||||
stream_mode=["values", "messages-tuple", "custom"],
|
||||
):
|
||||
print(event)
|
||||
```
|
||||
|
||||
### JavaScript/TypeScript
|
||||
|
||||
```typescript
|
||||
// Using fetch for Gateway API
|
||||
const response = await fetch('/api/models');
|
||||
const data = await response.json();
|
||||
console.log(data.models);
|
||||
|
||||
// Using EventSource for streaming
|
||||
const eventSource = new EventSource(
|
||||
`/api/langgraph/threads/${threadId}/runs/stream`
|
||||
);
|
||||
eventSource.onmessage = (event) => {
|
||||
console.log(JSON.parse(event.data));
|
||||
};
|
||||
```
|
||||
|
||||
### cURL Examples
|
||||
|
||||
```bash
|
||||
# List models
|
||||
curl http://localhost:2026/api/models
|
||||
|
||||
# Get MCP config
|
||||
curl http://localhost:2026/api/mcp/config
|
||||
|
||||
# Upload file
|
||||
curl -X POST http://localhost:2026/api/threads/abc123/uploads \
|
||||
-F "files=@document.pdf"
|
||||
|
||||
# Enable skill
|
||||
curl -X POST http://localhost:2026/api/skills/pdf-processing/enable
|
||||
|
||||
# Create thread and run agent
|
||||
curl -X POST http://localhost:2026/api/langgraph/threads \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{}'
|
||||
|
||||
curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": {"messages": [{"role": "user", "content": "Hello"}]},
|
||||
"config": {
|
||||
"recursion_limit": 100,
|
||||
"configurable": {"model_name": "gpt-4"}
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
> The `/api/langgraph/*` endpoints bypass DeerFlow's Gateway and inherit
|
||||
> LangGraph's native `recursion_limit` default of 25, which is too low for
|
||||
> plan-mode or subagent runs. Set `config.recursion_limit` explicitly — see
|
||||
> the [Create Run](#create-run) section for details.
|
||||
238
deer-flow/backend/docs/APPLE_CONTAINER.md
Normal file
238
deer-flow/backend/docs/APPLE_CONTAINER.md
Normal file
@@ -0,0 +1,238 @@
|
||||
# Apple Container Support
|
||||
|
||||
DeerFlow now supports Apple Container as the preferred container runtime on macOS, with automatic fallback to Docker.
|
||||
|
||||
## Overview
|
||||
|
||||
Starting with this version, DeerFlow automatically detects and uses Apple Container on macOS when available, falling back to Docker when:
|
||||
- Apple Container is not installed
|
||||
- Running on non-macOS platforms
|
||||
|
||||
This provides better performance on Apple Silicon Macs while maintaining compatibility across all platforms.
|
||||
|
||||
## Benefits
|
||||
|
||||
### On Apple Silicon Macs with Apple Container:
|
||||
- **Better Performance**: Native ARM64 execution without Rosetta 2 translation
|
||||
- **Lower Resource Usage**: Lighter weight than Docker Desktop
|
||||
- **Native Integration**: Uses macOS Virtualization.framework
|
||||
|
||||
### Fallback to Docker:
|
||||
- Full backward compatibility
|
||||
- Works on all platforms (macOS, Linux, Windows)
|
||||
- No configuration changes needed
|
||||
|
||||
## Requirements
|
||||
|
||||
### For Apple Container (macOS only):
|
||||
- macOS 15.0 or later
|
||||
- Apple Silicon (M1/M2/M3/M4)
|
||||
- Apple Container CLI installed
|
||||
|
||||
### Installation:
|
||||
```bash
|
||||
# Download from GitHub releases
|
||||
# https://github.com/apple/container/releases
|
||||
|
||||
# Verify installation
|
||||
container --version
|
||||
|
||||
# Start the service
|
||||
container system start
|
||||
```
|
||||
|
||||
### For Docker (all platforms):
|
||||
- Docker Desktop or Docker Engine
|
||||
|
||||
## How It Works
|
||||
|
||||
### Automatic Detection
|
||||
|
||||
The `AioSandboxProvider` automatically detects the available container runtime:
|
||||
|
||||
1. On macOS: Try `container --version`
|
||||
- Success → Use Apple Container
|
||||
- Failure → Fall back to Docker
|
||||
|
||||
2. On other platforms: Use Docker directly
|
||||
|
||||
### Runtime Differences
|
||||
|
||||
Both runtimes use nearly identical command syntax:
|
||||
|
||||
**Container Startup:**
|
||||
```bash
|
||||
# Apple Container
|
||||
container run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
|
||||
|
||||
# Docker
|
||||
docker run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
|
||||
```
|
||||
|
||||
**Container Cleanup:**
|
||||
```bash
|
||||
# Apple Container (with --rm flag)
|
||||
container stop <id> # Auto-removes due to --rm
|
||||
|
||||
# Docker (with --rm flag)
|
||||
docker stop <id> # Auto-removes due to --rm
|
||||
```
|
||||
|
||||
### Implementation Details
|
||||
|
||||
The implementation is in `backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py`:
|
||||
|
||||
- `_detect_container_runtime()`: Detects available runtime at startup
|
||||
- `_start_container()`: Uses detected runtime, skips Docker-specific options for Apple Container
|
||||
- `_stop_container()`: Uses appropriate stop command for the runtime
|
||||
|
||||
## Configuration
|
||||
|
||||
No configuration changes are needed! The system works automatically.
|
||||
|
||||
However, you can verify the runtime in use by checking the logs:
|
||||
|
||||
```
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Detected Apple Container: container version 0.1.0
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using container: ...
|
||||
```
|
||||
|
||||
Or for Docker:
|
||||
```
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Apple Container not available, falling back to Docker
|
||||
INFO:deerflow.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using docker: ...
|
||||
```
|
||||
|
||||
## Container Images
|
||||
|
||||
Both runtimes use OCI-compatible images. The default image works with both:
|
||||
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
image: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest # Default image
|
||||
```
|
||||
|
||||
Make sure your images are available for the appropriate architecture:
|
||||
- ARM64 for Apple Container on Apple Silicon
|
||||
- AMD64 for Docker on Intel Macs
|
||||
- Multi-arch images work on both
|
||||
|
||||
### Pre-pulling Images (Recommended)
|
||||
|
||||
**Important**: Container images are typically large (500MB+) and are pulled on first use, which can cause a long wait time without clear feedback.
|
||||
|
||||
**Best Practice**: Pre-pull the image during setup:
|
||||
|
||||
```bash
|
||||
# From project root
|
||||
make setup-sandbox
|
||||
```
|
||||
|
||||
This command will:
|
||||
1. Read the configured image from `config.yaml` (or use default)
|
||||
2. Detect available runtime (Apple Container or Docker)
|
||||
3. Pull the image with progress indication
|
||||
4. Verify the image is ready for use
|
||||
|
||||
**Manual pre-pull**:
|
||||
|
||||
```bash
|
||||
# Using Apple Container
|
||||
container image pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
|
||||
|
||||
# Using Docker
|
||||
docker pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
|
||||
```
|
||||
|
||||
If you skip pre-pulling, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
|
||||
|
||||
## Cleanup Scripts
|
||||
|
||||
The project includes a unified cleanup script that handles both runtimes:
|
||||
|
||||
**Script:** `scripts/cleanup-containers.sh`
|
||||
|
||||
**Usage:**
|
||||
```bash
|
||||
# Clean up all DeerFlow sandbox containers
|
||||
./scripts/cleanup-containers.sh deer-flow-sandbox
|
||||
|
||||
# Custom prefix
|
||||
./scripts/cleanup-containers.sh my-prefix
|
||||
```
|
||||
|
||||
**Makefile Integration:**
|
||||
|
||||
All cleanup commands in `Makefile` automatically handle both runtimes:
|
||||
```bash
|
||||
make stop # Stops all services and cleans up containers
|
||||
make clean # Full cleanup including logs
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
Test the container runtime detection:
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
python test_container_runtime.py
|
||||
```
|
||||
|
||||
This will:
|
||||
1. Detect the available runtime
|
||||
2. Optionally start a test container
|
||||
3. Verify connectivity
|
||||
4. Clean up
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Apple Container not detected on macOS
|
||||
|
||||
1. Check if installed:
|
||||
```bash
|
||||
which container
|
||||
container --version
|
||||
```
|
||||
|
||||
2. Check if service is running:
|
||||
```bash
|
||||
container system start
|
||||
```
|
||||
|
||||
3. Check logs for detection:
|
||||
```bash
|
||||
# Look for detection message in application logs
|
||||
grep "container runtime" logs/*.log
|
||||
```
|
||||
|
||||
### Containers not cleaning up
|
||||
|
||||
1. Manually check running containers:
|
||||
```bash
|
||||
# Apple Container
|
||||
container list
|
||||
|
||||
# Docker
|
||||
docker ps
|
||||
```
|
||||
|
||||
2. Run cleanup script manually:
|
||||
```bash
|
||||
./scripts/cleanup-containers.sh deer-flow-sandbox
|
||||
```
|
||||
|
||||
### Performance issues
|
||||
|
||||
- Apple Container should be faster on Apple Silicon
|
||||
- If experiencing issues, you can force Docker by temporarily renaming the `container` command:
|
||||
```bash
|
||||
# Temporary workaround - not recommended for permanent use
|
||||
sudo mv /opt/homebrew/bin/container /opt/homebrew/bin/container.bak
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
- [Apple Container GitHub](https://github.com/apple/container)
|
||||
- [Apple Container Documentation](https://github.com/apple/container/blob/main/docs/)
|
||||
- [OCI Image Spec](https://github.com/opencontainers/image-spec)
|
||||
484
deer-flow/backend/docs/ARCHITECTURE.md
Normal file
484
deer-flow/backend/docs/ARCHITECTURE.md
Normal file
@@ -0,0 +1,484 @@
|
||||
# Architecture Overview
|
||||
|
||||
This document provides a comprehensive overview of the DeerFlow backend architecture.
|
||||
|
||||
## System Architecture
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Client (Browser) │
|
||||
└─────────────────────────────────┬────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Nginx (Port 2026) │
|
||||
│ Unified Reverse Proxy Entry Point │
|
||||
│ ┌────────────────────────────────────────────────────────────────────┐ │
|
||||
│ │ /api/langgraph/* → LangGraph Server (2024) │ │
|
||||
│ │ /api/* → Gateway API (8001) │ │
|
||||
│ │ /* → Frontend (3000) │ │
|
||||
│ └────────────────────────────────────────────────────────────────────┘ │
|
||||
└─────────────────────────────────┬────────────────────────────────────────┘
|
||||
│
|
||||
┌───────────────────────┼───────────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
|
||||
│ LangGraph Server │ │ Gateway API │ │ Frontend │
|
||||
│ (Port 2024) │ │ (Port 8001) │ │ (Port 3000) │
|
||||
│ │ │ │ │ │
|
||||
│ - Agent Runtime │ │ - Models API │ │ - Next.js App │
|
||||
│ - Thread Mgmt │ │ - MCP Config │ │ - React UI │
|
||||
│ - SSE Streaming │ │ - Skills Mgmt │ │ - Chat Interface │
|
||||
│ - Checkpointing │ │ - File Uploads │ │ │
|
||||
│ │ │ - Thread Cleanup │ │ │
|
||||
│ │ │ - Artifacts │ │ │
|
||||
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
|
||||
│ │
|
||||
│ ┌─────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
┌──────────────────────────────────────────────────────────────────────────┐
|
||||
│ Shared Configuration │
|
||||
│ ┌─────────────────────────┐ ┌────────────────────────────────────────┐ │
|
||||
│ │ config.yaml │ │ extensions_config.json │ │
|
||||
│ │ - Models │ │ - MCP Servers │ │
|
||||
│ │ - Tools │ │ - Skills State │ │
|
||||
│ │ - Sandbox │ │ │ │
|
||||
│ │ - Summarization │ │ │ │
|
||||
│ └─────────────────────────┘ └────────────────────────────────────────┘ │
|
||||
└──────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Component Details
|
||||
|
||||
### LangGraph Server
|
||||
|
||||
The LangGraph server is the core agent runtime, built on LangGraph for robust multi-agent workflow orchestration.
|
||||
|
||||
**Entry Point**: `packages/harness/deerflow/agents/lead_agent/agent.py:make_lead_agent`
|
||||
|
||||
**Key Responsibilities**:
|
||||
- Agent creation and configuration
|
||||
- Thread state management
|
||||
- Middleware chain execution
|
||||
- Tool execution orchestration
|
||||
- SSE streaming for real-time responses
|
||||
|
||||
**Configuration**: `langgraph.json`
|
||||
|
||||
```json
|
||||
{
|
||||
"agent": {
|
||||
"type": "agent",
|
||||
"path": "deerflow.agents:make_lead_agent"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Gateway API
|
||||
|
||||
FastAPI application providing REST endpoints for non-agent operations.
|
||||
|
||||
**Entry Point**: `app/gateway/app.py`
|
||||
|
||||
**Routers**:
|
||||
- `models.py` - `/api/models` - Model listing and details
|
||||
- `mcp.py` - `/api/mcp` - MCP server configuration
|
||||
- `skills.py` - `/api/skills` - Skills management
|
||||
- `uploads.py` - `/api/threads/{id}/uploads` - File upload
|
||||
- `threads.py` - `/api/threads/{id}` - Local DeerFlow thread data cleanup after LangGraph deletion
|
||||
- `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
|
||||
- `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation
|
||||
|
||||
The web conversation delete flow is now split across both backend surfaces: LangGraph handles `DELETE /api/langgraph/threads/{thread_id}` for thread state, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
|
||||
|
||||
### Agent Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ make_lead_agent(config) │
|
||||
└────────────────────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Middleware Chain │
|
||||
│ ┌──────────────────────────────────────────────────────────────────┐ │
|
||||
│ │ 1. ThreadDataMiddleware - Initialize workspace/uploads/outputs │ │
|
||||
│ │ 2. UploadsMiddleware - Process uploaded files │ │
|
||||
│ │ 3. SandboxMiddleware - Acquire sandbox environment │ │
|
||||
│ │ 4. SummarizationMiddleware - Context reduction (if enabled) │ │
|
||||
│ │ 5. TitleMiddleware - Auto-generate titles │ │
|
||||
│ │ 6. TodoListMiddleware - Task tracking (if plan_mode) │ │
|
||||
│ │ 7. ViewImageMiddleware - Vision model support │ │
|
||||
│ │ 8. ClarificationMiddleware - Handle clarifications │ │
|
||||
│ └──────────────────────────────────────────────────────────────────┘ │
|
||||
└────────────────────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Agent Core │
|
||||
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────────┐ │
|
||||
│ │ Model │ │ Tools │ │ System Prompt │ │
|
||||
│ │ (from factory) │ │ (configured + │ │ (with skills) │ │
|
||||
│ │ │ │ MCP + builtin) │ │ │ │
|
||||
│ └──────────────────┘ └──────────────────┘ └──────────────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Thread State
|
||||
|
||||
The `ThreadState` extends LangGraph's `AgentState` with additional fields:
|
||||
|
||||
```python
|
||||
class ThreadState(AgentState):
|
||||
# Core state from AgentState
|
||||
messages: list[BaseMessage]
|
||||
|
||||
# DeerFlow extensions
|
||||
sandbox: dict # Sandbox environment info
|
||||
artifacts: list[str] # Generated file paths
|
||||
thread_data: dict # {workspace, uploads, outputs} paths
|
||||
title: str | None # Auto-generated conversation title
|
||||
todos: list[dict] # Task tracking (plan mode)
|
||||
viewed_images: dict # Vision model image data
|
||||
```
|
||||
|
||||
### Sandbox System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Sandbox Architecture │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌─────────────────────────┐
|
||||
│ SandboxProvider │ (Abstract)
|
||||
│ - acquire() │
|
||||
│ - get() │
|
||||
│ - release() │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────────┐ ┌─────────────────────────┐
|
||||
│ LocalSandboxProvider │ │ AioSandboxProvider │
|
||||
│ (packages/harness/deerflow/sandbox/local.py) │ │ (packages/harness/deerflow/community/) │
|
||||
│ │ │ │
|
||||
│ - Singleton instance │ │ - Docker-based │
|
||||
│ - Direct execution │ │ - Isolated containers │
|
||||
│ - Development use │ │ - Production use │
|
||||
└─────────────────────────┘ └─────────────────────────┘
|
||||
|
||||
┌─────────────────────────┐
|
||||
│ Sandbox │ (Abstract)
|
||||
│ - execute_command() │
|
||||
│ - read_file() │
|
||||
│ - write_file() │
|
||||
│ - list_dir() │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
**Virtual Path Mapping**:
|
||||
|
||||
| Virtual Path | Physical Path |
|
||||
|-------------|---------------|
|
||||
| `/mnt/user-data/workspace` | `backend/.deer-flow/threads/{thread_id}/user-data/workspace` |
|
||||
| `/mnt/user-data/uploads` | `backend/.deer-flow/threads/{thread_id}/user-data/uploads` |
|
||||
| `/mnt/user-data/outputs` | `backend/.deer-flow/threads/{thread_id}/user-data/outputs` |
|
||||
| `/mnt/skills` | `deer-flow/skills/` |
|
||||
|
||||
### Tool System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Tool Sources │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
|
||||
│ Built-in Tools │ │ Configured Tools │ │ MCP Tools │
|
||||
│ (packages/harness/deerflow/tools/) │ │ (config.yaml) │ │ (extensions.json) │
|
||||
├─────────────────────┤ ├─────────────────────┤ ├─────────────────────┤
|
||||
│ - present_file │ │ - web_search │ │ - github │
|
||||
│ - ask_clarification │ │ - web_fetch │ │ - filesystem │
|
||||
│ - view_image │ │ - bash │ │ - postgres │
|
||||
│ │ │ - read_file │ │ - brave-search │
|
||||
│ │ │ - write_file │ │ - puppeteer │
|
||||
│ │ │ - str_replace │ │ - ... │
|
||||
│ │ │ - ls │ │ │
|
||||
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
|
||||
│ │ │
|
||||
└───────────────────────┴───────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ get_available_tools() │
|
||||
│ (packages/harness/deerflow/tools/__init__) │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
### Model Factory
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Model Factory │
|
||||
│ (packages/harness/deerflow/models/factory.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
config.yaml:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ models: │
|
||||
│ - name: gpt-4 │
|
||||
│ display_name: GPT-4 │
|
||||
│ use: langchain_openai:ChatOpenAI │
|
||||
│ model: gpt-4 │
|
||||
│ api_key: $OPENAI_API_KEY │
|
||||
│ max_tokens: 4096 │
|
||||
│ supports_thinking: false │
|
||||
│ supports_vision: true │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ create_chat_model() │
|
||||
│ - name: str │
|
||||
│ - thinking_enabled │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ resolve_class() │
|
||||
│ (reflection system) │
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ BaseChatModel │
|
||||
│ (LangChain instance) │
|
||||
└─────────────────────────┘
|
||||
```
|
||||
|
||||
**Supported Providers**:
|
||||
- OpenAI (`langchain_openai:ChatOpenAI`)
|
||||
- Anthropic (`langchain_anthropic:ChatAnthropic`)
|
||||
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
|
||||
- Custom via LangChain integrations
|
||||
|
||||
### MCP Integration
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ MCP Integration │
|
||||
│ (packages/harness/deerflow/mcp/manager.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
extensions_config.json:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ { │
|
||||
│ "mcpServers": { │
|
||||
│ "github": { │
|
||||
│ "enabled": true, │
|
||||
│ "type": "stdio", │
|
||||
│ "command": "npx", │
|
||||
│ "args": ["-y", "@modelcontextprotocol/server-github"], │
|
||||
│ "env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"} │
|
||||
│ } │
|
||||
│ } │
|
||||
│ } │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────┐
|
||||
│ MultiServerMCPClient │
|
||||
│ (langchain-mcp-adapters)│
|
||||
└────────────┬────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌───────────┐ ┌───────────┐ ┌───────────┐
|
||||
│ stdio │ │ SSE │ │ HTTP │
|
||||
│ transport │ │ transport │ │ transport │
|
||||
└───────────┘ └───────────┘ └───────────┘
|
||||
```
|
||||
|
||||
### Skills System
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Skills System │
|
||||
│ (packages/harness/deerflow/skills/loader.py) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
Directory Structure:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ skills/ │
|
||||
│ ├── public/ # Public skills (committed) │
|
||||
│ │ ├── pdf-processing/ │
|
||||
│ │ │ └── SKILL.md │
|
||||
│ │ ├── frontend-design/ │
|
||||
│ │ │ └── SKILL.md │
|
||||
│ │ └── ... │
|
||||
│ └── custom/ # Custom skills (gitignored) │
|
||||
│ └── user-installed/ │
|
||||
│ └── SKILL.md │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
SKILL.md Format:
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ --- │
|
||||
│ name: PDF Processing │
|
||||
│ description: Handle PDF documents efficiently │
|
||||
│ license: MIT │
|
||||
│ allowed-tools: │
|
||||
│ - read_file │
|
||||
│ - write_file │
|
||||
│ - bash │
|
||||
│ --- │
|
||||
│ │
|
||||
│ # Skill Instructions │
|
||||
│ Content injected into system prompt... │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Request Flow
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ Request Flow Example │
|
||||
│ User sends message to agent │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
1. Client → Nginx
|
||||
POST /api/langgraph/threads/{thread_id}/runs
|
||||
{"input": {"messages": [{"role": "user", "content": "Hello"}]}}
|
||||
|
||||
2. Nginx → LangGraph Server (2024)
|
||||
Proxied to LangGraph server
|
||||
|
||||
3. LangGraph Server
|
||||
a. Load/create thread state
|
||||
b. Execute middleware chain:
|
||||
- ThreadDataMiddleware: Set up paths
|
||||
- UploadsMiddleware: Inject file list
|
||||
- SandboxMiddleware: Acquire sandbox
|
||||
- SummarizationMiddleware: Check token limits
|
||||
- TitleMiddleware: Generate title if needed
|
||||
- TodoListMiddleware: Load todos (if plan mode)
|
||||
- ViewImageMiddleware: Process images
|
||||
- ClarificationMiddleware: Check for clarifications
|
||||
|
||||
c. Execute agent:
|
||||
- Model processes messages
|
||||
- May call tools (bash, web_search, etc.)
|
||||
- Tools execute via sandbox
|
||||
- Results added to messages
|
||||
|
||||
d. Stream response via SSE
|
||||
|
||||
4. Client receives streaming response
|
||||
```
|
||||
|
||||
## Data Flow
|
||||
|
||||
### File Upload Flow
|
||||
|
||||
```
|
||||
1. Client uploads file
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
2. Gateway receives file
|
||||
- Validates file
|
||||
- Stores in .deer-flow/threads/{thread_id}/user-data/uploads/
|
||||
- If document: converts to Markdown via markitdown
|
||||
|
||||
3. Returns response
|
||||
{
|
||||
"files": [{
|
||||
"filename": "doc.pdf",
|
||||
"path": ".deer-flow/.../uploads/doc.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/doc.pdf",
|
||||
"artifact_url": "/api/threads/.../artifacts/mnt/.../doc.pdf"
|
||||
}]
|
||||
}
|
||||
|
||||
4. Next agent run
|
||||
- UploadsMiddleware lists files
|
||||
- Injects file list into messages
|
||||
- Agent can access via virtual_path
|
||||
```
|
||||
|
||||
### Thread Cleanup Flow
|
||||
|
||||
```
|
||||
1. Client deletes conversation via LangGraph
|
||||
DELETE /api/langgraph/threads/{thread_id}
|
||||
|
||||
2. Web UI follows up with Gateway cleanup
|
||||
DELETE /api/threads/{thread_id}
|
||||
|
||||
3. Gateway removes local DeerFlow-managed files
|
||||
- Deletes .deer-flow/threads/{thread_id}/ recursively
|
||||
- Missing directories are treated as a no-op
|
||||
- Invalid thread IDs are rejected before filesystem access
|
||||
```
|
||||
|
||||
### Configuration Reload
|
||||
|
||||
```
|
||||
1. Client updates MCP config
|
||||
PUT /api/mcp/config
|
||||
|
||||
2. Gateway writes extensions_config.json
|
||||
- Updates mcpServers section
|
||||
- File mtime changes
|
||||
|
||||
3. MCP Manager detects change
|
||||
- get_cached_mcp_tools() checks mtime
|
||||
- If changed: reinitializes MCP client
|
||||
- Loads updated server configurations
|
||||
|
||||
4. Next agent run uses new tools
|
||||
```
|
||||
|
||||
## Security Considerations
|
||||
|
||||
### Sandbox Isolation
|
||||
|
||||
- Agent code executes within sandbox boundaries
|
||||
- Local sandbox: Direct execution (development only)
|
||||
- Docker sandbox: Container isolation (production recommended)
|
||||
- Path traversal prevention in file operations
|
||||
|
||||
### API Security
|
||||
|
||||
- Thread isolation: Each thread has separate data directories
|
||||
- File validation: Uploads checked for path safety
|
||||
- Environment variable resolution: Secrets not stored in config
|
||||
|
||||
### MCP Security
|
||||
|
||||
- Each MCP server runs in its own process
|
||||
- Environment variables resolved at runtime
|
||||
- Servers can be enabled/disabled independently
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Caching
|
||||
|
||||
- MCP tools cached with file mtime invalidation
|
||||
- Configuration loaded once, reloaded on file change
|
||||
- Skills parsed once at startup, cached in memory
|
||||
|
||||
### Streaming
|
||||
|
||||
- SSE used for real-time response streaming
|
||||
- Reduces time to first token
|
||||
- Enables progress visibility for long operations
|
||||
|
||||
### Context Management
|
||||
|
||||
- Summarization middleware reduces context when limits approached
|
||||
- Configurable triggers: tokens, messages, or fraction
|
||||
- Preserves recent messages while summarizing older ones
|
||||
258
deer-flow/backend/docs/AUTO_TITLE_GENERATION.md
Normal file
258
deer-flow/backend/docs/AUTO_TITLE_GENERATION.md
Normal file
@@ -0,0 +1,258 @@
|
||||
# 自动 Thread Title 生成功能
|
||||
|
||||
## 功能说明
|
||||
|
||||
自动为对话线程生成标题，在用户首次提问并收到回复后自动触发。
|
||||
|
||||
## 实现方式
|
||||
|
||||
使用 `TitleMiddleware` 在 `after_model` 钩子中：
|
||||
1. 检测是否是首次对话（1个用户消息 + 1个助手回复）
|
||||
2. 检查 state 是否已有 title
|
||||
3. 调用 LLM 生成简洁的标题（默认最多6个词）
|
||||
4. 将 title 存储到 `ThreadState` 中（会被 checkpointer 持久化）
|
||||
|
||||
TitleMiddleware 会先把 LangChain message content 里的结构化 block/list 内容归一化为纯文本，再拼到 title prompt 里，避免把 Python/JSON 的原始 repr 泄漏到标题生成模型。
|
||||
|
||||
## ⚠️ 重要：存储机制
|
||||
|
||||
### Title 存储位置
|
||||
|
||||
Title 存储在 **`ThreadState.title`** 中，而非 thread metadata：
|
||||
|
||||
```python
|
||||
class ThreadState(AgentState):
|
||||
sandbox: SandboxState | None = None
|
||||
title: str | None = None # ✅ Title stored here
|
||||
```
|
||||
|
||||
### 持久化说明
|
||||
|
||||
| 部署方式 | 持久化 | 说明 |
|
||||
|---------|--------|------|
|
||||
| **LangGraph Studio (本地)** | ❌ 否 | 仅内存存储，重启后丢失 |
|
||||
| **LangGraph Platform** | ✅ 是 | 自动持久化到数据库 |
|
||||
| **自定义 + Checkpointer** | ✅ 是 | 需配置 PostgreSQL/SQLite checkpointer |
|
||||
|
||||
### 如何启用持久化
|
||||
|
||||
如果需要在本地开发时也持久化 title，需要配置 checkpointer：
|
||||
|
||||
```python
|
||||
# 在 langgraph.json 同级目录创建 checkpointer.py
|
||||
from langgraph.checkpoint.postgres import PostgresSaver
|
||||
|
||||
checkpointer = PostgresSaver.from_conn_string(
|
||||
"postgresql://user:pass@localhost/dbname"
|
||||
)
|
||||
```
|
||||
|
||||
然后在 `langgraph.json` 中引用：
|
||||
|
||||
```json
|
||||
{
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:lead_agent"
|
||||
},
|
||||
"checkpointer": "checkpointer:checkpointer"
|
||||
}
|
||||
```
|
||||
|
||||
## 配置
|
||||
|
||||
在 `config.yaml` 中添加（可选）：
|
||||
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 6
|
||||
max_chars: 60
|
||||
model_name: null # 使用默认模型
|
||||
```
|
||||
|
||||
或在代码中配置：
|
||||
|
||||
```python
|
||||
from deerflow.config.title_config import TitleConfig, set_title_config
|
||||
|
||||
set_title_config(TitleConfig(
|
||||
enabled=True,
|
||||
max_words=8,
|
||||
max_chars=80,
|
||||
))
|
||||
```
|
||||
|
||||
## 客户端使用
|
||||
|
||||
### 获取 Thread Title
|
||||
|
||||
```typescript
|
||||
// 方式1: 从 thread state 获取
|
||||
const state = await client.threads.getState(threadId);
|
||||
const title = state.values.title || "New Conversation";
|
||||
|
||||
// 方式2: 监听 stream 事件
|
||||
for await (const chunk of client.runs.stream(threadId, assistantId, {
|
||||
input: { messages: [{ role: "user", content: "Hello" }] }
|
||||
})) {
|
||||
if (chunk.event === "values" && chunk.data.title) {
|
||||
console.log("Title:", chunk.data.title);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 显示 Title
|
||||
|
||||
```typescript
|
||||
// 在对话列表中显示
|
||||
function ConversationList() {
|
||||
const [threads, setThreads] = useState([]);
|
||||
|
||||
useEffect(() => {
|
||||
async function loadThreads() {
|
||||
const allThreads = await client.threads.list();
|
||||
|
||||
// 获取每个 thread 的 state 来读取 title
|
||||
const threadsWithTitles = await Promise.all(
|
||||
allThreads.map(async (t) => {
|
||||
const state = await client.threads.getState(t.thread_id);
|
||||
return {
|
||||
id: t.thread_id,
|
||||
title: state.values.title || "New Conversation",
|
||||
updatedAt: t.updated_at,
|
||||
};
|
||||
})
|
||||
);
|
||||
|
||||
setThreads(threadsWithTitles);
|
||||
}
|
||||
loadThreads();
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<ul>
|
||||
{threads.map(thread => (
|
||||
<li key={thread.id}>
|
||||
<a href={`/chat/${thread.id}`}>{thread.title}</a>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
## 工作流程
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant User
|
||||
participant Client
|
||||
participant LangGraph
|
||||
participant TitleMiddleware
|
||||
participant LLM
|
||||
participant Checkpointer
|
||||
|
||||
User->>Client: 发送首条消息
|
||||
Client->>LangGraph: POST /threads/{id}/runs
|
||||
LangGraph->>Agent: 处理消息
|
||||
Agent-->>LangGraph: 返回回复
|
||||
LangGraph->>TitleMiddleware: after_agent()
|
||||
TitleMiddleware->>TitleMiddleware: 检查是否需要生成 title
|
||||
TitleMiddleware->>LLM: 生成 title
|
||||
LLM-->>TitleMiddleware: 返回 title
|
||||
TitleMiddleware->>LangGraph: return {"title": "..."}
|
||||
LangGraph->>Checkpointer: 保存 state (含 title)
|
||||
LangGraph-->>Client: 返回响应
|
||||
Client->>Client: 从 state.values.title 读取
|
||||
```
|
||||
|
||||
## 优势
|
||||
|
||||
✅ **可靠持久化** - 使用 LangGraph 的 state 机制，自动持久化
|
||||
✅ **完全后端处理** - 客户端无需额外逻辑
|
||||
✅ **自动触发** - 首次对话后自动生成
|
||||
✅ **可配置** - 支持自定义长度、模型等
|
||||
✅ **容错性强** - 失败时使用 fallback 策略
|
||||
✅ **架构一致** - 与现有 SandboxMiddleware 保持一致
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **读取方式不同**：Title 在 `state.values.title` 而非 `thread.metadata.title`
|
||||
2. **性能考虑**：title 生成会增加约 0.5-1 秒延迟，可通过使用更快的模型优化
|
||||
3. **并发安全**：middleware 在 agent 执行后运行，不会阻塞主流程
|
||||
4. **Fallback 策略**：如果 LLM 调用失败，会使用用户消息的前几个词作为 title
|
||||
|
||||
## 测试
|
||||
|
||||
```python
|
||||
# 测试 title 生成
|
||||
import pytest
|
||||
from deerflow.agents.title_middleware import TitleMiddleware
|
||||
|
||||
def test_title_generation():
|
||||
# TODO: 添加单元测试
|
||||
pass
|
||||
```
|
||||
|
||||
## 故障排查
|
||||
|
||||
### Title 没有生成
|
||||
|
||||
1. 检查配置是否启用：`get_title_config().enabled == True`
|
||||
2. 检查日志：查找 "Generated thread title" 或错误信息
|
||||
3. 确认是首次对话：只有 1 个用户消息和 1 个助手回复时才会触发
|
||||
|
||||
### Title 生成但客户端看不到
|
||||
|
||||
1. 确认读取位置：应该从 `state.values.title` 读取，而非 `thread.metadata.title`
|
||||
2. 检查 API 响应：确认 state 中包含 title 字段
|
||||
3. 尝试重新获取 state：`client.threads.getState(threadId)`
|
||||
|
||||
### Title 重启后丢失
|
||||
|
||||
1. 检查是否配置了 checkpointer（本地开发需要）
|
||||
2. 确认部署方式：LangGraph Platform 会自动持久化
|
||||
3. 查看数据库：确认 checkpointer 正常工作
|
||||
|
||||
## 架构设计
|
||||
|
||||
### 为什么使用 State 而非 Metadata？
|
||||
|
||||
| 特性 | State | Metadata |
|
||||
|------|-------|----------|
|
||||
| **持久化** | ✅ 自动（通过 checkpointer） | ⚠️ 取决于实现 |
|
||||
| **版本控制** | ✅ 支持时间旅行 | ❌ 不支持 |
|
||||
| **类型安全** | ✅ TypedDict 定义 | ❌ 任意字典 |
|
||||
| **可追溯** | ✅ 每次更新都记录 | ⚠️ 只有最新值 |
|
||||
| **标准化** | ✅ LangGraph 核心机制 | ⚠️ 扩展功能 |
|
||||
|
||||
### 实现细节
|
||||
|
||||
```python
|
||||
# TitleMiddleware 核心逻辑
|
||||
@override
|
||||
def after_agent(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
"""Generate and set thread title after the first agent response."""
|
||||
if self._should_generate_title(state, runtime):
|
||||
title = self._generate_title(runtime)
|
||||
print(f"Generated thread title: {title}")
|
||||
|
||||
# ✅ 返回 state 更新，会被 checkpointer 自动持久化
|
||||
return {"title": title}
|
||||
|
||||
return None
|
||||
```
|
||||
|
||||
## 相关文件
|
||||
|
||||
- [`packages/harness/deerflow/agents/thread_state.py`](../packages/harness/deerflow/agents/thread_state.py) - ThreadState 定义
|
||||
- [`packages/harness/deerflow/agents/middlewares/title_middleware.py`](../packages/harness/deerflow/agents/middlewares/title_middleware.py) - TitleMiddleware 实现
|
||||
- [`packages/harness/deerflow/config/title_config.py`](../packages/harness/deerflow/config/title_config.py) - 配置管理
|
||||
- [`config.yaml`](../../config.example.yaml) - 配置文件
|
||||
- [`packages/harness/deerflow/agents/lead_agent/agent.py`](../packages/harness/deerflow/agents/lead_agent/agent.py) - Middleware 注册
|
||||
|
||||
## 参考资料
|
||||
|
||||
- [LangGraph Checkpointer 文档](https://langchain-ai.github.io/langgraph/concepts/persistence/)
|
||||
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
|
||||
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)
|
||||
369
deer-flow/backend/docs/CONFIGURATION.md
Normal file
369
deer-flow/backend/docs/CONFIGURATION.md
Normal file
@@ -0,0 +1,369 @@
|
||||
# Configuration Guide
|
||||
|
||||
This guide explains how to configure DeerFlow for your environment.
|
||||
|
||||
## Config Versioning
|
||||
|
||||
`config.example.yaml` contains a `config_version` field that tracks schema changes. When the example version is higher than your local `config.yaml`, the application emits a startup warning:
|
||||
|
||||
```
|
||||
WARNING - Your config.yaml (version 0) is outdated — the latest version is 1.
|
||||
Run `make config-upgrade` to merge new fields into your config.
|
||||
```
|
||||
|
||||
- **Missing `config_version`** in your config is treated as version 0.
|
||||
- Run `make config-upgrade` to auto-merge missing fields (your existing values are preserved, a `.bak` backup is created).
|
||||
- When changing the config schema, bump `config_version` in `config.example.yaml`.
|
||||
|
||||
## Configuration Sections
|
||||
|
||||
### Models
|
||||
|
||||
Configure the LLM models available to the agent:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-4 # Internal identifier
|
||||
display_name: GPT-4 # Human-readable name
|
||||
use: langchain_openai:ChatOpenAI # LangChain class path
|
||||
model: gpt-4 # Model identifier for API
|
||||
api_key: $OPENAI_API_KEY # API key (use env var)
|
||||
max_tokens: 4096 # Max tokens per request
|
||||
temperature: 0.7 # Sampling temperature
|
||||
```
|
||||
|
||||
**Supported Providers**:
|
||||
- OpenAI (`langchain_openai:ChatOpenAI`)
|
||||
- Anthropic (`langchain_anthropic:ChatAnthropic`)
|
||||
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
|
||||
- Claude Code OAuth (`deerflow.models.claude_provider:ClaudeChatModel`)
|
||||
- Codex CLI (`deerflow.models.openai_codex_provider:CodexChatModel`)
|
||||
- Any LangChain-compatible provider
|
||||
|
||||
CLI-backed provider examples:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5.4
|
||||
display_name: GPT-5.4 (Codex CLI)
|
||||
use: deerflow.models.openai_codex_provider:CodexChatModel
|
||||
model: gpt-5.4
|
||||
supports_thinking: true
|
||||
supports_reasoning_effort: true
|
||||
|
||||
- name: claude-sonnet-4.6
|
||||
display_name: Claude Sonnet 4.6 (Claude Code OAuth)
|
||||
use: deerflow.models.claude_provider:ClaudeChatModel
|
||||
model: claude-sonnet-4-6
|
||||
max_tokens: 4096
|
||||
supports_thinking: true
|
||||
```
|
||||
|
||||
**Auth behavior for CLI-backed providers**:
|
||||
- `CodexChatModel` loads Codex CLI auth from `~/.codex/auth.json`
|
||||
- The Codex Responses endpoint currently rejects `max_tokens` and `max_output_tokens`, so `CodexChatModel` does not expose a request-level token cap
|
||||
- `ClaudeChatModel` accepts `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR`, `CLAUDE_CODE_CREDENTIALS_PATH`, or plaintext `~/.claude/.credentials.json`
|
||||
- On macOS, DeerFlow does not probe Keychain automatically. Use `scripts/export_claude_code_oauth.py` to export Claude Code auth explicitly when needed
|
||||
|
||||
To use OpenAI's `/v1/responses` endpoint with LangChain, keep using `langchain_openai:ChatOpenAI` and set:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gpt-5-responses
|
||||
display_name: GPT-5 (Responses API)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: gpt-5
|
||||
api_key: $OPENAI_API_KEY
|
||||
use_responses_api: true
|
||||
output_version: responses/v1
|
||||
```
|
||||
|
||||
For OpenAI-compatible gateways (for example Novita or OpenRouter), keep using `langchain_openai:ChatOpenAI` and set `base_url`:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: novita-deepseek-v3.2
|
||||
display_name: Novita DeepSeek V3.2
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: deepseek/deepseek-v3.2
|
||||
api_key: $NOVITA_API_KEY
|
||||
base_url: https://api.novita.ai/openai
|
||||
supports_thinking: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
|
||||
- name: minimax-m2.5
|
||||
display_name: MiniMax M2.5
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: MiniMax-M2.5
|
||||
api_key: $MINIMAX_API_KEY
|
||||
base_url: https://api.minimax.io/v1
|
||||
max_tokens: 4096
|
||||
temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
|
||||
supports_vision: true
|
||||
|
||||
- name: minimax-m2.5-highspeed
|
||||
display_name: MiniMax M2.5 Highspeed
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: MiniMax-M2.5-highspeed
|
||||
api_key: $MINIMAX_API_KEY
|
||||
base_url: https://api.minimax.io/v1
|
||||
max_tokens: 4096
|
||||
temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
|
||||
supports_vision: true
|
||||
- name: openrouter-gemini-2.5-flash
|
||||
display_name: Gemini 2.5 Flash (OpenRouter)
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: google/gemini-2.5-flash-preview
|
||||
api_key: $OPENAI_API_KEY
|
||||
base_url: https://openrouter.ai/api/v1
|
||||
```
|
||||
|
||||
If your OpenRouter key lives in a different environment variable name, point `api_key` at that variable explicitly (for example `api_key: $OPENROUTER_API_KEY`).
|
||||
|
||||
**Thinking Models**:
|
||||
Some models support "thinking" mode for complex reasoning:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: deepseek-v3
|
||||
supports_thinking: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
```
|
||||
|
||||
**Gemini with thinking via OpenAI-compatible gateway**:
|
||||
|
||||
When routing Gemini through an OpenAI-compatible proxy (Vertex AI OpenAI compat endpoint, AI Studio, or third-party gateways) with thinking enabled, the API attaches a `thought_signature` to each tool-call object returned in the response. Every subsequent request that replays those assistant messages **must** echo those signatures back on the tool-call entries or the API returns:
|
||||
|
||||
```
|
||||
HTTP 400 INVALID_ARGUMENT: function call `<tool>` in the N. content block is
|
||||
missing a `thought_signature`.
|
||||
```
|
||||
|
||||
Standard `langchain_openai:ChatOpenAI` silently drops `thought_signature` when serialising messages. Use `deerflow.models.patched_openai:PatchedChatOpenAI` instead — it re-injects the tool-call signatures (sourced from `AIMessage.additional_kwargs["tool_calls"]`) into every outgoing payload:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- name: gemini-2.5-pro-thinking
|
||||
display_name: Gemini 2.5 Pro (Thinking)
|
||||
use: deerflow.models.patched_openai:PatchedChatOpenAI
|
||||
model: google/gemini-2.5-pro-preview # model name as expected by your gateway
|
||||
api_key: $GEMINI_API_KEY
|
||||
base_url: https://<your-openai-compat-gateway>/v1
|
||||
max_tokens: 16384
|
||||
supports_thinking: true
|
||||
supports_vision: true
|
||||
when_thinking_enabled:
|
||||
extra_body:
|
||||
thinking:
|
||||
type: enabled
|
||||
```
|
||||
|
||||
For Gemini accessed **without** thinking (e.g. via OpenRouter where thinking is not activated), the plain `langchain_openai:ChatOpenAI` with `supports_thinking: false` is sufficient and no patch is needed.
|
||||
|
||||
### Tool Groups
|
||||
|
||||
Organize tools into logical groups:
|
||||
|
||||
```yaml
|
||||
tool_groups:
|
||||
- name: web # Web browsing and search
|
||||
- name: file:read # Read-only file operations
|
||||
- name: file:write # Write file operations
|
||||
- name: bash # Shell command execution
|
||||
```
|
||||
|
||||
### Tools
|
||||
|
||||
Configure specific tools available to the agent:
|
||||
|
||||
```yaml
|
||||
tools:
|
||||
- name: web_search
|
||||
group: web
|
||||
use: deerflow.community.tavily.tools:web_search_tool
|
||||
max_results: 5
|
||||
# api_key: $TAVILY_API_KEY # Optional
|
||||
```
|
||||
|
||||
**Built-in Tools**:
|
||||
- `web_search` - Search the web (DuckDuckGo, Tavily, Exa, InfoQuest, Firecrawl)
|
||||
- `web_fetch` - Fetch web pages (Jina AI, Exa, InfoQuest, Firecrawl)
|
||||
- `ls` - List directory contents
|
||||
- `read_file` - Read file contents
|
||||
- `write_file` - Write file contents
|
||||
- `str_replace` - String replacement in files
|
||||
- `bash` - Execute bash commands
|
||||
|
||||
### Sandbox
|
||||
|
||||
DeerFlow supports multiple sandbox execution modes. Configure your preferred mode in `config.yaml`:
|
||||
|
||||
**Local Execution** (runs sandbox code directly on the host machine):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.sandbox.local:LocalSandboxProvider # Local execution
|
||||
allow_host_bash: false # default; host bash is disabled unless explicitly re-enabled
|
||||
```
|
||||
|
||||
**Docker Execution** (runs sandbox code in isolated Docker containers):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider # Docker-based sandbox
|
||||
```
|
||||
|
||||
**Docker Execution with Kubernetes** (runs sandbox code in Kubernetes pods via provisioner service):
|
||||
|
||||
This mode runs each sandbox in an isolated Kubernetes Pod on your **host machine's cluster**. Requires Docker Desktop K8s, OrbStack, or similar local K8s setup.
|
||||
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
provisioner_url: http://provisioner:8002
|
||||
```
|
||||
|
||||
When using Docker development (`make docker-start`), DeerFlow starts the `provisioner` service only if this provisioner mode is configured. In local or plain Docker sandbox modes, `provisioner` is skipped.
|
||||
|
||||
See [Provisioner Setup Guide](../../docker/provisioner/README.md) for detailed configuration, prerequisites, and troubleshooting.
|
||||
|
||||
Choose between local execution or Docker-based isolation:
|
||||
|
||||
**Option 1: Local Sandbox** (default, simpler setup):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.sandbox.local:LocalSandboxProvider
|
||||
allow_host_bash: false
|
||||
```
|
||||
|
||||
`allow_host_bash` is intentionally `false` by default. DeerFlow's local sandbox is a host-side convenience mode, not a secure shell isolation boundary. If you need `bash`, prefer `AioSandboxProvider`. Only set `allow_host_bash: true` for fully trusted single-user local workflows.
|
||||
|
||||
**Option 2: Docker Sandbox** (isolated, more secure):
|
||||
```yaml
|
||||
sandbox:
|
||||
use: deerflow.community.aio_sandbox:AioSandboxProvider
|
||||
port: 8080
|
||||
auto_start: true
|
||||
container_prefix: deer-flow-sandbox
|
||||
|
||||
# Optional: Additional mounts
|
||||
mounts:
|
||||
- host_path: /path/on/host
|
||||
container_path: /path/in/container
|
||||
read_only: false
|
||||
```
|
||||
|
||||
When you configure `sandbox.mounts`, DeerFlow exposes those `container_path` values in the agent prompt so the agent can discover and operate on mounted directories directly instead of assuming everything must live under `/mnt/user-data`.
|
||||
|
||||
### Skills
|
||||
|
||||
Configure the skills directory for specialized workflows:
|
||||
|
||||
```yaml
|
||||
skills:
|
||||
# Host path (optional, default: ../skills)
|
||||
path: /custom/path/to/skills
|
||||
|
||||
# Container mount path (default: /mnt/skills)
|
||||
container_path: /mnt/skills
|
||||
```
|
||||
|
||||
**How Skills Work**:
|
||||
- Skills are stored in `deer-flow/skills/{public,custom}/`
|
||||
- Each skill has a `SKILL.md` file with metadata
|
||||
- Skills are automatically discovered and loaded
|
||||
- Available in both local and Docker sandbox via path mapping
|
||||
|
||||
**Per-Agent Skill Filtering**:
|
||||
Custom agents can restrict which skills they load by defining a `skills` field in their `config.yaml` (located at `workspace/agents/<agent_name>/config.yaml`):
|
||||
- **Omitted or `null`**: Loads all globally enabled skills (default fallback).
|
||||
- **`[]` (empty list)**: Disables all skills for this specific agent.
|
||||
- **`["skill-name"]`**: Loads only the explicitly specified skills.
|
||||
|
||||
### Title Generation
|
||||
|
||||
Automatic conversation title generation:
|
||||
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 6
|
||||
max_chars: 60
|
||||
model_name: null # Use first model in list
|
||||
```
|
||||
|
||||
### GitHub API Token (Optional for GitHub Deep Research Skill)
|
||||
|
||||
The default GitHub API rate limits are quite restrictive. For frequent project research, we recommend configuring a personal access token (PAT) with read-only permissions.
|
||||
|
||||
**Configuration Steps**:
|
||||
1. Uncomment the `GITHUB_TOKEN` line in the `.env` file and add your personal access token
|
||||
2. Restart the DeerFlow service to apply changes
|
||||
|
||||
## Environment Variables
|
||||
|
||||
DeerFlow supports environment variable substitution using the `$` prefix:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- api_key: $OPENAI_API_KEY # Reads from environment
|
||||
```
|
||||
|
||||
**Common Environment Variables**:
|
||||
- `OPENAI_API_KEY` - OpenAI API key
|
||||
- `ANTHROPIC_API_KEY` - Anthropic API key
|
||||
- `DEEPSEEK_API_KEY` - DeepSeek API key
|
||||
- `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
|
||||
- `TAVILY_API_KEY` - Tavily search API key
|
||||
- `DEER_FLOW_CONFIG_PATH` - Custom config file path
|
||||
|
||||
## Configuration Location
|
||||
|
||||
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
|
||||
|
||||
## Configuration Priority
|
||||
|
||||
DeerFlow searches for configuration in this order:
|
||||
|
||||
1. Path specified in code via `config_path` argument
|
||||
2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
|
||||
3. `config.yaml` in current working directory (typically `backend/` when running)
|
||||
4. `config.yaml` in parent directory (project root: `deer-flow/`)
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Place `config.yaml` in project root** - Not in `backend/` directory
|
||||
2. **Never commit `config.yaml`** - It's already in `.gitignore`
|
||||
3. **Use environment variables for secrets** - Don't hardcode API keys
|
||||
4. **Keep `config.example.yaml` updated** - Document all new options
|
||||
5. **Test configuration changes locally** - Before deploying
|
||||
6. **Use Docker sandbox for production** - Better isolation and security
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "Config file not found"
|
||||
- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
|
||||
- The backend searches parent directory by default, so root location is preferred
|
||||
- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
|
||||
|
||||
### "Invalid API key"
|
||||
- Verify environment variables are set correctly
|
||||
- Check that `$` prefix is used for env var references
|
||||
|
||||
### "Skills not loading"
|
||||
- Check that `deer-flow/skills/` directory exists
|
||||
- Verify skills have valid `SKILL.md` files
|
||||
- Check `skills.path` configuration if using custom path
|
||||
|
||||
### "Docker sandbox fails to start"
|
||||
- Ensure Docker is running
|
||||
- Check port 8080 (or configured port) is available
|
||||
- Verify Docker image is accessible
|
||||
|
||||
## Examples
|
||||
|
||||
See `config.example.yaml` for complete examples of all configuration options.
|
||||
293
deer-flow/backend/docs/FILE_UPLOAD.md
Normal file
293
deer-flow/backend/docs/FILE_UPLOAD.md
Normal file
@@ -0,0 +1,293 @@
|
||||
# 文件上传功能
|
||||
|
||||
## 概述
|
||||
|
||||
DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并自动将 Office 文档和 PDF 转换为 Markdown 格式。
|
||||
|
||||
## 功能特性
|
||||
|
||||
- ✅ 支持多文件同时上传
|
||||
- ✅ 自动转换文档为 Markdown（PDF、PPT、Excel、Word）
|
||||
- ✅ 文件存储在线程隔离的目录中
|
||||
- ✅ Agent 自动感知已上传的文件
|
||||
- ✅ 支持文件列表查询和删除
|
||||
|
||||
## API 端点
|
||||
|
||||
### 1. 上传文件
|
||||
```
|
||||
POST /api/threads/{thread_id}/uploads
|
||||
```
|
||||
|
||||
**请求体：** `multipart/form-data`
|
||||
- `files`: 一个或多个文件
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"markdown_file": "document.md",
|
||||
"markdown_path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.md",
|
||||
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
|
||||
"markdown_artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.md"
|
||||
}
|
||||
],
|
||||
"message": "Successfully uploaded 1 file(s)"
|
||||
}
|
||||
```
|
||||
|
||||
**路径说明：**
|
||||
- `path`: 实际文件系统路径（相对于 `backend/` 目录）
|
||||
- `virtual_path`: Agent 在沙箱中使用的虚拟路径
|
||||
- `artifact_url`: 前端通过 HTTP 访问文件的 URL
|
||||
|
||||
### 2. 列出已上传文件
|
||||
```
|
||||
GET /api/threads/{thread_id}/uploads/list
|
||||
```
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"filename": "document.pdf",
|
||||
"size": 1234567,
|
||||
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
|
||||
"virtual_path": "/mnt/user-data/uploads/document.pdf",
|
||||
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
|
||||
"extension": ".pdf",
|
||||
"modified": 1705997600.0
|
||||
}
|
||||
],
|
||||
"count": 1
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 删除文件
|
||||
```
|
||||
DELETE /api/threads/{thread_id}/uploads/{filename}
|
||||
```
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Deleted document.pdf"
|
||||
}
|
||||
```
|
||||
|
||||
## 支持的文档格式
|
||||
|
||||
以下格式会自动转换为 Markdown：
|
||||
- PDF (`.pdf`)
|
||||
- PowerPoint (`.ppt`, `.pptx`)
|
||||
- Excel (`.xls`, `.xlsx`)
|
||||
- Word (`.doc`, `.docx`)
|
||||
|
||||
转换后的 Markdown 文件会保存在同一目录下，文件名为原文件名 + `.md` 扩展名。
|
||||
|
||||
## Agent 集成
|
||||
|
||||
### 自动文件列举
|
||||
|
||||
Agent 在每次请求时会自动收到已上传文件的列表，格式如下：
|
||||
|
||||
```xml
|
||||
<uploaded_files>
|
||||
The following files have been uploaded and are available for use:
|
||||
|
||||
- document.pdf (1.2 MB)
|
||||
Path: /mnt/user-data/uploads/document.pdf
|
||||
|
||||
- document.md (45.3 KB)
|
||||
Path: /mnt/user-data/uploads/document.md
|
||||
|
||||
You can read these files using the `read_file` tool with the paths shown above.
|
||||
</uploaded_files>
|
||||
```
|
||||
|
||||
### 使用上传的文件
|
||||
|
||||
Agent 在沙箱中运行，使用虚拟路径访问文件。Agent 可以直接使用 `read_file` 工具读取上传的文件：
|
||||
|
||||
```python
|
||||
# 读取原始 PDF（如果支持）
|
||||
read_file(path="/mnt/user-data/uploads/document.pdf")
|
||||
|
||||
# 读取转换后的 Markdown（推荐）
|
||||
read_file(path="/mnt/user-data/uploads/document.md")
|
||||
```
|
||||
|
||||
**路径映射关系：**
|
||||
- Agent 使用：`/mnt/user-data/uploads/document.pdf`（虚拟路径）
|
||||
- 实际存储：`backend/.deer-flow/threads/{thread_id}/user-data/uploads/document.pdf`
|
||||
- 前端访问：`/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf`（HTTP URL）
|
||||
|
||||
上传流程采用“线程目录优先”策略：
|
||||
- 先写入 `backend/.deer-flow/threads/{thread_id}/user-data/uploads/` 作为权威存储
|
||||
- 本地沙箱（`sandbox_id=local`）直接使用线程目录内容
|
||||
- 非本地沙箱会额外同步到 `/mnt/user-data/uploads/*`，确保运行时可见
|
||||
|
||||
## 测试示例
|
||||
|
||||
### 使用 curl 测试
|
||||
|
||||
```bash
|
||||
# 1. 上传单个文件
|
||||
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
|
||||
-F "files=@/path/to/document.pdf"
|
||||
|
||||
# 2. 上传多个文件
|
||||
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
|
||||
-F "files=@/path/to/document.pdf" \
|
||||
-F "files=@/path/to/presentation.pptx" \
|
||||
-F "files=@/path/to/spreadsheet.xlsx"
|
||||
|
||||
# 3. 列出已上传文件
|
||||
curl http://localhost:2026/api/threads/test-thread/uploads/list
|
||||
|
||||
# 4. 删除文件
|
||||
curl -X DELETE http://localhost:2026/api/threads/test-thread/uploads/document.pdf
|
||||
```
|
||||
|
||||
### 使用 Python 测试
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
thread_id = "test-thread"
|
||||
base_url = "http://localhost:2026"
|
||||
|
||||
# 上传文件
|
||||
files = [
|
||||
("files", open("document.pdf", "rb")),
|
||||
("files", open("presentation.pptx", "rb")),
|
||||
]
|
||||
response = requests.post(
|
||||
f"{base_url}/api/threads/{thread_id}/uploads",
|
||||
files=files
|
||||
)
|
||||
print(response.json())
|
||||
|
||||
# 列出文件
|
||||
response = requests.get(f"{base_url}/api/threads/{thread_id}/uploads/list")
|
||||
print(response.json())
|
||||
|
||||
# 删除文件
|
||||
response = requests.delete(
|
||||
f"{base_url}/api/threads/{thread_id}/uploads/document.pdf"
|
||||
)
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
## 文件存储结构
|
||||
|
||||
```
|
||||
backend/.deer-flow/threads/
|
||||
└── {thread_id}/
|
||||
└── user-data/
|
||||
└── uploads/
|
||||
├── document.pdf # 原始文件
|
||||
├── document.md # 转换后的 Markdown
|
||||
├── presentation.pptx
|
||||
├── presentation.md
|
||||
└── ...
|
||||
```
|
||||
|
||||
## 限制
|
||||
|
||||
- 最大文件大小：100MB（可在 nginx.conf 中配置 `client_max_body_size`）
|
||||
- 文件名安全性：系统会自动验证文件路径，防止目录遍历攻击
|
||||
- 线程隔离：每个线程的上传文件相互隔离，无法跨线程访问
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 组件
|
||||
|
||||
1. **Upload Router** (`app/gateway/routers/uploads.py`)
|
||||
- 处理文件上传、列表、删除请求
|
||||
- 使用 markitdown 转换文档
|
||||
|
||||
2. **Uploads Middleware** (`packages/harness/deerflow/agents/middlewares/uploads_middleware.py`)
|
||||
- 在每次 Agent 请求前注入文件列表
|
||||
- 自动生成格式化的文件列表消息
|
||||
|
||||
3. **Nginx 配置** (`nginx.conf`)
|
||||
- 路由上传请求到 Gateway API
|
||||
- 配置大文件上传支持
|
||||
|
||||
### 依赖
|
||||
|
||||
- `markitdown>=0.0.1a2` - 文档转换
|
||||
- `python-multipart>=0.0.20` - 文件上传处理
|
||||
|
||||
## 故障排查
|
||||
|
||||
### 文件上传失败
|
||||
|
||||
1. 检查文件大小是否超过限制
|
||||
2. 检查 Gateway API 是否正常运行
|
||||
3. 检查磁盘空间是否充足
|
||||
4. 查看 Gateway 日志：`make gateway`
|
||||
|
||||
### 文档转换失败
|
||||
|
||||
1. 检查 markitdown 是否正确安装：`uv run python -c "import markitdown"`
|
||||
2. 查看日志中的具体错误信息
|
||||
3. 某些损坏或加密的文档可能无法转换，但原文件仍会保存
|
||||
|
||||
### Agent 看不到上传的文件
|
||||
|
||||
1. 确认 UploadsMiddleware 已在 agent.py 中注册
|
||||
2. 检查 thread_id 是否正确
|
||||
3. 确认文件确实已上传到 `backend/.deer-flow/threads/{thread_id}/user-data/uploads/`
|
||||
4. 非本地沙箱场景下，确认上传接口没有报错（需要成功完成 sandbox 同步）
|
||||
|
||||
## 开发建议
|
||||
|
||||
### 前端集成
|
||||
|
||||
```typescript
|
||||
// 上传文件示例
|
||||
async function uploadFiles(threadId: string, files: File[]) {
|
||||
const formData = new FormData();
|
||||
files.forEach(file => {
|
||||
formData.append('files', file);
|
||||
});
|
||||
|
||||
const response = await fetch(
|
||||
`/api/threads/${threadId}/uploads`,
|
||||
{
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
}
|
||||
);
|
||||
|
||||
return response.json();
|
||||
}
|
||||
|
||||
// 列出文件
|
||||
async function listFiles(threadId: string) {
|
||||
const response = await fetch(
|
||||
`/api/threads/${threadId}/uploads/list`
|
||||
);
|
||||
return response.json();
|
||||
}
|
||||
```
|
||||
|
||||
### 扩展功能建议
|
||||
|
||||
1. **文件预览**：添加预览端点，支持在浏览器中直接查看文件
|
||||
2. **批量删除**：支持一次删除多个文件
|
||||
3. **文件搜索**：支持按文件名或类型搜索
|
||||
4. **版本控制**：保留文件的多个版本
|
||||
5. **压缩包支持**：自动解压 zip 文件
|
||||
6. **图片 OCR**：对上传的图片进行 OCR 识别
|
||||
385
deer-flow/backend/docs/GUARDRAILS.md
Normal file
385
deer-flow/backend/docs/GUARDRAILS.md
Normal file
@@ -0,0 +1,385 @@
|
||||
# Guardrails: Pre-Tool-Call Authorization
|
||||
|
||||
> **Context:** [Issue #1213](https://github.com/bytedance/deer-flow/issues/1213) — DeerFlow has Docker sandboxing and human approval via `ask_clarification`, but no deterministic, policy-driven authorization layer for tool calls. An agent running autonomous multi-step tasks can execute any loaded tool with any arguments. Guardrails add a middleware that evaluates every tool call against a policy **before** execution.
|
||||
|
||||
## Why Guardrails
|
||||
|
||||
```
|
||||
Without guardrails: With guardrails:
|
||||
|
||||
Agent Agent
|
||||
│ │
|
||||
▼ ▼
|
||||
┌──────────┐ ┌──────────┐
|
||||
│ bash │──▶ executes immediately │ bash │──▶ GuardrailMiddleware
|
||||
│ rm -rf / │ │ rm -rf / │ │
|
||||
└──────────┘ └──────────┘ ▼
|
||||
┌──────────────┐
|
||||
│ Provider │
|
||||
│ evaluates │
|
||||
│ against │
|
||||
│ policy │
|
||||
└──────┬───────┘
|
||||
│
|
||||
┌─────┴─────┐
|
||||
│ │
|
||||
ALLOW DENY
|
||||
│ │
|
||||
▼ ▼
|
||||
Tool runs Agent sees:
|
||||
normally "Guardrail denied:
|
||||
rm -rf blocked"
|
||||
```
|
||||
|
||||
- **Sandboxing** provides process isolation but not semantic authorization. A sandboxed `bash` can still `curl` data out.
|
||||
- **Human approval** (`ask_clarification`) requires a human in the loop for every action. Not viable for autonomous workflows.
|
||||
- **Guardrails** provide deterministic, policy-driven authorization that works without human intervention.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ Middleware Chain │
|
||||
│ │
|
||||
│ 1. ThreadDataMiddleware ─── per-thread dirs │
|
||||
│ 2. UploadsMiddleware ─── file upload tracking │
|
||||
│ 3. SandboxMiddleware ─── sandbox acquisition │
|
||||
│ 4. DanglingToolCallMiddleware ── fix incomplete tool calls │
|
||||
│ 5. GuardrailMiddleware ◄──── EVALUATES EVERY TOOL CALL │
|
||||
│ 6. ToolErrorHandlingMiddleware ── convert exceptions to messages │
|
||||
│ 7-12. (Summarization, Title, Memory, Vision, Subagent, Clarify) │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ GuardrailProvider │ ◄── pluggable: any class
|
||||
│ (configured in YAML) │ with evaluate/aevaluate
|
||||
└────────────┬─────────────┘
|
||||
│
|
||||
┌─────────┼──────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
Built-in OAP Passport Custom
|
||||
Allowlist Provider Provider
|
||||
(zero dep) (open standard) (your code)
|
||||
│
|
||||
Any implementation
|
||||
(e.g. APort, or
|
||||
your own evaluator)
|
||||
```
|
||||
|
||||
The `GuardrailMiddleware` implements `wrap_tool_call` / `awrap_tool_call` (the same `AgentMiddleware` pattern used by `ToolErrorHandlingMiddleware`). It:
|
||||
|
||||
1. Builds a `GuardrailRequest` with tool name, arguments, and passport reference
|
||||
2. Calls `provider.evaluate(request)` on whatever provider is configured
|
||||
3. If **deny**: returns `ToolMessage(status="error")` with the reason -- agent sees the denial and adapts
|
||||
4. If **allow**: passes through to the actual tool handler
|
||||
5. If **provider error** and `fail_closed=true` (default): blocks the call
|
||||
6. `GraphBubbleUp` exceptions (LangGraph control signals) are always propagated, never caught
|
||||
|
||||
## Three Provider Options
|
||||
|
||||
### Option 1: Built-in AllowlistProvider (Zero Dependencies)
|
||||
|
||||
The simplest option. Ships with DeerFlow. Block or allow tools by name. No external packages, no passport, no network.
|
||||
|
||||
**config.yaml:**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config:
|
||||
denied_tools: ["bash", "write_file"]
|
||||
```
|
||||
|
||||
This blocks `bash` and `write_file` for all requests. All other tools pass through.
|
||||
|
||||
You can also use an allowlist (only these tools are permitted):
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config:
|
||||
allowed_tools: ["web_search", "read_file", "ls"]
|
||||
```
|
||||
|
||||
**Try it:**
|
||||
1. Add the config above to your `config.yaml`
|
||||
2. Start DeerFlow: `make dev`
|
||||
3. Ask the agent: "Use bash to run echo hello"
|
||||
4. The agent sees: `Guardrail denied: tool 'bash' was blocked (oap.tool_not_allowed)`
|
||||
|
||||
### Option 2: OAP Passport Provider (Policy-Based)
|
||||
|
||||
For policy enforcement based on the [Open Agent Passport (OAP)](https://github.com/aporthq/aport-spec) open standard. An OAP passport is a JSON document that declares an agent's identity, capabilities, and operational limits. Any provider that reads an OAP passport and returns OAP-compliant decisions works with DeerFlow.
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ OAP Passport (JSON) │
|
||||
│ (open standard, any provider) │
|
||||
│ { │
|
||||
│ "spec_version": "oap/1.0", │
|
||||
│ "status": "active", │
|
||||
│ "capabilities": [ │
|
||||
│ {"id": "system.command.execute"}, │
|
||||
│ {"id": "data.file.read"}, │
|
||||
│ {"id": "data.file.write"}, │
|
||||
│ {"id": "web.fetch"}, │
|
||||
│ {"id": "mcp.tool.execute"} │
|
||||
│ ], │
|
||||
│ "limits": { │
|
||||
│ "system.command.execute": { │
|
||||
│ "allowed_commands": ["git", "npm", "node", "ls"], │
|
||||
│ "blocked_patterns": ["rm -rf", "sudo", "chmod 777"] │
|
||||
│ } │
|
||||
│ } │
|
||||
│ } │
|
||||
└──────────────────────────┬──────────────────────────────────┘
|
||||
│
|
||||
Any OAP-compliant provider
|
||||
┌────────────────┼────────────────┐
|
||||
│ │ │
|
||||
Your own APort (ref. Other future
|
||||
evaluator implementation) implementations
|
||||
```
|
||||
|
||||
**Creating a passport manually:**
|
||||
|
||||
An OAP passport is just a JSON file. You can create one by hand following the [OAP specification](https://github.com/aporthq/aport-spec/blob/main/oap/oap-spec.md) and validate it against the [JSON schema](https://github.com/aporthq/aport-spec/blob/main/oap/passport-schema.json). See the [examples](https://github.com/aporthq/aport-spec/tree/main/oap/examples) directory for templates.
|
||||
|
||||
**Using APort as a reference implementation:**
|
||||
|
||||
[APort Agent Guardrails](https://github.com/aporthq/aport-agent-guardrails) is one open-source (Apache 2.0) implementation of an OAP provider. It handles passport creation, local evaluation, and optional hosted API evaluation.
|
||||
|
||||
```bash
|
||||
pip install aport-agent-guardrails
|
||||
aport setup --framework deerflow
|
||||
```
|
||||
|
||||
This creates:
|
||||
- `~/.aport/deerflow/config.yaml` -- evaluator config (local or API mode)
|
||||
- `~/.aport/deerflow/aport/passport.json` -- OAP passport with capabilities and limits
|
||||
|
||||
**config.yaml (using APort as the provider):**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: aport_guardrails.providers.generic:OAPGuardrailProvider
|
||||
```
|
||||
|
||||
**config.yaml (using your own OAP provider):**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: my_oap_provider:MyOAPProvider
|
||||
config:
|
||||
passport_path: ./my-passport.json
|
||||
```
|
||||
|
||||
Any provider that accepts `framework` as a kwarg and implements `evaluate`/`aevaluate` works. The OAP standard defines the passport format and decision codes; DeerFlow doesn't care which provider reads them.
|
||||
|
||||
**What the passport controls:**
|
||||
|
||||
| Passport field | What it does | Example |
|
||||
|---|---|---|
|
||||
| `capabilities[].id` | Which tool categories the agent can use | `system.command.execute`, `data.file.write` |
|
||||
| `limits.*.allowed_commands` | Which commands are allowed | `["git", "npm", "node"]` or `["*"]` for all |
|
||||
| `limits.*.blocked_patterns` | Patterns always denied | `["rm -rf", "sudo", "chmod 777"]` |
|
||||
| `status` | Kill switch | `active`, `suspended`, `revoked` |
|
||||
|
||||
**Evaluation modes (provider-dependent):**
|
||||
|
||||
OAP providers may support different evaluation modes. For example, the APort reference implementation supports:
|
||||
|
||||
| Mode | How it works | Network | Latency |
|
||||
|---|---|---|---|
|
||||
| **Local** | Evaluates passport locally (bash script). | None | ~300ms |
|
||||
| **API** | Sends passport + context to a hosted evaluator. Signed decisions. | Yes | ~65ms |
|
||||
|
||||
A custom OAP provider can implement any evaluation strategy -- the DeerFlow middleware doesn't care how the provider reaches its decision.
|
||||
|
||||
**Try it:**
|
||||
1. Install and set up as above
|
||||
2. Start DeerFlow and ask: "Create a file called test.txt with content hello"
|
||||
3. Then ask: "Now delete it using bash rm -rf"
|
||||
4. Guardrail blocks it: `oap.blocked_pattern: Command contains blocked pattern: rm -rf`
|
||||
|
||||
### Option 3: Custom Provider (Bring Your Own)
|
||||
|
||||
Any Python class with `evaluate(request)` and `aevaluate(request)` methods works. No base class or inheritance needed -- it's a structural protocol.
|
||||
|
||||
```python
|
||||
# my_guardrail.py
|
||||
|
||||
class MyGuardrailProvider:
|
||||
name = "my-company"
|
||||
|
||||
def evaluate(self, request):
|
||||
from deerflow.guardrails.provider import GuardrailDecision, GuardrailReason
|
||||
|
||||
# Example: block any bash command containing "delete"
|
||||
if request.tool_name == "bash" and "delete" in str(request.tool_input):
|
||||
return GuardrailDecision(
|
||||
allow=False,
|
||||
reasons=[GuardrailReason(code="custom.blocked", message="delete not allowed")],
|
||||
policy_id="custom.v1",
|
||||
)
|
||||
return GuardrailDecision(allow=True, reasons=[GuardrailReason(code="oap.allowed")])
|
||||
|
||||
async def aevaluate(self, request):
|
||||
return self.evaluate(request)
|
||||
```
|
||||
|
||||
**config.yaml:**
|
||||
```yaml
|
||||
guardrails:
|
||||
enabled: true
|
||||
provider:
|
||||
use: my_guardrail:MyGuardrailProvider
|
||||
```
|
||||
|
||||
Make sure `my_guardrail.py` is on the Python path (e.g. in the backend directory or installed as a package).
|
||||
|
||||
**Try it:**
|
||||
1. Create `my_guardrail.py` in the backend directory
|
||||
2. Add the config
|
||||
3. Start DeerFlow and ask: "Use bash to delete test.txt"
|
||||
4. Your provider blocks it
|
||||
|
||||
## Implementing a Provider
|
||||
|
||||
### Required Interface
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────┐
|
||||
│ GuardrailProvider Protocol │
|
||||
│ │
|
||||
│ name: str │
|
||||
│ │
|
||||
│ evaluate(request: GuardrailRequest) │
|
||||
│ -> GuardrailDecision │
|
||||
│ │
|
||||
│ aevaluate(request: GuardrailRequest) (async) │
|
||||
│ -> GuardrailDecision │
|
||||
└──────────────────────────────────────────────────┘
|
||||
|
||||
┌──────────────────────────┐ ┌──────────────────────────┐
|
||||
│ GuardrailRequest │ │ GuardrailDecision │
|
||||
│ │ │ │
|
||||
│ tool_name: str │ │ allow: bool │
|
||||
│ tool_input: dict │ │ reasons: [GuardrailReason]│
|
||||
│ agent_id: str | None │ │ policy_id: str | None │
|
||||
│ thread_id: str | None │ │ metadata: dict │
|
||||
│ is_subagent: bool │ │ │
|
||||
│ timestamp: str │ │ GuardrailReason: │
|
||||
│ │ │ code: str │
|
||||
└──────────────────────────┘ │ message: str │
|
||||
└──────────────────────────┘
|
||||
```
|
||||
|
||||
### DeerFlow Tool Names
|
||||
|
||||
These are the tool names your provider will see in `request.tool_name`:
|
||||
|
||||
| Tool | What it does |
|
||||
|---|---|
|
||||
| `bash` | Shell command execution |
|
||||
| `write_file` | Create/overwrite a file |
|
||||
| `str_replace` | Edit a file (find and replace) |
|
||||
| `read_file` | Read file content |
|
||||
| `ls` | List directory |
|
||||
| `web_search` | Web search query |
|
||||
| `web_fetch` | Fetch URL content |
|
||||
| `image_search` | Image search |
|
||||
| `present_file` | Present file to user |
|
||||
| `view_image` | Display image |
|
||||
| `ask_clarification` | Ask user a question |
|
||||
| `task` | Delegate to subagent |
|
||||
| `mcp__*` | MCP tools (dynamic) |
|
||||
|
||||
### OAP Reason Codes
|
||||
|
||||
Standard codes used by the [OAP specification](https://github.com/aporthq/aport-spec):
|
||||
|
||||
| Code | Meaning |
|
||||
|---|---|
|
||||
| `oap.allowed` | Tool call authorized |
|
||||
| `oap.tool_not_allowed` | Tool not in allowlist |
|
||||
| `oap.command_not_allowed` | Command not in allowed_commands |
|
||||
| `oap.blocked_pattern` | Command matches a blocked pattern |
|
||||
| `oap.limit_exceeded` | Operation exceeds a limit |
|
||||
| `oap.passport_suspended` | Passport status is suspended/revoked |
|
||||
| `oap.evaluator_error` | Provider crashed (fail-closed) |
|
||||
|
||||
### Provider Loading
|
||||
|
||||
DeerFlow loads providers via `resolve_variable()` -- the same mechanism used for models, tools, and sandbox providers. The `use:` field is a Python class path: `package.module:ClassName`.
|
||||
|
||||
The provider is instantiated with `**config` kwargs if `config:` is set, plus `framework="deerflow"` is always injected. Accept `**kwargs` to stay forward-compatible:
|
||||
|
||||
```python
|
||||
class YourProvider:
|
||||
def __init__(self, framework: str = "generic", **kwargs):
|
||||
# framework="deerflow" tells you which config dir to use
|
||||
...
|
||||
```
|
||||
|
||||
## Configuration Reference
|
||||
|
||||
```yaml
|
||||
guardrails:
|
||||
# Enable/disable guardrail middleware (default: false)
|
||||
enabled: true
|
||||
|
||||
# Block tool calls if provider raises an exception (default: true)
|
||||
fail_closed: true
|
||||
|
||||
# Passport reference -- passed as request.agent_id to the provider.
|
||||
# File path, hosted agent ID, or null (provider resolves from its config).
|
||||
passport: null
|
||||
|
||||
# Provider: loaded by class path via resolve_variable
|
||||
provider:
|
||||
use: deerflow.guardrails.builtin:AllowlistProvider
|
||||
config: # optional kwargs passed to provider.__init__
|
||||
denied_tools: ["bash"]
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
uv run python -m pytest tests/test_guardrail_middleware.py -v
|
||||
```
|
||||
|
||||
25 tests covering:
|
||||
- AllowlistProvider: allow, deny, both allowlist+denylist, async
|
||||
- GuardrailMiddleware: allow passthrough, deny with OAP codes, fail-closed, fail-open, passport forwarding, empty reasons fallback, empty tool name, protocol isinstance check
|
||||
- Async paths: awrap_tool_call for allow, deny, fail-closed, fail-open
|
||||
- GraphBubbleUp: LangGraph control signals propagate through (not caught)
|
||||
- Config: defaults, from_dict, singleton load/reset
|
||||
|
||||
## Files
|
||||
|
||||
```
|
||||
packages/harness/deerflow/guardrails/
|
||||
__init__.py # Public exports
|
||||
provider.py # GuardrailProvider protocol, GuardrailRequest, GuardrailDecision
|
||||
middleware.py # GuardrailMiddleware (AgentMiddleware subclass)
|
||||
builtin.py # AllowlistProvider (zero deps)
|
||||
|
||||
packages/harness/deerflow/config/
|
||||
guardrails_config.py # GuardrailsConfig Pydantic model + singleton
|
||||
|
||||
packages/harness/deerflow/agents/middlewares/
|
||||
tool_error_handling_middleware.py # Registers GuardrailMiddleware in chain
|
||||
|
||||
config.example.yaml # Three provider options documented
|
||||
tests/test_guardrail_middleware.py # 25 tests
|
||||
docs/GUARDRAILS.md # This file
|
||||
```
|
||||
343
deer-flow/backend/docs/HARNESS_APP_SPLIT.md
Normal file
343
deer-flow/backend/docs/HARNESS_APP_SPLIT.md
Normal file
@@ -0,0 +1,343 @@
|
||||
# DeerFlow 后端拆分设计文档：Harness + App
|
||||
|
||||
> 状态：Draft
|
||||
> 作者：DeerFlow Team
|
||||
> 日期：2026-03-13
|
||||
|
||||
## 1. 背景与动机
|
||||
|
||||
DeerFlow 后端当前是一个单一 Python 包（`src.*`），包含了从底层 agent 编排到上层用户产品的所有代码。随着项目发展，这种结构带来了几个问题：
|
||||
|
||||
- **复用困难**：其他产品（CLI 工具、Slack bot、第三方集成）想用 agent 能力，必须依赖整个后端，包括 FastAPI、IM SDK 等不需要的依赖
|
||||
- **职责模糊**：agent 编排逻辑和用户产品逻辑混在同一个 `src/` 下，边界不清晰
|
||||
- **依赖膨胀**：LangGraph Server 运行时不需要 FastAPI/uvicorn/Slack SDK，但当前必须安装全部依赖
|
||||
|
||||
本文档提出将后端拆分为两部分：**deerflow-harness**（可发布的 agent 框架包）和 **app**（不打包的用户产品代码）。
|
||||
|
||||
## 2. 核心概念
|
||||
|
||||
### 2.1 Harness（线束/框架层）
|
||||
|
||||
Harness 是 agent 的构建与编排框架，回答 **"如何构建和运行 agent"** 的问题：
|
||||
|
||||
- Agent 工厂与生命周期管理
|
||||
- Middleware pipeline
|
||||
- 工具系统（内置工具 + MCP + 社区工具）
|
||||
- 沙箱执行环境
|
||||
- 子 agent 委派
|
||||
- 记忆系统
|
||||
- 技能加载与注入
|
||||
- 模型工厂
|
||||
- 配置系统
|
||||
|
||||
**Harness 是一个可发布的 Python 包**（`deerflow-harness`），可以独立安装和使用。
|
||||
|
||||
**Harness 的设计原则**：对上层应用完全无感知。它不知道也不关心谁在调用它——可以是 Web App、CLI、Slack Bot、或者一个单元测试。
|
||||
|
||||
### 2.2 App（应用层）
|
||||
|
||||
App 是面向用户的产品代码，回答 **"如何将 agent 呈现给用户"** 的问题：
|
||||
|
||||
- Gateway API（FastAPI REST 接口）
|
||||
- IM Channels（飞书、Slack、Telegram 集成）
|
||||
- Custom Agent 的 CRUD 管理
|
||||
- 文件上传/下载的 HTTP 接口
|
||||
|
||||
**App 不打包、不发布**，它是 DeerFlow 项目内部的应用代码，直接运行。
|
||||
|
||||
**App 依赖 Harness，但 Harness 不依赖 App。**
|
||||
|
||||
### 2.3 边界划分
|
||||
|
||||
| 模块 | 归属 | 说明 |
|
||||
|------|------|------|
|
||||
| `config/` | Harness | 配置系统是基础设施 |
|
||||
| `reflection/` | Harness | 动态模块加载工具 |
|
||||
| `utils/` | Harness | 通用工具函数 |
|
||||
| `agents/` | Harness | Agent 工厂、middleware、state、memory |
|
||||
| `subagents/` | Harness | 子 agent 委派系统 |
|
||||
| `sandbox/` | Harness | 沙箱执行环境 |
|
||||
| `tools/` | Harness | 工具注册与发现 |
|
||||
| `mcp/` | Harness | MCP 协议集成 |
|
||||
| `skills/` | Harness | 技能加载、解析、定义 schema |
|
||||
| `models/` | Harness | LLM 模型工厂 |
|
||||
| `community/` | Harness | 社区工具（tavily、jina 等） |
|
||||
| `client.py` | Harness | 嵌入式 Python 客户端 |
|
||||
| `gateway/` | App | FastAPI REST API |
|
||||
| `channels/` | App | IM 平台集成 |
|
||||
|
||||
**关于 Custom Agents**：agent 定义格式（`config.yaml` + `SOUL.md` schema）由 Harness 层的 `config/agents_config.py` 定义，但文件的存储、CRUD、发现机制由 App 层的 `gateway/routers/agents.py` 负责。
|
||||
|
||||
## 3. 目标架构
|
||||
|
||||
### 3.1 目录结构
|
||||
|
||||
```
|
||||
backend/
|
||||
├── packages/
|
||||
│ └── harness/
|
||||
│ ├── pyproject.toml # deerflow-harness 包定义
|
||||
│ └── deerflow/ # Python 包根（import 前缀: deerflow.*）
|
||||
│ ├── __init__.py
|
||||
│ ├── config/
|
||||
│ ├── reflection/
|
||||
│ ├── utils/
|
||||
│ ├── agents/
|
||||
│ │ ├── lead_agent/
|
||||
│ │ ├── middlewares/
|
||||
│ │ ├── memory/
|
||||
│ │ ├── checkpointer/
|
||||
│ │ └── thread_state.py
|
||||
│ ├── subagents/
|
||||
│ ├── sandbox/
|
||||
│ ├── tools/
|
||||
│ ├── mcp/
|
||||
│ ├── skills/
|
||||
│ ├── models/
|
||||
│ ├── community/
|
||||
│ └── client.py
|
||||
├── app/ # 不打包（import 前缀: app.*）
|
||||
│ ├── __init__.py
|
||||
│ ├── gateway/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── app.py
|
||||
│ │ ├── config.py
|
||||
│ │ ├── path_utils.py
|
||||
│ │ └── routers/
|
||||
│ └── channels/
|
||||
│ ├── __init__.py
|
||||
│ ├── base.py
|
||||
│ ├── manager.py
|
||||
│ ├── service.py
|
||||
│ ├── store.py
|
||||
│ ├── message_bus.py
|
||||
│ ├── feishu.py
|
||||
│ ├── slack.py
|
||||
│ └── telegram.py
|
||||
├── pyproject.toml # uv workspace root
|
||||
├── langgraph.json
|
||||
├── tests/
|
||||
├── docs/
|
||||
└── Makefile
|
||||
```
|
||||
|
||||
### 3.2 Import 规则
|
||||
|
||||
两个层使用不同的 import 前缀，职责边界一目了然：
|
||||
|
||||
```python
|
||||
# ---------------------------------------------------------------
|
||||
# Harness 内部互相引用（deerflow.* 前缀）
|
||||
# ---------------------------------------------------------------
|
||||
from deerflow.agents import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.config import get_app_config
|
||||
from deerflow.tools import get_available_tools
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# App 内部互相引用（app.* 前缀）
|
||||
# ---------------------------------------------------------------
|
||||
from app.gateway.app import app
|
||||
from app.gateway.routers.uploads import upload_files
|
||||
from app.channels.service import start_channel_service
|
||||
|
||||
# ---------------------------------------------------------------
|
||||
# App 调用 Harness（单向依赖，Harness 永远不 import app）
|
||||
# ---------------------------------------------------------------
|
||||
from deerflow.agents import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.skills import load_skills
|
||||
from deerflow.config.extensions_config import get_extensions_config
|
||||
```
|
||||
|
||||
**App 调用 Harness 示例 — Gateway 中启动 agent**：
|
||||
|
||||
```python
|
||||
# app/gateway/routers/chat.py
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
from deerflow.models import create_chat_model
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
async def create_chat_session(thread_id: str, model_name: str):
|
||||
config = get_app_config()
|
||||
model = create_chat_model(name=model_name)
|
||||
agent = make_lead_agent(config=...)
|
||||
# ... 使用 agent 处理用户消息
|
||||
```
|
||||
|
||||
**App 调用 Harness 示例 — Channel 中查询 skills**：
|
||||
|
||||
```python
|
||||
# app/channels/manager.py
|
||||
from deerflow.skills import load_skills
|
||||
from deerflow.agents.memory.updater import get_memory_data
|
||||
|
||||
def handle_status_command():
|
||||
skills = load_skills(enabled_only=True)
|
||||
memory = get_memory_data()
|
||||
return f"Skills: {len(skills)}, Memory facts: {len(memory.get('facts', []))}"
|
||||
```
|
||||
|
||||
**禁止方向**：Harness 代码中绝不能出现 `from app.` 或 `import app.`。
|
||||
|
||||
### 3.3 为什么 App 不打包
|
||||
|
||||
| 方面 | 打包（放 packages/ 下） | 不打包（放 backend/app/） |
|
||||
|------|------------------------|--------------------------|
|
||||
| 命名空间 | 需要 pkgutil `extend_path` 合并，或独立前缀 | 天然独立，`app.*` vs `deerflow.*` |
|
||||
| 发布需求 | 没有——App 是项目内部代码 | 不需要 pyproject.toml |
|
||||
| 复杂度 | 需要管理两个包的构建、版本、依赖声明 | 直接运行，零额外配置 |
|
||||
| 运行方式 | `pip install deerflow-app` | `PYTHONPATH=. uvicorn app.gateway.app:app` |
|
||||
|
||||
App 的唯一消费者是 DeerFlow 项目自身，没有独立发布的需求。放在 `backend/app/` 下作为普通 Python 包，通过 `PYTHONPATH` 或 editable install 让 Python 找到即可。
|
||||
|
||||
### 3.4 依赖关系
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ app/ (不打包，直接运行) │
|
||||
│ ├── fastapi, uvicorn │
|
||||
│ ├── slack-sdk, lark-oapi, ... │
|
||||
│ └── import deerflow.* │
|
||||
└──────────────┬──────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────┐
|
||||
│ deerflow-harness (可发布的包) │
|
||||
│ ├── langgraph, langchain │
|
||||
│ ├── markitdown, pydantic, ... │
|
||||
│ └── 零 app 依赖 │
|
||||
└─────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**依赖分类**：
|
||||
|
||||
| 分类 | 依赖包 |
|
||||
|------|--------|
|
||||
| Harness only | agent-sandbox, langchain*, langgraph*, markdownify, markitdown, pydantic, pyyaml, readabilipy, tavily-python, firecrawl-py, tiktoken, ddgs, duckdb, httpx, kubernetes, dotenv |
|
||||
| App only | fastapi, uvicorn, sse-starlette, python-multipart, lark-oapi, slack-sdk, python-telegram-bot, markdown-to-mrkdwn |
|
||||
| Shared | langgraph-sdk（channels 用 HTTP client）, pydantic, httpx |
|
||||
|
||||
### 3.5 Workspace 配置
|
||||
|
||||
`backend/pyproject.toml`（workspace root）：
|
||||
|
||||
```toml
|
||||
[project]
|
||||
name = "deer-flow"
|
||||
version = "0.1.0"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = ["deerflow-harness"]
|
||||
|
||||
[dependency-groups]
|
||||
dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
|
||||
# App 的额外依赖（fastapi 等）也声明在 workspace root，因为 app 不打包
|
||||
app = ["fastapi", "uvicorn", "sse-starlette", "python-multipart"]
|
||||
channels = ["lark-oapi", "slack-sdk", "python-telegram-bot"]
|
||||
|
||||
[tool.uv.workspace]
|
||||
members = ["packages/harness"]
|
||||
|
||||
[tool.uv.sources]
|
||||
deerflow-harness = { workspace = true }
|
||||
```
|
||||
|
||||
## 4. 当前的跨层依赖问题
|
||||
|
||||
在拆分之前，需要先解决 `client.py` 中两处从 harness 到 app 的反向依赖：
|
||||
|
||||
### 4.1 `_validate_skill_frontmatter`
|
||||
|
||||
```python
|
||||
# client.py — harness 导入了 app 层代码
|
||||
from src.gateway.routers.skills import _validate_skill_frontmatter
|
||||
```
|
||||
|
||||
**解决方案**：将该函数提取到 `deerflow/skills/validation.py`。这是一个纯逻辑函数（解析 YAML frontmatter、校验字段），与 FastAPI 无关。
|
||||
|
||||
### 4.2 `CONVERTIBLE_EXTENSIONS` + `convert_file_to_markdown`
|
||||
|
||||
```python
|
||||
# client.py — harness 导入了 app 层代码
|
||||
from src.gateway.routers.uploads import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
|
||||
```
|
||||
|
||||
**解决方案**：将它们提取到 `deerflow/utils/file_conversion.py`。仅依赖 `markitdown` + `pathlib`，是通用工具函数。
|
||||
|
||||
## 5. 基础设施变更
|
||||
|
||||
### 5.1 LangGraph Server
|
||||
|
||||
LangGraph Server 只需要 harness 包。`langgraph.json` 更新：
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["./packages/harness"],
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:make_lead_agent"
|
||||
},
|
||||
"checkpointer": {
|
||||
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5.2 Gateway API
|
||||
|
||||
```bash
|
||||
# serve.sh / Makefile
|
||||
# PYTHONPATH 包含 backend/ 根目录，使 app.* 和 deerflow.* 都能被找到
|
||||
PYTHONPATH=. uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
|
||||
```
|
||||
|
||||
### 5.3 Nginx
|
||||
|
||||
无需变更（只做 URL 路由，不涉及 Python 模块路径）。
|
||||
|
||||
### 5.4 Docker
|
||||
|
||||
Dockerfile 中的 module 引用从 `src.` 改为 `deerflow.` / `app.`，`COPY` 命令需覆盖 `packages/` 和 `app/` 目录。
|
||||
|
||||
## 6. 实施计划
|
||||
|
||||
分 3 个 PR 递进执行：
|
||||
|
||||
### PR 1：提取共享工具函数（Low Risk）
|
||||
|
||||
1. 创建 `src/skills/validation.py`，从 `gateway/routers/skills.py` 提取 `_validate_skill_frontmatter`
|
||||
2. 创建 `src/utils/file_conversion.py`，从 `gateway/routers/uploads.py` 提取文件转换逻辑
|
||||
3. 更新 `client.py`、`gateway/routers/skills.py`、`gateway/routers/uploads.py` 的 import
|
||||
4. 运行全部测试确认无回归
|
||||
|
||||
### PR 2：Rename + 物理拆分（High Risk，原子操作）
|
||||
|
||||
1. 创建 `packages/harness/` 目录，创建 `pyproject.toml`
|
||||
2. `git mv` 将 harness 相关模块从 `src/` 移入 `packages/harness/deerflow/`
|
||||
3. `git mv` 将 app 相关模块从 `src/` 移入 `app/`
|
||||
4. 全局替换 import：
|
||||
- harness 模块：`src.*` → `deerflow.*`（所有 `.py` 文件、`langgraph.json`、测试、文档）
|
||||
- app 模块：`src.gateway.*` → `app.gateway.*`、`src.channels.*` → `app.channels.*`
|
||||
5. 更新 workspace root `pyproject.toml`
|
||||
6. 更新 `langgraph.json`、`Makefile`、`Dockerfile`
|
||||
7. `uv sync` + 全部测试 + 手动验证服务启动
|
||||
|
||||
### PR 3：边界检查 + 文档（Low Risk）
|
||||
|
||||
1. 添加 lint 规则：检查 harness 不 import app 模块
|
||||
2. 更新 `CLAUDE.md`、`README.md`
|
||||
|
||||
## 7. 风险与缓解
|
||||
|
||||
| 风险 | 影响 | 缓解措施 |
|
||||
|------|------|----------|
|
||||
| 全局 rename 误伤 | 字符串中的 `src` 被错误替换 | 正则精确匹配 `\bsrc\.`，review diff |
|
||||
| LangGraph Server 找不到模块 | 服务启动失败 | `langgraph.json` 的 `dependencies` 指向正确的 harness 包路径 |
|
||||
| App 的 `PYTHONPATH` 缺失 | Gateway/Channel 启动 import 报错 | Makefile/Docker 统一设置 `PYTHONPATH=.` |
|
||||
| `config.yaml` 中的 `use` 字段引用旧路径 | 运行时模块解析失败 | `config.yaml` 中的 `use` 字段同步更新为 `deerflow.*` |
|
||||
| 测试中 `sys.path` 混乱 | 测试失败 | 用 editable install（`uv sync`）确保 deerflow 可导入，`conftest.py` 中添加 `app/` 到 `sys.path` |
|
||||
|
||||
## 8. 未来演进
|
||||
|
||||
- **独立发布**：harness 可以发布到内部 PyPI，让其他项目直接 `pip install deerflow-harness`
|
||||
- **插件化 App**：不同的 app（web、CLI、bot）可以各自独立，都依赖同一个 harness
|
||||
- **更细粒度拆分**：如果 harness 内部模块继续增长，可以进一步拆分（如 `deerflow-sandbox`、`deerflow-mcp`）
|
||||
65
deer-flow/backend/docs/MCP_SERVER.md
Normal file
65
deer-flow/backend/docs/MCP_SERVER.md
Normal file
@@ -0,0 +1,65 @@
|
||||
# MCP (Model Context Protocol) Configuration
|
||||
|
||||
DeerFlow supports configurable MCP servers and skills to extend its capabilities, which are loaded from a dedicated `extensions_config.json` file in the project root directory.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Copy `extensions_config.example.json` to `extensions_config.json` in the project root directory.
|
||||
```bash
|
||||
# Copy example configuration
|
||||
cp extensions_config.example.json extensions_config.json
|
||||
```
|
||||
|
||||
2. Enable the desired MCP servers or skills by setting `"enabled": true`.
|
||||
3. Configure each server’s command, arguments, and environment variables as needed.
|
||||
4. Restart the application to load and register MCP tools.
|
||||
|
||||
## OAuth Support (HTTP/SSE MCP Servers)
|
||||
|
||||
For `http` and `sse` MCP servers, DeerFlow supports OAuth token acquisition and automatic token refresh.
|
||||
|
||||
- Supported grants: `client_credentials`, `refresh_token`
|
||||
- Configure per-server `oauth` block in `extensions_config.json`
|
||||
- Secrets should be provided via environment variables (for example: `$MCP_OAUTH_CLIENT_SECRET`)
|
||||
|
||||
Example:
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"secure-http-server": {
|
||||
"enabled": true,
|
||||
"type": "http",
|
||||
"url": "https://api.example.com/mcp",
|
||||
"oauth": {
|
||||
"enabled": true,
|
||||
"token_url": "https://auth.example.com/oauth/token",
|
||||
"grant_type": "client_credentials",
|
||||
"client_id": "$MCP_OAUTH_CLIENT_ID",
|
||||
"client_secret": "$MCP_OAUTH_CLIENT_SECRET",
|
||||
"scope": "mcp.read",
|
||||
"refresh_skew_seconds": 60
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
MCP servers expose tools that are automatically discovered and integrated into DeerFlow’s agent system at runtime. Once enabled, these tools become available to agents without additional code changes.
|
||||
|
||||
## Example Capabilities
|
||||
|
||||
MCP servers can provide access to:
|
||||
|
||||
- **File systems**
|
||||
- **Databases** (e.g., PostgreSQL)
|
||||
- **External APIs** (e.g., GitHub, Brave Search)
|
||||
- **Browser automation** (e.g., Puppeteer)
|
||||
- **Custom MCP server implementations**
|
||||
|
||||
## Learn More
|
||||
|
||||
For detailed documentation about the Model Context Protocol, visit:
|
||||
https://modelcontextprotocol.io
|
||||
65
deer-flow/backend/docs/MEMORY_IMPROVEMENTS.md
Normal file
65
deer-flow/backend/docs/MEMORY_IMPROVEMENTS.md
Normal file
@@ -0,0 +1,65 @@
|
||||
# Memory System Improvements
|
||||
|
||||
This document tracks memory injection behavior and roadmap status.
|
||||
|
||||
## Status (As Of 2026-03-10)
|
||||
|
||||
Implemented in `main`:
|
||||
- Accurate token counting via `tiktoken` in `format_memory_for_injection`.
|
||||
- Facts are injected into prompt memory context.
|
||||
- Facts are ranked by confidence (descending).
|
||||
- Injection respects `max_injection_tokens` budget.
|
||||
|
||||
Planned / not yet merged:
|
||||
- TF-IDF similarity-based fact retrieval.
|
||||
- `current_context` input for context-aware scoring.
|
||||
- Configurable similarity/confidence weights (`similarity_weight`, `confidence_weight`).
|
||||
- Middleware/runtime wiring for context-aware retrieval before each model call.
|
||||
|
||||
## Current Behavior
|
||||
|
||||
Function today:
|
||||
|
||||
```python
|
||||
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
|
||||
```
|
||||
|
||||
Current injection format:
|
||||
- `User Context` section from `user.*.summary`
|
||||
- `History` section from `history.*.summary`
|
||||
- `Facts` section from `facts[]`, sorted by confidence, appended until token budget is reached
|
||||
|
||||
Token counting:
|
||||
- Uses `tiktoken` (`cl100k_base`) when available
|
||||
- Falls back to `len(text) // 4` if tokenizer import fails
|
||||
|
||||
## Known Gap
|
||||
|
||||
Previous versions of this document described TF-IDF/context-aware retrieval as if it were already shipped.
|
||||
That was not accurate for `main` and caused confusion.
|
||||
|
||||
Issue reference: `#1059`
|
||||
|
||||
## Roadmap (Planned)
|
||||
|
||||
Planned scoring strategy:
|
||||
|
||||
```text
|
||||
final_score = (similarity * 0.6) + (confidence * 0.4)
|
||||
```
|
||||
|
||||
Planned integration shape:
|
||||
1. Extract recent conversational context from filtered user/final-assistant turns.
|
||||
2. Compute TF-IDF cosine similarity between each fact and current context.
|
||||
3. Rank by weighted score and inject under token budget.
|
||||
4. Fall back to confidence-only ranking if context is unavailable.
|
||||
|
||||
## Validation
|
||||
|
||||
Current regression coverage includes:
|
||||
- facts inclusion in memory injection output
|
||||
- confidence ordering
|
||||
- token-budget-limited fact inclusion
|
||||
|
||||
Tests:
|
||||
- `backend/tests/test_memory_prompt_injection.py`
|
||||
38
deer-flow/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
Normal file
38
deer-flow/backend/docs/MEMORY_IMPROVEMENTS_SUMMARY.md
Normal file
@@ -0,0 +1,38 @@
|
||||
# Memory System Improvements - Summary
|
||||
|
||||
## Sync Note (2026-03-10)
|
||||
|
||||
This summary is synchronized with the `main` branch implementation.
|
||||
TF-IDF/context-aware retrieval is **planned**, not merged yet.
|
||||
|
||||
## Implemented
|
||||
|
||||
- Accurate token counting with `tiktoken` in memory injection.
|
||||
- Facts are injected into `<memory>` prompt content.
|
||||
- Facts are ordered by confidence and bounded by `max_injection_tokens`.
|
||||
|
||||
## Planned (Not Yet Merged)
|
||||
|
||||
- TF-IDF cosine similarity recall based on recent conversation context.
|
||||
- `current_context` parameter for `format_memory_for_injection`.
|
||||
- Weighted ranking (`similarity` + `confidence`).
|
||||
- Runtime extraction/injection flow for context-aware fact selection.
|
||||
|
||||
## Why This Sync Was Needed
|
||||
|
||||
Earlier docs described TF-IDF behavior as already implemented, which did not match code in `main`.
|
||||
This mismatch is tracked in issue `#1059`.
|
||||
|
||||
## Current API Shape
|
||||
|
||||
```python
|
||||
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
|
||||
```
|
||||
|
||||
No `current_context` argument is currently available in `main`.
|
||||
|
||||
## Verification Pointers
|
||||
|
||||
- Implementation: `packages/harness/deerflow/agents/memory/prompt.py`
|
||||
- Prompt assembly: `packages/harness/deerflow/agents/lead_agent/prompt.py`
|
||||
- Regression tests: `backend/tests/test_memory_prompt_injection.py`
|
||||
63
deer-flow/backend/docs/MEMORY_SETTINGS_REVIEW.md
Normal file
63
deer-flow/backend/docs/MEMORY_SETTINGS_REVIEW.md
Normal file
@@ -0,0 +1,63 @@
|
||||
# Memory Settings Review
|
||||
|
||||
Use this when reviewing the Memory Settings add/edit flow locally with the fewest possible manual steps.
|
||||
|
||||
## Quick Review
|
||||
|
||||
1. Start DeerFlow locally using any working development setup you already use.
|
||||
|
||||
Examples:
|
||||
|
||||
```bash
|
||||
make dev
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
make docker-start
|
||||
```
|
||||
|
||||
If you already have DeerFlow running locally, you can reuse that existing setup.
|
||||
|
||||
2. Load the sample memory fixture.
|
||||
|
||||
```bash
|
||||
python scripts/load_memory_sample.py
|
||||
```
|
||||
|
||||
3. Open `Settings > Memory`.
|
||||
|
||||
Default local URLs:
|
||||
- App: `http://localhost:2026`
|
||||
- Local frontend-only fallback: `http://localhost:3000`
|
||||
|
||||
## Minimal Manual Test
|
||||
|
||||
1. Click `Add fact`.
|
||||
2. Create a new fact with:
|
||||
- Content: `Reviewer-added memory fact`
|
||||
- Category: `testing`
|
||||
- Confidence: `0.88`
|
||||
3. Confirm the new fact appears immediately and shows `Manual` as the source.
|
||||
4. Edit the sample fact `This sample fact is intended for edit testing.` and change it to:
|
||||
- Content: `This sample fact was edited during manual review.`
|
||||
- Category: `testing`
|
||||
- Confidence: `0.91`
|
||||
5. Confirm the edited fact updates immediately.
|
||||
6. Refresh the page and confirm both the newly added fact and the edited fact still persist.
|
||||
|
||||
## Optional Sanity Checks
|
||||
|
||||
- Search `Reviewer-added` and confirm the new fact is matched.
|
||||
- Search `workflow` and confirm category text is searchable.
|
||||
- Switch between `All`, `Facts`, and `Summaries`.
|
||||
- Delete the disposable sample fact `Delete fact testing can target this disposable sample entry.` and confirm the list updates immediately.
|
||||
- Clear all memory and confirm the page enters the empty state.
|
||||
|
||||
## Fixture Files
|
||||
|
||||
- Sample fixture: `backend/docs/memory-settings-sample.json`
|
||||
- Default local runtime target: `backend/.deer-flow/memory.json`
|
||||
|
||||
The loader script creates a timestamped backup automatically before overwriting an existing runtime memory file.
|
||||
289
deer-flow/backend/docs/PATH_EXAMPLES.md
Normal file
289
deer-flow/backend/docs/PATH_EXAMPLES.md
Normal file
@@ -0,0 +1,289 @@
|
||||
# 文件路径使用示例
|
||||
|
||||
## 三种路径类型
|
||||
|
||||
DeerFlow 的文件上传系统返回三种不同的路径，每种路径用于不同的场景：
|
||||
|
||||
### 1. 实际文件系统路径 (path)
|
||||
|
||||
```
|
||||
.deer-flow/threads/{thread_id}/user-data/uploads/document.pdf
|
||||
```
|
||||
|
||||
**用途：**
|
||||
- 文件在服务器文件系统中的实际位置
|
||||
- 相对于 `backend/` 目录
|
||||
- 用于直接文件系统访问、备份、调试等
|
||||
|
||||
**示例：**
|
||||
```python
|
||||
# Python 代码中直接访问
|
||||
from pathlib import Path
|
||||
file_path = Path("backend/.deer-flow/threads/abc123/user-data/uploads/document.pdf")
|
||||
content = file_path.read_bytes()
|
||||
```
|
||||
|
||||
### 2. 虚拟路径 (virtual_path)
|
||||
|
||||
```
|
||||
/mnt/user-data/uploads/document.pdf
|
||||
```
|
||||
|
||||
**用途：**
|
||||
- Agent 在沙箱环境中使用的路径
|
||||
- 沙箱系统会自动映射到实际路径
|
||||
- Agent 的所有文件操作工具都使用这个路径
|
||||
|
||||
**示例：**
|
||||
Agent 在对话中使用：
|
||||
```python
|
||||
# Agent 使用 read_file 工具
|
||||
read_file(path="/mnt/user-data/uploads/document.pdf")
|
||||
|
||||
# Agent 使用 bash 工具
|
||||
bash(command="cat /mnt/user-data/uploads/document.pdf")
|
||||
```
|
||||
|
||||
### 3. HTTP 访问 URL (artifact_url)
|
||||
|
||||
```
|
||||
/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf
|
||||
```
|
||||
|
||||
**用途：**
|
||||
- 前端通过 HTTP 访问文件
|
||||
- 用于下载、预览文件
|
||||
- 可以直接在浏览器中打开
|
||||
|
||||
**示例：**
|
||||
```typescript
|
||||
// 前端 TypeScript/JavaScript 代码
|
||||
const threadId = 'abc123';
|
||||
const filename = 'document.pdf';
|
||||
|
||||
// 下载文件
|
||||
const downloadUrl = `/api/threads/${threadId}/artifacts/mnt/user-data/uploads/${filename}?download=true`;
|
||||
window.open(downloadUrl);
|
||||
|
||||
// 在新窗口预览
|
||||
const viewUrl = `/api/threads/${threadId}/artifacts/mnt/user-data/uploads/${filename}`;
|
||||
window.open(viewUrl, '_blank');
|
||||
|
||||
// 使用 fetch API 获取
|
||||
const response = await fetch(viewUrl);
|
||||
const blob = await response.blob();
|
||||
```
|
||||
|
||||
## 完整使用流程示例
|
||||
|
||||
### 场景：前端上传文件并让 Agent 处理
|
||||
|
||||
```typescript
|
||||
// 1. 前端上传文件
|
||||
async function uploadAndProcess(threadId: string, file: File) {
|
||||
// 上传文件
|
||||
const formData = new FormData();
|
||||
formData.append('files', file);
|
||||
|
||||
const uploadResponse = await fetch(
|
||||
`/api/threads/${threadId}/uploads`,
|
||||
{
|
||||
method: 'POST',
|
||||
body: formData
|
||||
}
|
||||
);
|
||||
|
||||
const uploadData = await uploadResponse.json();
|
||||
const fileInfo = uploadData.files[0];
|
||||
|
||||
console.log('文件信息：', fileInfo);
|
||||
// {
|
||||
// filename: "report.pdf",
|
||||
// path: ".deer-flow/threads/abc123/user-data/uploads/report.pdf",
|
||||
// virtual_path: "/mnt/user-data/uploads/report.pdf",
|
||||
// artifact_url: "/api/threads/abc123/artifacts/mnt/user-data/uploads/report.pdf",
|
||||
// markdown_file: "report.md",
|
||||
// markdown_path: ".deer-flow/threads/abc123/user-data/uploads/report.md",
|
||||
// markdown_virtual_path: "/mnt/user-data/uploads/report.md",
|
||||
// markdown_artifact_url: "/api/threads/abc123/artifacts/mnt/user-data/uploads/report.md"
|
||||
// }
|
||||
|
||||
// 2. 发送消息给 Agent
|
||||
await sendMessage(threadId, "请分析刚上传的 PDF 文件");
|
||||
|
||||
// Agent 会自动看到文件列表，包含：
|
||||
// - report.pdf (虚拟路径: /mnt/user-data/uploads/report.pdf)
|
||||
// - report.md (虚拟路径: /mnt/user-data/uploads/report.md)
|
||||
|
||||
// 3. 前端可以直接访问转换后的 Markdown
|
||||
const mdResponse = await fetch(fileInfo.markdown_artifact_url);
|
||||
const markdownContent = await mdResponse.text();
|
||||
console.log('Markdown 内容：', markdownContent);
|
||||
|
||||
// 4. 或者下载原始 PDF
|
||||
const downloadLink = document.createElement('a');
|
||||
downloadLink.href = fileInfo.artifact_url + '?download=true';
|
||||
downloadLink.download = fileInfo.filename;
|
||||
downloadLink.click();
|
||||
}
|
||||
```
|
||||
|
||||
## 路径转换表
|
||||
|
||||
| 场景 | 使用的路径类型 | 示例 |
|
||||
|------|---------------|------|
|
||||
| 服务器后端代码直接访问 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
|
||||
| Agent 工具调用 | `virtual_path` | `/mnt/user-data/uploads/file.pdf` |
|
||||
| 前端下载/预览 | `artifact_url` | `/api/threads/abc123/artifacts/mnt/user-data/uploads/file.pdf` |
|
||||
| 备份脚本 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
|
||||
| 日志记录 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
|
||||
|
||||
## 代码示例集合
|
||||
|
||||
### Python - 后端处理
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from deerflow.agents.middlewares.thread_data_middleware import THREAD_DATA_BASE_DIR
|
||||
|
||||
def process_uploaded_file(thread_id: str, filename: str):
|
||||
# 使用实际路径
|
||||
base_dir = Path.cwd() / THREAD_DATA_BASE_DIR / thread_id / "user-data" / "uploads"
|
||||
file_path = base_dir / filename
|
||||
|
||||
# 直接读取
|
||||
with open(file_path, 'rb') as f:
|
||||
content = f.read()
|
||||
|
||||
return content
|
||||
```
|
||||
|
||||
### JavaScript - 前端访问
|
||||
|
||||
```javascript
|
||||
// 列出已上传的文件
|
||||
async function listUploadedFiles(threadId) {
|
||||
const response = await fetch(`/api/threads/${threadId}/uploads/list`);
|
||||
const data = await response.json();
|
||||
|
||||
// 为每个文件创建下载链接
|
||||
data.files.forEach(file => {
|
||||
console.log(`文件: ${file.filename}`);
|
||||
console.log(`下载: ${file.artifact_url}?download=true`);
|
||||
console.log(`预览: ${file.artifact_url}`);
|
||||
|
||||
// 如果是文档，还有 Markdown 版本
|
||||
if (file.markdown_artifact_url) {
|
||||
console.log(`Markdown: ${file.markdown_artifact_url}`);
|
||||
}
|
||||
});
|
||||
|
||||
return data.files;
|
||||
}
|
||||
|
||||
// 删除文件
|
||||
async function deleteFile(threadId, filename) {
|
||||
const response = await fetch(
|
||||
`/api/threads/${threadId}/uploads/${filename}`,
|
||||
{ method: 'DELETE' }
|
||||
);
|
||||
return response.json();
|
||||
}
|
||||
```
|
||||
|
||||
### React 组件示例
|
||||
|
||||
```tsx
|
||||
import React, { useState, useEffect } from 'react';
|
||||
|
||||
interface UploadedFile {
|
||||
filename: string;
|
||||
size: number;
|
||||
path: string;
|
||||
virtual_path: string;
|
||||
artifact_url: string;
|
||||
extension: string;
|
||||
modified: number;
|
||||
markdown_artifact_url?: string;
|
||||
}
|
||||
|
||||
function FileUploadList({ threadId }: { threadId: string }) {
|
||||
const [files, setFiles] = useState<UploadedFile[]>([]);
|
||||
|
||||
useEffect(() => {
|
||||
fetchFiles();
|
||||
}, [threadId]);
|
||||
|
||||
async function fetchFiles() {
|
||||
const response = await fetch(`/api/threads/${threadId}/uploads/list`);
|
||||
const data = await response.json();
|
||||
setFiles(data.files);
|
||||
}
|
||||
|
||||
async function handleUpload(event: React.ChangeEvent<HTMLInputElement>) {
|
||||
const fileList = event.target.files;
|
||||
if (!fileList) return;
|
||||
|
||||
const formData = new FormData();
|
||||
Array.from(fileList).forEach(file => {
|
||||
formData.append('files', file);
|
||||
});
|
||||
|
||||
await fetch(`/api/threads/${threadId}/uploads`, {
|
||||
method: 'POST',
|
||||
body: formData
|
||||
});
|
||||
|
||||
fetchFiles(); // 刷新列表
|
||||
}
|
||||
|
||||
async function handleDelete(filename: string) {
|
||||
await fetch(`/api/threads/${threadId}/uploads/${filename}`, {
|
||||
method: 'DELETE'
|
||||
});
|
||||
fetchFiles(); // 刷新列表
|
||||
}
|
||||
|
||||
return (
|
||||
<div>
|
||||
<input type="file" multiple onChange={handleUpload} />
|
||||
|
||||
<ul>
|
||||
{files.map(file => (
|
||||
<li key={file.filename}>
|
||||
<span>{file.filename}</span>
|
||||
<a href={file.artifact_url} target="_blank">预览</a>
|
||||
<a href={`${file.artifact_url}?download=true`}>下载</a>
|
||||
{file.markdown_artifact_url && (
|
||||
<a href={file.markdown_artifact_url} target="_blank">Markdown</a>
|
||||
)}
|
||||
<button onClick={() => handleDelete(file.filename)}>删除</button>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **路径安全性**
|
||||
- 实际路径（`path`）包含线程 ID，确保隔离
|
||||
- API 会验证路径，防止目录遍历攻击
|
||||
- 前端不应直接使用 `path`，而应使用 `artifact_url`
|
||||
|
||||
2. **Agent 使用**
|
||||
- Agent 只能看到和使用 `virtual_path`
|
||||
- 沙箱系统自动映射到实际路径
|
||||
- Agent 不需要知道实际的文件系统结构
|
||||
|
||||
3. **前端集成**
|
||||
- 始终使用 `artifact_url` 访问文件
|
||||
- 不要尝试直接访问文件系统路径
|
||||
- 使用 `?download=true` 参数强制下载
|
||||
|
||||
4. **Markdown 转换**
|
||||
- 转换成功时，会返回额外的 `markdown_*` 字段
|
||||
- 建议优先使用 Markdown 版本（更易处理）
|
||||
- 原始文件始终保留
|
||||
55
deer-flow/backend/docs/README.md
Normal file
55
deer-flow/backend/docs/README.md
Normal file
@@ -0,0 +1,55 @@
|
||||
# Documentation
|
||||
|
||||
This directory contains detailed documentation for the DeerFlow backend.
|
||||
|
||||
## Quick Links
|
||||
|
||||
| Document | Description |
|
||||
|----------|-------------|
|
||||
| [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
|
||||
| [API.md](API.md) | Complete API reference |
|
||||
| [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
|
||||
| [SETUP.md](SETUP.md) | Quick setup guide |
|
||||
|
||||
## Feature Documentation
|
||||
|
||||
| Document | Description |
|
||||
|----------|-------------|
|
||||
| [STREAMING.md](STREAMING.md) | Token-level streaming design: Gateway vs DeerFlowClient paths, `stream_mode` semantics, per-id dedup |
|
||||
| [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
|
||||
| [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
|
||||
| [summarization.md](summarization.md) | Context summarization feature |
|
||||
| [plan_mode_usage.md](plan_mode_usage.md) | Plan mode with TodoList |
|
||||
| [AUTO_TITLE_GENERATION.md](AUTO_TITLE_GENERATION.md) | Automatic title generation |
|
||||
|
||||
## Development
|
||||
|
||||
| Document | Description |
|
||||
|----------|-------------|
|
||||
| [TODO.md](TODO.md) | Planned features and known issues |
|
||||
|
||||
## Getting Started
|
||||
|
||||
1. **New to DeerFlow?** Start with [SETUP.md](SETUP.md) for quick installation
|
||||
2. **Configuring the system?** See [CONFIGURATION.md](CONFIGURATION.md)
|
||||
3. **Understanding the architecture?** Read [ARCHITECTURE.md](ARCHITECTURE.md)
|
||||
4. **Building integrations?** Check [API.md](API.md) for API reference
|
||||
|
||||
## Document Organization
|
||||
|
||||
```
|
||||
docs/
|
||||
├── README.md # This file
|
||||
├── ARCHITECTURE.md # System architecture
|
||||
├── API.md # API reference
|
||||
├── CONFIGURATION.md # Configuration guide
|
||||
├── SETUP.md # Setup instructions
|
||||
├── FILE_UPLOAD.md # File upload feature
|
||||
├── PATH_EXAMPLES.md # Path usage examples
|
||||
├── summarization.md # Summarization feature
|
||||
├── plan_mode_usage.md # Plan mode feature
|
||||
├── STREAMING.md # Token-level streaming design
|
||||
├── AUTO_TITLE_GENERATION.md # Title generation
|
||||
├── TITLE_GENERATION_IMPLEMENTATION.md # Title implementation details
|
||||
└── TODO.md # Roadmap and issues
|
||||
```
|
||||
92
deer-flow/backend/docs/SETUP.md
Normal file
92
deer-flow/backend/docs/SETUP.md
Normal file
@@ -0,0 +1,92 @@
|
||||
# Setup Guide
|
||||
|
||||
Quick setup instructions for DeerFlow.
|
||||
|
||||
## Configuration Setup
|
||||
|
||||
DeerFlow uses a YAML configuration file that should be placed in the **project root directory**.
|
||||
|
||||
### Steps
|
||||
|
||||
1. **Navigate to project root**:
|
||||
```bash
|
||||
cd /path/to/deer-flow
|
||||
```
|
||||
|
||||
2. **Copy example configuration**:
|
||||
```bash
|
||||
cp config.example.yaml config.yaml
|
||||
```
|
||||
|
||||
3. **Edit configuration**:
|
||||
```bash
|
||||
# Option A: Set environment variables (recommended)
|
||||
export OPENAI_API_KEY="your-key-here"
|
||||
|
||||
# Option B: Edit config.yaml directly
|
||||
vim config.yaml # or your preferred editor
|
||||
```
|
||||
|
||||
4. **Verify configuration**:
|
||||
```bash
|
||||
cd backend
|
||||
python -c "from deerflow.config import get_app_config; print('✓ Config loaded:', get_app_config().models[0].name)"
|
||||
```
|
||||
|
||||
## Important Notes
|
||||
|
||||
- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
|
||||
- **Git**: `config.yaml` is automatically ignored by git (contains secrets)
|
||||
- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
|
||||
|
||||
## Configuration File Locations
|
||||
|
||||
The backend searches for `config.yaml` in this order:
|
||||
|
||||
1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
|
||||
2. `backend/config.yaml` (current directory when running from backend/)
|
||||
3. `deer-flow/config.yaml` (parent directory - **recommended location**)
|
||||
|
||||
**Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
|
||||
|
||||
## Sandbox Setup (Optional but Recommended)
|
||||
|
||||
If you plan to use Docker/Container-based sandbox (configured in `config.yaml` under `sandbox.use: deerflow.community.aio_sandbox:AioSandboxProvider`), it's highly recommended to pre-pull the container image:
|
||||
|
||||
```bash
|
||||
# From project root
|
||||
make setup-sandbox
|
||||
```
|
||||
|
||||
**Why pre-pull?**
|
||||
- The sandbox image (~500MB+) is pulled on first use, causing a long wait
|
||||
- Pre-pulling provides clear progress indication
|
||||
- Avoids confusion when first using the agent
|
||||
|
||||
If you skip this step, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Config file not found
|
||||
|
||||
```bash
|
||||
# Check where the backend is looking
|
||||
cd deer-flow/backend
|
||||
python -c "from deerflow.config.app_config import AppConfig; print(AppConfig.resolve_config_path())"
|
||||
```
|
||||
|
||||
If it can't find the config:
|
||||
1. Ensure you've copied `config.example.yaml` to `config.yaml`
|
||||
2. Verify you're in the correct directory
|
||||
3. Check the file exists: `ls -la ../config.yaml`
|
||||
|
||||
### Permission denied
|
||||
|
||||
```bash
|
||||
chmod 600 ../config.yaml # Protect sensitive configuration
|
||||
```
|
||||
|
||||
## See Also
|
||||
|
||||
- [Configuration Guide](CONFIGURATION.md) - Detailed configuration options
|
||||
- [Architecture Overview](../CLAUDE.md) - System architecture
|
||||
351
deer-flow/backend/docs/STREAMING.md
Normal file
351
deer-flow/backend/docs/STREAMING.md
Normal file
@@ -0,0 +1,351 @@
|
||||
# DeerFlow 流式输出设计
|
||||
|
||||
本文档解释 DeerFlow 是如何把 LangGraph agent 的事件流端到端送到两类消费者（HTTP 客户端、嵌入式 Python 调用方）的：两条路径为什么**必须**并存、它们各自的契约是什么、以及设计里那些 non-obvious 的不变式。
|
||||
|
||||
---
|
||||
|
||||
## TL;DR
|
||||
|
||||
- DeerFlow 有**两条并行**的流式路径：**Gateway 路径**（async / HTTP SSE / JSON 序列化）服务浏览器和 IM 渠道；**DeerFlowClient 路径**（sync / in-process / 原生 LangChain 对象）服务 Jupyter、脚本、测试。它们**无法合并**——消费者模型不同。
|
||||
- 两条路径都从 `create_agent()` 工厂出发，核心都是订阅 LangGraph 的 `stream_mode=["values", "messages", "custom"]`。`values` 是节点级 state 快照，`messages` 是 LLM token 级 delta，`custom` 是显式 `StreamWriter` 事件。**这三种模式不是详细程度的梯度，是三个独立的事件源**，要 token 流就必须显式订阅 `messages`。
|
||||
- 嵌入式 client 为每个 `stream()` 调用维护三个 `set[str]`：`seen_ids` / `streamed_ids` / `counted_usage_ids`。三者看起来相似但管理**三个独立的不变式**，不能合并。
|
||||
|
||||
---
|
||||
|
||||
## 为什么有两条流式路径
|
||||
|
||||
两条路径服务的消费者模型根本不同：
|
||||
|
||||
| 维度 | Gateway 路径 | DeerFlowClient 路径 |
|
||||
|---|---|---|
|
||||
| 入口 | FastAPI `/runs/stream` endpoint | `DeerFlowClient.stream(message)` |
|
||||
| 触发层 | `runtime/runs/worker.py::run_agent` | `packages/harness/deerflow/client.py::DeerFlowClient.stream` |
|
||||
| 执行模型 | `async def` + `agent.astream()` | sync generator + `agent.stream()` |
|
||||
| 事件传输 | `StreamBridge`（asyncio Queue）+ `sse_consumer` | 直接 `yield` |
|
||||
| 序列化 | `serialize(chunk)` → 纯 JSON dict，匹配 LangGraph Platform wire 格式 | `StreamEvent.data`，携带原生 LangChain 对象 |
|
||||
| 消费者 | 前端 `useStream` React hook、飞书/Slack/Telegram channel、LangGraph SDK 客户端 | Jupyter notebook、集成测试、内部 Python 脚本 |
|
||||
| 生命周期管理 | `RunManager`：run_id 跟踪、disconnect 语义、multitask 策略、heartbeat | 无；函数返回即结束 |
|
||||
| 断连恢复 | `Last-Event-ID` SSE 重连 | 无需要 |
|
||||
|
||||
**两条路径的存在是 DRY 的刻意妥协**：Gateway 的全部基础设施（async + Queue + JSON + RunManager）**都是为了跨网络边界把事件送给 HTTP 消费者**。当生产者（agent）和消费者（Python 调用栈）在同一个进程时，这整套东西都是纯开销。
|
||||
|
||||
### 为什么不能让 DeerFlowClient 复用 Gateway
|
||||
|
||||
曾经考虑过三种复用方案，都被否决：
|
||||
|
||||
1. **让 `client.stream()` 变成 `async def client.astream()`**
|
||||
breaking change。用户用不上的 `async for` / `asyncio.run()` 要硬塞进 Jupyter notebook 和同步脚本。DeerFlowClient 的一大卖点（"把 agent 当普通函数调用"）直接消失。
|
||||
|
||||
2. **在 `client.stream()` 内部起一个独立事件循环线程，用 `StreamBridge` 在 sync/async 之间做桥接**
|
||||
引入线程池、队列、信号量。为了"消除重复"，把**复杂度**代替代码行数引进来。是典型的"wrong abstraction"——开销高于复用收益。
|
||||
|
||||
3. **让 `run_agent` 自己兼容 sync mode**
|
||||
给 Gateway 加一条用不到的死分支，污染 worker.py 的焦点。
|
||||
|
||||
所以两条路径的事件处理逻辑会**相似但不共享**。这是刻意设计，不是疏忽。
|
||||
|
||||
---
|
||||
|
||||
## LangGraph `stream_mode` 三层语义
|
||||
|
||||
LangGraph 的 `agent.stream(stream_mode=[...])` 是**多路复用**接口：一次订阅多个 mode，每个 mode 是一个独立的事件源。三种核心 mode：
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
classDef values fill:#B8C5D1,stroke:#5A6B7A,color:#2C3E50
|
||||
classDef messages fill:#C9B8A8,stroke:#7A6B5A,color:#2C3E50
|
||||
classDef custom fill:#B5C4B1,stroke:#5A7A5A,color:#2C3E50
|
||||
|
||||
subgraph LG["LangGraph agent graph"]
|
||||
direction TB
|
||||
Node1["node: LLM call"]
|
||||
Node2["node: tool call"]
|
||||
Node3["node: reducer"]
|
||||
end
|
||||
|
||||
LG -->|"每个节点完成后"| V["values: 完整 state 快照"]
|
||||
Node1 -->|"LLM 每产生一个 token"| M["messages: (AIMessageChunk, meta)"]
|
||||
Node1 -->|"StreamWriter.write()"| C["custom: 任意 dict"]
|
||||
|
||||
class V values
|
||||
class M messages
|
||||
class C custom
|
||||
```
|
||||
|
||||
| Mode | 发射时机 | Payload | 粒度 |
|
||||
|---|---|---|---|
|
||||
| `values` | 每个 graph 节点完成后 | 完整 state dict（title、messages、artifacts）| 节点级 |
|
||||
| `messages` | LLM 每次 yield 一个 chunk；tool 节点完成时 | `(AIMessageChunk \| ToolMessage, metadata_dict)` | token 级 |
|
||||
| `custom` | 用户代码显式调用 `StreamWriter.write()` | 任意 dict | 应用定义 |
|
||||
|
||||
### 两套命名的由来
|
||||
|
||||
同一件事在**三个协议层**有三个名字：
|
||||
|
||||
```
|
||||
Application HTTP / SSE LangGraph Graph
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ frontend │ │ LangGraph │ │ agent.astream│
|
||||
│ useStream │──"messages- │ Platform SDK │──"messages"──│ graph.astream│
|
||||
│ Feishu IM │ tuple"──────│ HTTP wire │ │ │
|
||||
└──────────────┘ └──────────────┘ └──────────────┘
|
||||
```
|
||||
|
||||
- **Graph 层**（`agent.stream` / `agent.astream`）：LangGraph Python 直接 API，mode 叫 **`"messages"`**。
|
||||
- **Platform SDK 层**（`langgraph-sdk` HTTP client）：跨进程 HTTP 契约，mode 叫 **`"messages-tuple"`**。
|
||||
- **Gateway worker** 显式做翻译：`if m == "messages-tuple": lg_modes.append("messages")`（`runtime/runs/worker.py:117-121`）。
|
||||
|
||||
**后果**：`DeerFlowClient.stream()` 直接调 `agent.stream()`（Graph 层），所以必须传 `"messages"`。`app/channels/manager.py` 通过 `langgraph-sdk` 走 HTTP SDK，所以传 `"messages-tuple"`。**这两个字符串不能互相替代**，也不能抽成"一个共享常量"——它们是不同协议层的 type alias，共享只会让某一层说不是它母语的话。
|
||||
|
||||
---
|
||||
|
||||
## Gateway 路径：async + HTTP SSE
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant Client as HTTP Client
|
||||
participant API as FastAPI<br/>thread_runs.py
|
||||
participant Svc as services.py<br/>start_run
|
||||
participant Worker as worker.py<br/>run_agent (async)
|
||||
participant Bridge as StreamBridge<br/>(asyncio.Queue)
|
||||
participant Agent as LangGraph<br/>agent.astream
|
||||
participant SSE as sse_consumer
|
||||
|
||||
Client->>API: POST /runs/stream
|
||||
API->>Svc: start_run(body)
|
||||
Svc->>Bridge: create bridge
|
||||
Svc->>Worker: asyncio.create_task(run_agent(...))
|
||||
Svc-->>API: StreamingResponse(sse_consumer)
|
||||
API-->>Client: event-stream opens
|
||||
|
||||
par worker (producer)
|
||||
Worker->>Agent: astream(stream_mode=lg_modes)
|
||||
loop 每个 chunk
|
||||
Agent-->>Worker: (mode, chunk)
|
||||
Worker->>Bridge: publish(run_id, event, serialize(chunk))
|
||||
end
|
||||
Worker->>Bridge: publish_end(run_id)
|
||||
and sse_consumer (consumer)
|
||||
SSE->>Bridge: subscribe(run_id)
|
||||
loop 每个 event
|
||||
Bridge-->>SSE: StreamEvent
|
||||
SSE-->>Client: "event: <name>\ndata: <json>\n\n"
|
||||
end
|
||||
end
|
||||
```
|
||||
|
||||
关键组件：
|
||||
|
||||
- `runtime/runs/worker.py::run_agent` — 在 `asyncio.Task` 里跑 `agent.astream()`，把每个 chunk 通过 `serialize(chunk, mode=mode)` 转成 JSON，再 `bridge.publish()`。
|
||||
- `runtime/stream_bridge` — 抽象 Queue。`publish/subscribe` 解耦生产者和消费者，支持 `Last-Event-ID` 重连、心跳、多订阅者 fan-out。
|
||||
- `app/gateway/services.py::sse_consumer` — 从 bridge 订阅，格式化为 SSE wire 帧。
|
||||
- `runtime/serialization.py::serialize` — mode-aware 序列化；`messages` mode 下 `serialize_messages_tuple` 把 `(chunk, metadata)` 转成 `[chunk.model_dump(), metadata]`。
|
||||
|
||||
**`StreamBridge` 的存在价值**：当生产者（`run_agent` 任务）和消费者（HTTP 连接）在不同的 asyncio task 里运行时，需要一个可以跨 task 传递事件的中介。Queue 同时还承担断连重连的 buffer 和多订阅者的 fan-out。
|
||||
|
||||
---
|
||||
|
||||
## DeerFlowClient 路径：sync + in-process
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant User as Python caller
|
||||
participant Client as DeerFlowClient.stream
|
||||
participant Agent as LangGraph<br/>agent.stream (sync)
|
||||
|
||||
User->>Client: for event in client.stream("hi"):
|
||||
Client->>Agent: stream(stream_mode=["values","messages","custom"])
|
||||
loop 每个 chunk
|
||||
Agent-->>Client: (mode, chunk)
|
||||
Client->>Client: 分发 mode<br/>构建 StreamEvent
|
||||
Client-->>User: yield StreamEvent
|
||||
end
|
||||
Client-->>User: yield StreamEvent(type="end")
|
||||
```
|
||||
|
||||
对比之下，sync 路径的每个环节都是显著更少的移动部件：
|
||||
|
||||
- 没有 `RunManager` —— 一次 `stream()` 调用对应一次生命周期，无需 run_id。
|
||||
- 没有 `StreamBridge` —— 直接 `yield`，生产和消费在同一个 Python 调用栈，不需要跨 task 中介。
|
||||
- 没有 JSON 序列化 —— `StreamEvent.data` 直接装原生 LangChain 对象（`AIMessage.content`、`usage_metadata` 的 `UsageMetadata` TypedDict）。Jupyter 用户拿到的是真正的类型，不是匿名 dict。
|
||||
- 没有 asyncio —— 调用者可以直接 `for event in ...`，不必写 `async for`。
|
||||
|
||||
---
|
||||
|
||||
## 消费语义：delta vs cumulative
|
||||
|
||||
LangGraph `messages` mode 给出的是 **delta**：每个 `AIMessageChunk.content` 只包含这一次新 yield 的 token，**不是**从头的累计文本。
|
||||
|
||||
这个语义和 LangChain 的 `fs2 Stream` 风格一致：**上游发增量，下游负责累加**。Gateway 路径里前端 `useStream` React hook 自己维护累加器；DeerFlowClient 路径里 `chat()` 方法替调用者做累加。
|
||||
|
||||
### `DeerFlowClient.chat()` 的 O(n) 累加器
|
||||
|
||||
```python
|
||||
chunks: dict[str, list[str]] = {}
|
||||
last_id: str = ""
|
||||
for event in self.stream(message, thread_id=thread_id, **kwargs):
|
||||
if event.type == "messages-tuple" and event.data.get("type") == "ai":
|
||||
msg_id = event.data.get("id") or ""
|
||||
delta = event.data.get("content", "")
|
||||
if delta:
|
||||
chunks.setdefault(msg_id, []).append(delta)
|
||||
last_id = msg_id
|
||||
return "".join(chunks.get(last_id, ()))
|
||||
```
|
||||
|
||||
**为什么不是 `buffers[id] = buffers.get(id,"") + delta`**：CPython 的字符串 in-place concat 优化仅在 refcount=1 且 LHS 是 local name 时生效；这里字符串存在 dict 里被 reassign，优化失效，每次都是 O(n) 拷贝 → 总体 O(n²)。实测 50 KB / 5000 chunk 的回复要 100-300ms 纯拷贝开销。用 `list` + `"".join()` 是 O(n)。
|
||||
|
||||
---
|
||||
|
||||
## 三个 id set 为什么不能合并
|
||||
|
||||
`DeerFlowClient.stream()` 在一次调用生命周期内维护三个 `set[str]`：
|
||||
|
||||
```python
|
||||
seen_ids: set[str] = set() # values 路径内部 dedup
|
||||
streamed_ids: set[str] = set() # messages → values 跨模式 dedup
|
||||
counted_usage_ids: set[str] = set() # usage_metadata 幂等计数
|
||||
```
|
||||
|
||||
乍看像是"三份几乎一样的东西"，实际每个管**不同的不变式**。
|
||||
|
||||
| Set | 负责的不变式 | 被谁填充 | 被谁查询 |
|
||||
|---|---|---|---|
|
||||
| `seen_ids` | 连续两个 `values` 快照里同一条 message 只生成一个 `messages-tuple` 事件 | values 分支每处理一条消息就加入 | values 分支处理下一条消息前检查 |
|
||||
| `streamed_ids` | 如果一条消息已经通过 `messages` 模式 token 级流过，values 快照到达时**不要**再合成一次完整 `messages-tuple` | messages 分支每发一个 AI/tool 事件就加入 | values 分支看到消息时检查 |
|
||||
| `counted_usage_ids` | 同一个 `usage_metadata` 在 messages 末尾 chunk 和 values 快照的 final AIMessage 里各带一份，**累计总量只算一次** | `_account_usage()` 每次接受 usage 就加入 | `_account_usage()` 每次调用时检查 |
|
||||
|
||||
### 为什么不能只用一个 set
|
||||
|
||||
关键观察：**同一个 message id 在这三个 set 里的加入时机不同**。
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant M as messages mode
|
||||
participant V as values mode
|
||||
participant SS as streamed_ids
|
||||
participant SU as counted_usage_ids
|
||||
participant SE as seen_ids
|
||||
|
||||
Note over M: 第一个 AI text chunk 到达
|
||||
M->>SS: add(msg_id)
|
||||
Note over M: 最后一个 chunk 带 usage
|
||||
M->>SU: add(msg_id)
|
||||
Note over V: snapshot 到达，包含同一条 AI message
|
||||
V->>SE: add(msg_id)
|
||||
V->>SS: 查询 → 已存在，跳过文本合成
|
||||
V->>SU: 查询 → 已存在，不重复计数
|
||||
```
|
||||
|
||||
- `seen_ids` **永远在 values 快照到达时**加入，所以它是 "values 已处理" 的标记。一条只出现在 messages 流里的消息（罕见但可能），`seen_ids` 里永远没有它。
|
||||
- `streamed_ids` **在 messages 流的第一个有效事件时**加入。一条只通过 values 快照到达的非 AI 消息（HumanMessage、被 truncate 的 tool 消息），`streamed_ids` 里永远没有它。
|
||||
- `counted_usage_ids` **只在看到非空 `usage_metadata` 时**加入。一条完全没有 usage 的消息（tool message、错误消息）永远不会进去。
|
||||
|
||||
**集合包含关系**：`counted_usage_ids ⊆ (streamed_ids ∪ seen_ids)` 大致成立，但**不是严格子集**，因为一条消息可以在 messages 模式流完 text 但**在最后那个带 usage 的 chunk 之前**就被 values snapshot 赶上——此时它已经在 `streamed_ids` 里，但还不在 `counted_usage_ids` 里。把它们合并成一个 dict-of-flags 会让这个微妙的时序依赖**从类型系统里消失**，变成注释里的一句话。三个独立的 set 把不变式显式化了：每个 set 名对应一个可以口头回答的问题。
|
||||
|
||||
---
|
||||
|
||||
## 端到端：一次真实对话的事件时序
|
||||
|
||||
假设调用 `client.stream("Count from 1 to 15")`，LLM 给出 "one\ntwo\n...\nfifteen"（88 字符），tokenizer 把它拆成 ~35 个 BPE chunk。下面是事件到达序列的精简版：
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant U as User
|
||||
participant C as DeerFlowClient
|
||||
participant A as LangGraph<br/>agent.stream
|
||||
|
||||
U->>C: stream("Count ... 15")
|
||||
C->>A: stream(mode=["values","messages","custom"])
|
||||
|
||||
A-->>C: ("values", {messages: [HumanMessage]})
|
||||
C-->>U: StreamEvent(type="values", ...)
|
||||
|
||||
Note over A,C: LLM 开始 yield token
|
||||
loop 35 次，约 476ms
|
||||
A-->>C: ("messages", (AIMessageChunk(content="ele"), meta))
|
||||
C->>C: streamed_ids.add(ai-1)
|
||||
C-->>U: StreamEvent(type="messages-tuple",<br/>data={type:ai, content:"ele", id:ai-1})
|
||||
end
|
||||
|
||||
Note over A: LLM finish_reason=stop，最后一个 chunk 带 usage
|
||||
A-->>C: ("messages", (AIMessageChunk(content="", usage_metadata={...}), meta))
|
||||
C->>C: counted_usage_ids.add(ai-1)<br/>(无文本，不 yield)
|
||||
|
||||
A-->>C: ("values", {messages: [..., AIMessage(complete)]})
|
||||
C->>C: ai-1 in streamed_ids → 跳过合成
|
||||
C->>C: 捕获 usage (已在 counted_usage_ids，no-op)
|
||||
C-->>U: StreamEvent(type="values", ...)
|
||||
|
||||
C-->>U: StreamEvent(type="end", data={usage:{...}})
|
||||
```
|
||||
|
||||
关键观察：
|
||||
|
||||
1. 用户看到 **35 个 messages-tuple 事件**，跨越约 476ms，每个事件带一个 token delta 和同一个 `id=ai-1`。
|
||||
2. 最后一个 `values` 快照里的 `AIMessage` **不会**再触发一个完整的 `messages-tuple` 事件——因为 `ai-1 in streamed_ids` 跳过了合成。
|
||||
3. `end` 事件里的 `usage` 正好等于那一份 cumulative usage，**不是它的两倍**——`counted_usage_ids` 在 messages 末尾 chunk 上已经吸收了，values 分支的重复访问是 no-op。
|
||||
4. 消费者拿到的 `content` 是**增量**："ele" 只包含 3 个字符，不是 "one\ntwo\n...ele"。想要完整文本要按 `id` 累加，`chat()` 已经帮你做了。
|
||||
|
||||
---
|
||||
|
||||
## 为什么这个设计容易出 bug，以及测试策略
|
||||
|
||||
本文档的直接起因是 bytedance/deer-flow#1969：`DeerFlowClient.stream()` 原本只订阅 `["values", "custom"]`，**漏了 `"messages"`**。结果 `client.stream("hello")` 等价于一次性返回，视觉上和 `chat()` 没区别。
|
||||
|
||||
这类 bug 有三个结构性原因：
|
||||
|
||||
1. **多协议层命名**：`messages` / `messages-tuple` / HTTP SSE `messages` 是同一概念的三个名字。在其中一层出错不会在另外两层报错。
|
||||
2. **多消费者模型**：Gateway 和 DeerFlowClient 是两套独立实现，**没有单一的"订阅哪些 mode"的 single source of truth**。前者订阅对了不代表后者也订阅对了。
|
||||
3. **mock 测试绕开了真实路径**：老测试用 `agent.stream.return_value = iter([dict_chunk, ...])` 喂 values 形状的 dict 模拟 state 快照。这样构造的输入**永远不会进入 `messages` mode 分支**，所以即使 `stream_mode` 里少一个元素，CI 依然全绿。
|
||||
|
||||
### 防御手段
|
||||
|
||||
真正的防线是**显式断言 "messages" mode 被订阅 + 用真实 chunk shape mock**：
|
||||
|
||||
```python
|
||||
# tests/test_client.py::test_messages_mode_emits_token_deltas
|
||||
agent.stream.return_value = iter([
|
||||
("messages", (AIMessageChunk(content="Hel", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="lo ", id="ai-1"), {})),
|
||||
("messages", (AIMessageChunk(content="world!", id="ai-1"), {})),
|
||||
("values", {"messages": [HumanMessage(...), AIMessage(content="Hello world!", id="ai-1")]}),
|
||||
])
|
||||
# ...
|
||||
assert [e.data["content"] for e in ai_text_events] == ["Hel", "lo ", "world!"]
|
||||
assert len(ai_text_events) == 3 # values snapshot must NOT re-synthesize
|
||||
assert "messages" in agent.stream.call_args.kwargs["stream_mode"]
|
||||
```
|
||||
|
||||
**为什么这比"抽一个共享常量"更有效**：共享常量只能保证"用它的人写对字符串"，但新增消费者的人可能根本不知道常量在哪。行为断言强制任何改动都要穿过**实际执行路径**，改回 `["values", "custom"]` 会立刻让 `assert "messages" in ...` 失败。
|
||||
|
||||
### 活体信号：BPE 子词边界
|
||||
|
||||
回归的最终验证是让真实 LLM 数 1-15，然后看是否能在输出里看到 tokenizer 的子词切分：
|
||||
|
||||
```
|
||||
[5.460s] 'ele' / 'ven' eleven 被拆成两个 token
|
||||
[5.508s] 'tw' / 'elve' twelve 拆两个
|
||||
[5.568s] 'th' / 'irteen' thirteen 拆两个
|
||||
[5.623s] 'four'/ 'teen' fourteen 拆两个
|
||||
[5.677s] 'f' / 'if' / 'teen' fifteen 拆三个
|
||||
```
|
||||
|
||||
子词切分是 tokenizer 的外部事实，**无法伪造**。能看到它就说明数据流**逐 chunk** 地穿过了整条管道，没有被任何中间层缓冲成整段。这种"活体信号"在流式系统里是比单元测试更高置信度的证据。
|
||||
|
||||
---
|
||||
|
||||
## 相关源码定位
|
||||
|
||||
| 关心什么 | 看这里 |
|
||||
|---|---|
|
||||
| DeerFlowClient 嵌入式流 | `packages/harness/deerflow/client.py::DeerFlowClient.stream` |
|
||||
| `chat()` 的 delta 累加器 | `packages/harness/deerflow/client.py::DeerFlowClient.chat` |
|
||||
| Gateway async 流 | `packages/harness/deerflow/runtime/runs/worker.py::run_agent` |
|
||||
| HTTP SSE 帧输出 | `app/gateway/services.py::sse_consumer` / `format_sse` |
|
||||
| 序列化到 wire 格式 | `packages/harness/deerflow/runtime/serialization.py` |
|
||||
| LangGraph mode 命名翻译 | `packages/harness/deerflow/runtime/runs/worker.py:117-121` |
|
||||
| 飞书渠道的增量卡片更新 | `app/channels/manager.py::_handle_streaming_chat` |
|
||||
| Channels 自带的 delta/cumulative 防御性累加 | `app/channels/manager.py::_merge_stream_text` |
|
||||
| Frontend useStream 支持的 mode 集合 | `frontend/src/core/api/stream-mode.ts` |
|
||||
| 核心回归测试 | `backend/tests/test_client.py::TestStream::test_messages_mode_emits_token_deltas` |
|
||||
222
deer-flow/backend/docs/TITLE_GENERATION_IMPLEMENTATION.md
Normal file
222
deer-flow/backend/docs/TITLE_GENERATION_IMPLEMENTATION.md
Normal file
@@ -0,0 +1,222 @@
|
||||
# 自动 Title 生成功能实现总结
|
||||
|
||||
## ✅ 已完成的工作
|
||||
|
||||
### 1. 核心实现文件
|
||||
|
||||
#### [`packages/harness/deerflow/agents/thread_state.py`](../packages/harness/deerflow/agents/thread_state.py)
|
||||
- ✅ 添加 `title: str | None = None` 字段到 `ThreadState`
|
||||
|
||||
#### [`packages/harness/deerflow/config/title_config.py`](../packages/harness/deerflow/config/title_config.py) (新建)
|
||||
- ✅ 创建 `TitleConfig` 配置类
|
||||
- ✅ 支持配置：enabled, max_words, max_chars, model_name, prompt_template
|
||||
- ✅ 提供 `get_title_config()` 和 `set_title_config()` 函数
|
||||
- ✅ 提供 `load_title_config_from_dict()` 从配置文件加载
|
||||
|
||||
#### [`packages/harness/deerflow/agents/middlewares/title_middleware.py`](../packages/harness/deerflow/agents/middlewares/title_middleware.py) (新建)
|
||||
- ✅ 创建 `TitleMiddleware` 类
|
||||
- ✅ 实现 `_should_generate_title()` 检查是否需要生成
|
||||
- ✅ 实现 `_generate_title()` 调用 LLM 生成标题
|
||||
- ✅ 实现 `after_agent()` 钩子，在首次对话后自动触发
|
||||
- ✅ 包含 fallback 策略（LLM 失败时使用用户消息前几个词）
|
||||
|
||||
#### [`packages/harness/deerflow/config/app_config.py`](../packages/harness/deerflow/config/app_config.py)
|
||||
- ✅ 导入 `load_title_config_from_dict`
|
||||
- ✅ 在 `from_file()` 中加载 title 配置
|
||||
|
||||
#### [`packages/harness/deerflow/agents/lead_agent/agent.py`](../packages/harness/deerflow/agents/lead_agent/agent.py)
|
||||
- ✅ 导入 `TitleMiddleware`
|
||||
- ✅ 注册到 `middleware` 列表：`[SandboxMiddleware(), TitleMiddleware()]`
|
||||
|
||||
### 2. 配置文件
|
||||
|
||||
#### [`config.yaml`](../../config.example.yaml)
|
||||
- ✅ 添加 title 配置段：
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 6
|
||||
max_chars: 60
|
||||
model_name: null
|
||||
```
|
||||
|
||||
### 3. 文档
|
||||
|
||||
#### [`docs/AUTO_TITLE_GENERATION.md`](../docs/AUTO_TITLE_GENERATION.md) (新建)
|
||||
- ✅ 完整的功能说明文档
|
||||
- ✅ 实现方式和架构设计
|
||||
- ✅ 配置说明
|
||||
- ✅ 客户端使用示例（TypeScript）
|
||||
- ✅ 工作流程图（Mermaid）
|
||||
- ✅ 故障排查指南
|
||||
- ✅ State vs Metadata 对比
|
||||
|
||||
#### [`TODO.md`](TODO.md)
|
||||
- ✅ 添加功能完成记录
|
||||
|
||||
### 4. 测试
|
||||
|
||||
#### [`tests/test_title_generation.py`](../tests/test_title_generation.py) (新建)
|
||||
- ✅ 配置类测试
|
||||
- ✅ Middleware 初始化测试
|
||||
- ✅ TODO: 集成测试（需要 mock Runtime）
|
||||
|
||||
---
|
||||
|
||||
## 🎯 核心设计决策
|
||||
|
||||
### 为什么使用 State 而非 Metadata？
|
||||
|
||||
| 方面 | State (✅ 采用) | Metadata (❌ 未采用) |
|
||||
|------|----------------|---------------------|
|
||||
| **持久化** | 自动（通过 checkpointer） | 取决于实现，不可靠 |
|
||||
| **版本控制** | 支持时间旅行 | 不支持 |
|
||||
| **类型安全** | TypedDict 定义 | 任意字典 |
|
||||
| **标准化** | LangGraph 核心机制 | 扩展功能 |
|
||||
|
||||
### 工作流程
|
||||
|
||||
```
|
||||
用户发送首条消息
|
||||
↓
|
||||
Agent 处理并返回回复
|
||||
↓
|
||||
TitleMiddleware.after_agent() 触发
|
||||
↓
|
||||
检查：是否首次对话？是否已有 title？
|
||||
↓
|
||||
调用 LLM 生成 title
|
||||
↓
|
||||
返回 {"title": "..."} 更新 state
|
||||
↓
|
||||
Checkpointer 自动持久化（如果配置了）
|
||||
↓
|
||||
客户端从 state.values.title 读取
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📋 使用指南
|
||||
|
||||
### 后端配置
|
||||
|
||||
1. **启用/禁用功能**
|
||||
```yaml
|
||||
# config.yaml
|
||||
title:
|
||||
enabled: true # 设为 false 禁用
|
||||
```
|
||||
|
||||
2. **自定义配置**
|
||||
```yaml
|
||||
title:
|
||||
enabled: true
|
||||
max_words: 8 # 标题最多 8 个词
|
||||
max_chars: 80 # 标题最多 80 个字符
|
||||
model_name: null # 使用默认模型
|
||||
```
|
||||
|
||||
3. **配置持久化（可选）**
|
||||
|
||||
如果需要在本地开发时持久化 title：
|
||||
|
||||
```python
|
||||
# checkpointer.py
|
||||
from langgraph.checkpoint.sqlite import SqliteSaver
|
||||
|
||||
checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
|
||||
```
|
||||
|
||||
```json
|
||||
// langgraph.json
|
||||
{
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:lead_agent"
|
||||
},
|
||||
"checkpointer": "checkpointer:checkpointer"
|
||||
}
|
||||
```
|
||||
|
||||
### 客户端使用
|
||||
|
||||
```typescript
|
||||
// 获取 thread title
|
||||
const state = await client.threads.getState(threadId);
|
||||
const title = state.values.title || "New Conversation";
|
||||
|
||||
// 显示在对话列表
|
||||
<li>{title}</li>
|
||||
```
|
||||
|
||||
**⚠️ 注意**：Title 在 `state.values.title`，而非 `thread.metadata.title`
|
||||
|
||||
---
|
||||
|
||||
## 🧪 测试
|
||||
|
||||
```bash
|
||||
# 运行测试
|
||||
pytest tests/test_title_generation.py -v
|
||||
|
||||
# 运行所有测试
|
||||
pytest
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔍 故障排查
|
||||
|
||||
### Title 没有生成？
|
||||
|
||||
1. 检查配置：`title.enabled = true`
|
||||
2. 查看日志：搜索 "Generated thread title"
|
||||
3. 确认是首次对话（1 个用户消息 + 1 个助手回复）
|
||||
|
||||
### Title 生成但看不到？
|
||||
|
||||
1. 确认读取位置：`state.values.title`（不是 `thread.metadata.title`）
|
||||
2. 检查 API 响应是否包含 title
|
||||
3. 重新获取 state
|
||||
|
||||
### Title 重启后丢失？
|
||||
|
||||
1. 本地开发需要配置 checkpointer
|
||||
2. LangGraph Platform 会自动持久化
|
||||
3. 检查数据库确认 checkpointer 工作正常
|
||||
|
||||
---
|
||||
|
||||
## 📊 性能影响
|
||||
|
||||
- **延迟增加**：约 0.5-1 秒（LLM 调用）
|
||||
- **并发安全**：在 `after_agent` 中运行，不阻塞主流程
|
||||
- **资源消耗**：每个 thread 只生成一次
|
||||
|
||||
### 优化建议
|
||||
|
||||
1. 使用更快的模型（如 `gpt-3.5-turbo`）
|
||||
2. 减少 `max_words` 和 `max_chars`
|
||||
3. 调整 prompt 使其更简洁
|
||||
|
||||
---
|
||||
|
||||
## 🚀 下一步
|
||||
|
||||
- [ ] 添加集成测试（需要 mock LangGraph Runtime）
|
||||
- [ ] 支持自定义 prompt template
|
||||
- [ ] 支持多语言 title 生成
|
||||
- [ ] 添加 title 重新生成功能
|
||||
- [ ] 监控 title 生成成功率和延迟
|
||||
|
||||
---
|
||||
|
||||
## 📚 相关资源
|
||||
|
||||
- [完整文档](../docs/AUTO_TITLE_GENERATION.md)
|
||||
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)
|
||||
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
|
||||
- [LangGraph Checkpointer](https://langchain-ai.github.io/langgraph/concepts/persistence/)
|
||||
|
||||
---
|
||||
|
||||
*实现完成时间: 2026-01-14*
|
||||
34
deer-flow/backend/docs/TODO.md
Normal file
34
deer-flow/backend/docs/TODO.md
Normal file
@@ -0,0 +1,34 @@
|
||||
# TODO List
|
||||
|
||||
## Completed Features
|
||||
|
||||
- [x] Launch the sandbox only after the first file system or bash tool is called
|
||||
- [x] Add Clarification Process for the whole process
|
||||
- [x] Implement Context Summarization Mechanism to avoid context explosion
|
||||
- [x] Integrate MCP (Model Context Protocol) for extensible tools
|
||||
- [x] Add file upload support with automatic document conversion
|
||||
- [x] Implement automatic thread title generation
|
||||
- [x] Add Plan Mode with TodoList middleware
|
||||
- [x] Add vision model support with ViewImageMiddleware
|
||||
- [x] Skills system with SKILL.md format
|
||||
|
||||
## Planned Features
|
||||
|
||||
- [ ] Pooling the sandbox resources to reduce the number of sandbox containers
|
||||
- [ ] Add authentication/authorization layer
|
||||
- [ ] Implement rate limiting
|
||||
- [ ] Add metrics and monitoring
|
||||
- [ ] Support for more document formats in upload
|
||||
- [ ] Skill marketplace / remote skill installation
|
||||
- [ ] Optimize async concurrency in agent hot path (IM channels multi-task scenario)
|
||||
- Replace `time.sleep(5)` with `asyncio.sleep()` in `packages/harness/deerflow/tools/builtins/task_tool.py` (subagent polling)
|
||||
- Replace `subprocess.run()` with `asyncio.create_subprocess_shell()` in `packages/harness/deerflow/sandbox/local/local_sandbox.py`
|
||||
- Replace sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
|
||||
- Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
|
||||
- Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
|
||||
- For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
|
||||
|
||||
## Resolved Issues
|
||||
|
||||
- [x] Make sure that no duplicated files in `state.artifacts`
|
||||
- [x] Long thinking but with empty content (answer inside thinking process)
|
||||
114
deer-flow/backend/docs/memory-settings-sample.json
Normal file
114
deer-flow/backend/docs/memory-settings-sample.json
Normal file
@@ -0,0 +1,114 @@
|
||||
{
|
||||
"version": "1.0",
|
||||
"lastUpdated": "2026-03-28T10:30:00Z",
|
||||
"user": {
|
||||
"workContext": {
|
||||
"summary": "Working on DeerFlow memory management UX, including local search, local filters, clear-all, and single-fact deletion in Settings > Memory.",
|
||||
"updatedAt": "2026-03-28T10:30:00Z"
|
||||
},
|
||||
"personalContext": {
|
||||
"summary": "Prefers Chinese during collaboration, but wants GitHub PR titles and bodies written in English with a Chinese translation provided alongside them.",
|
||||
"updatedAt": "2026-03-28T10:28:00Z"
|
||||
},
|
||||
"topOfMind": {
|
||||
"summary": "Wants reviewers to be able to reproduce the memory search and filter flow quickly with pre-populated sample data.",
|
||||
"updatedAt": "2026-03-28T10:26:00Z"
|
||||
}
|
||||
},
|
||||
"history": {
|
||||
"recentMonths": {
|
||||
"summary": "Recently contributed multiple DeerFlow pull requests covering memory, uploads, and compatibility fixes.",
|
||||
"updatedAt": "2026-03-28T10:24:00Z"
|
||||
},
|
||||
"earlierContext": {
|
||||
"summary": "Often prefers shipping smaller, reviewable changes with explicit validation notes.",
|
||||
"updatedAt": "2026-03-28T10:22:00Z"
|
||||
},
|
||||
"longTermBackground": {
|
||||
"summary": "Actively building open-source contribution experience and improving end-to-end delivery quality.",
|
||||
"updatedAt": "2026-03-28T10:20:00Z"
|
||||
}
|
||||
},
|
||||
"facts": [
|
||||
{
|
||||
"id": "fact_review_001",
|
||||
"content": "User prefers Chinese for day-to-day collaboration.",
|
||||
"category": "preference",
|
||||
"confidence": 0.95,
|
||||
"createdAt": "2026-03-28T09:50:00Z",
|
||||
"source": "thread_pref_cn"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_002",
|
||||
"content": "PR titles and bodies should be drafted in English and accompanied by a Chinese translation.",
|
||||
"category": "workflow",
|
||||
"confidence": 0.93,
|
||||
"createdAt": "2026-03-28T09:52:00Z",
|
||||
"source": "thread_pr_style"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_003",
|
||||
"content": "User implemented memory search and filter improvements in the DeerFlow settings page.",
|
||||
"category": "project",
|
||||
"confidence": 0.91,
|
||||
"createdAt": "2026-03-28T09:54:00Z",
|
||||
"source": "thread_memory_filters"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_004",
|
||||
"content": "User added clear-all memory support through the gateway memory API.",
|
||||
"category": "project",
|
||||
"confidence": 0.89,
|
||||
"createdAt": "2026-03-28T09:56:00Z",
|
||||
"source": "thread_memory_clear"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_005",
|
||||
"content": "User added single-fact deletion support for persisted memory entries.",
|
||||
"category": "project",
|
||||
"confidence": 0.9,
|
||||
"createdAt": "2026-03-28T09:58:00Z",
|
||||
"source": "thread_memory_delete"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_006",
|
||||
"content": "Reviewer can search for keyword memory to see multiple matching facts.",
|
||||
"category": "testing",
|
||||
"confidence": 0.84,
|
||||
"createdAt": "2026-03-28T10:00:00Z",
|
||||
"source": "thread_review_demo"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_007",
|
||||
"content": "Reviewer can search for keyword Chinese to verify cross-category matching.",
|
||||
"category": "testing",
|
||||
"confidence": 0.82,
|
||||
"createdAt": "2026-03-28T10:02:00Z",
|
||||
"source": "thread_review_demo"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_008",
|
||||
"content": "Reviewer can search for workflow to verify category text is included in local filtering.",
|
||||
"category": "testing",
|
||||
"confidence": 0.81,
|
||||
"createdAt": "2026-03-28T10:04:00Z",
|
||||
"source": "thread_review_demo"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_009",
|
||||
"content": "Delete fact testing can target this disposable sample entry.",
|
||||
"category": "testing",
|
||||
"confidence": 0.78,
|
||||
"createdAt": "2026-03-28T10:06:00Z",
|
||||
"source": "thread_delete_demo"
|
||||
},
|
||||
{
|
||||
"id": "fact_review_010",
|
||||
"content": "This sample fact is intended for edit testing.",
|
||||
"category": "testing",
|
||||
"confidence": 0.8,
|
||||
"createdAt": "2026-03-28T10:08:00Z",
|
||||
"source": "manual"
|
||||
}
|
||||
]
|
||||
}
|
||||
291
deer-flow/backend/docs/middleware-execution-flow.md
Normal file
291
deer-flow/backend/docs/middleware-execution-flow.md
Normal file
@@ -0,0 +1,291 @@
|
||||
# Middleware 执行流程
|
||||
|
||||
## Middleware 列表
|
||||
|
||||
`create_deerflow_agent` 通过 `RuntimeFeatures` 组装的完整 middleware 链（默认全开时）：
|
||||
|
||||
| # | Middleware | `before_agent` | `before_model` | `after_model` | `after_agent` | `wrap_tool_call` | 主 Agent | Subagent | 来源 |
|
||||
|---|-----------|:-:|:-:|:-:|:-:|:-:|:-:|:-:|------|
|
||||
| 0 | ThreadDataMiddleware | ✓ | | | | | ✓ | ✓ | `sandbox` |
|
||||
| 1 | UploadsMiddleware | ✓ | | | | | ✓ | ✗ | `sandbox` |
|
||||
| 2 | SandboxMiddleware | ✓ | | | ✓ | | ✓ | ✓ | `sandbox` |
|
||||
| 3 | DanglingToolCallMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
|
||||
| 4 | GuardrailMiddleware | | | | | ✓ | ✓ | ✓ | *Phase 2 纳入* |
|
||||
| 5 | ToolErrorHandlingMiddleware | | | | | ✓ | ✓ | ✓ | 始终开启 |
|
||||
| 6 | SummarizationMiddleware | | | ✓ | | | ✓ | ✗ | `summarization` |
|
||||
| 7 | TodoMiddleware | | | ✓ | | | ✓ | ✗ | `plan_mode` 参数 |
|
||||
| 8 | TitleMiddleware | | | ✓ | | | ✓ | ✗ | `auto_title` |
|
||||
| 9 | MemoryMiddleware | | | | ✓ | | ✓ | ✗ | `memory` |
|
||||
| 10 | ViewImageMiddleware | | ✓ | | | | ✓ | ✗ | `vision` |
|
||||
| 11 | SubagentLimitMiddleware | | | ✓ | | | ✓ | ✗ | `subagent` |
|
||||
| 12 | LoopDetectionMiddleware | | | ✓ | | | ✓ | ✗ | 始终开启 |
|
||||
| 13 | ClarificationMiddleware | | | ✓ | | | ✓ | ✗ | 始终最后 |
|
||||
|
||||
主 agent **14 个** middleware（`make_lead_agent`），subagent **4 个**（ThreadData、Sandbox、Guardrail、ToolErrorHandling）。`create_deerflow_agent` Phase 1 实现 **13 个**（Guardrail 仅支持自定义实例，无内置默认）。
|
||||
|
||||
## 执行流程
|
||||
|
||||
LangChain `create_agent` 的规则：
|
||||
- **`before_*` 正序执行**（列表位置 0 → N）
|
||||
- **`after_*` 反序执行**（列表位置 N → 0）
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
START(["invoke"]) --> TD
|
||||
|
||||
subgraph BA ["<b>before_agent</b> 正序 0→N"]
|
||||
direction TB
|
||||
TD["[0] ThreadData<br/>创建线程目录"] --> UL["[1] Uploads<br/>扫描上传文件"] --> SB["[2] Sandbox<br/>获取沙箱"]
|
||||
end
|
||||
|
||||
subgraph BM ["<b>before_model</b> 正序 0→N"]
|
||||
direction TB
|
||||
VI["[10] ViewImage<br/>注入图片 base64"]
|
||||
end
|
||||
|
||||
SB --> VI
|
||||
VI --> M["<b>MODEL</b>"]
|
||||
|
||||
subgraph AM ["<b>after_model</b> 反序 N→0"]
|
||||
direction TB
|
||||
CL["[13] Clarification<br/>拦截 ask_clarification"] --> LD["[12] LoopDetection<br/>检测循环"] --> SL["[11] SubagentLimit<br/>截断多余 task"] --> TI["[8] Title<br/>生成标题"] --> SM["[6] Summarization<br/>上下文压缩"] --> DTC["[3] DanglingToolCall<br/>补缺失 ToolMessage"]
|
||||
end
|
||||
|
||||
M --> CL
|
||||
|
||||
subgraph AA ["<b>after_agent</b> 反序 N→0"]
|
||||
direction TB
|
||||
SBR["[2] Sandbox<br/>释放沙箱"] --> MEM["[9] Memory<br/>入队记忆"]
|
||||
end
|
||||
|
||||
DTC --> SBR
|
||||
MEM --> END(["response"])
|
||||
|
||||
classDef beforeNode fill:#a0a8b5,stroke:#636b7a,color:#2d3239
|
||||
classDef modelNode fill:#b5a8a0,stroke:#7a6b63,color:#2d3239
|
||||
classDef afterModelNode fill:#b5a0a8,stroke:#7a636b,color:#2d3239
|
||||
classDef afterAgentNode fill:#a0b5a8,stroke:#637a6b,color:#2d3239
|
||||
classDef terminalNode fill:#a8b5a0,stroke:#6b7a63,color:#2d3239
|
||||
|
||||
class TD,UL,SB,VI beforeNode
|
||||
class M modelNode
|
||||
class CL,LD,SL,TI,SM,DTC afterModelNode
|
||||
class SBR,MEM afterAgentNode
|
||||
class START,END terminalNode
|
||||
```
|
||||
|
||||
## 时序图
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant U as User
|
||||
participant TD as ThreadDataMiddleware
|
||||
participant UL as UploadsMiddleware
|
||||
participant SB as SandboxMiddleware
|
||||
participant VI as ViewImageMiddleware
|
||||
participant M as MODEL
|
||||
participant CL as ClarificationMiddleware
|
||||
participant SL as SubagentLimitMiddleware
|
||||
participant TI as TitleMiddleware
|
||||
participant SM as SummarizationMiddleware
|
||||
participant DTC as DanglingToolCallMiddleware
|
||||
participant MEM as MemoryMiddleware
|
||||
|
||||
U ->> TD: invoke
|
||||
activate TD
|
||||
Note right of TD: before_agent 创建目录
|
||||
|
||||
TD ->> UL: before_agent
|
||||
activate UL
|
||||
Note right of UL: before_agent 扫描上传文件
|
||||
|
||||
UL ->> SB: before_agent
|
||||
activate SB
|
||||
Note right of SB: before_agent 获取沙箱
|
||||
|
||||
SB ->> VI: before_model
|
||||
activate VI
|
||||
Note right of VI: before_model 注入图片 base64
|
||||
|
||||
VI ->> M: messages + tools
|
||||
activate M
|
||||
M -->> CL: AI response
|
||||
deactivate M
|
||||
|
||||
activate CL
|
||||
Note right of CL: after_model 拦截 ask_clarification
|
||||
CL -->> SL: after_model
|
||||
deactivate CL
|
||||
|
||||
activate SL
|
||||
Note right of SL: after_model 截断多余 task
|
||||
SL -->> TI: after_model
|
||||
deactivate SL
|
||||
|
||||
activate TI
|
||||
Note right of TI: after_model 生成标题
|
||||
TI -->> SM: after_model
|
||||
deactivate TI
|
||||
|
||||
activate SM
|
||||
Note right of SM: after_model 上下文压缩
|
||||
SM -->> DTC: after_model
|
||||
deactivate SM
|
||||
|
||||
activate DTC
|
||||
Note right of DTC: after_model 补缺失 ToolMessage
|
||||
DTC -->> VI: done
|
||||
deactivate DTC
|
||||
|
||||
VI -->> SB: done
|
||||
deactivate VI
|
||||
|
||||
Note right of SB: after_agent 释放沙箱
|
||||
SB -->> UL: done
|
||||
deactivate SB
|
||||
|
||||
UL -->> TD: done
|
||||
deactivate UL
|
||||
|
||||
Note right of MEM: after_agent 入队记忆
|
||||
|
||||
TD -->> U: response
|
||||
deactivate TD
|
||||
```
|
||||
|
||||
## 洋葱模型
|
||||
|
||||
列表位置决定在洋葱中的层级 — 位置 0 最外层，位置 N 最内层：
|
||||
|
||||
```
|
||||
进入 before_*： [0] → [1] → [2] → ... → [10] → MODEL
|
||||
退出 after_*： MODEL → [13] → [11] → ... → [6] → [3] → [2] → [0]
|
||||
↑ 最内层最先执行
|
||||
```
|
||||
|
||||
> [!important] 核心规则
|
||||
> 列表最后的 middleware，其 `after_model` **最先执行**。
|
||||
> ClarificationMiddleware 在列表末尾，所以它第一个拦截 model 输出。
|
||||
|
||||
## 对比：真正的洋葱 vs DeerFlow 的实际情况
|
||||
|
||||
### 真正的洋葱（如 Koa/Express）
|
||||
|
||||
每个 middleware 同时负责 before 和 after，形成对称嵌套：
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant U as User
|
||||
participant A as AuthMiddleware
|
||||
participant L as LogMiddleware
|
||||
participant R as RateLimitMiddleware
|
||||
participant H as Handler
|
||||
|
||||
U ->> A: request
|
||||
activate A
|
||||
Note right of A: before: 校验 token
|
||||
|
||||
A ->> L: next()
|
||||
activate L
|
||||
Note right of L: before: 记录请求时间
|
||||
|
||||
L ->> R: next()
|
||||
activate R
|
||||
Note right of R: before: 检查频率
|
||||
|
||||
R ->> H: next()
|
||||
activate H
|
||||
H -->> R: result
|
||||
deactivate H
|
||||
|
||||
Note right of R: after: 更新计数器
|
||||
R -->> L: result
|
||||
deactivate R
|
||||
|
||||
Note right of L: after: 记录耗时
|
||||
L -->> A: result
|
||||
deactivate L
|
||||
|
||||
Note right of A: after: 清理上下文
|
||||
A -->> U: response
|
||||
deactivate A
|
||||
```
|
||||
|
||||
> [!tip] 洋葱特征
|
||||
> 每个 middleware 都有 before/after 对称操作，`activate` 跨越整个内层执行，形成完美嵌套。
|
||||
|
||||
### DeerFlow 的实际情况
|
||||
|
||||
不是洋葱，是管道。大部分 middleware 只用一个钩子，不存在对称嵌套。多轮对话时 before_model / after_model 循环执行：
|
||||
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant U as User
|
||||
participant TD as ThreadData
|
||||
participant UL as Uploads
|
||||
participant SB as Sandbox
|
||||
participant VI as ViewImage
|
||||
participant M as MODEL
|
||||
participant CL as Clarification
|
||||
participant SL as SubagentLimit
|
||||
participant TI as Title
|
||||
participant SM as Summarization
|
||||
participant MEM as Memory
|
||||
|
||||
U ->> TD: invoke
|
||||
Note right of TD: before_agent 创建目录
|
||||
TD ->> UL: .
|
||||
Note right of UL: before_agent 扫描文件
|
||||
UL ->> SB: .
|
||||
Note right of SB: before_agent 获取沙箱
|
||||
|
||||
loop 每轮对话（tool call 循环）
|
||||
SB ->> VI: .
|
||||
Note right of VI: before_model 注入图片
|
||||
VI ->> M: messages + tools
|
||||
M -->> CL: AI response
|
||||
Note right of CL: after_model 拦截 ask_clarification
|
||||
CL -->> SL: .
|
||||
Note right of SL: after_model 截断多余 task
|
||||
SL -->> TI: .
|
||||
Note right of TI: after_model 生成标题
|
||||
TI -->> SM: .
|
||||
Note right of SM: after_model 上下文压缩
|
||||
end
|
||||
|
||||
Note right of SB: after_agent 释放沙箱
|
||||
SB -->> MEM: .
|
||||
Note right of MEM: after_agent 入队记忆
|
||||
MEM -->> U: response
|
||||
```
|
||||
|
||||
> [!warning] 不是洋葱
|
||||
> 14 个 middleware 中只有 SandboxMiddleware 有 before/after 对称（获取/释放）。其余都是单向的：要么只在 `before_*` 做事，要么只在 `after_*` 做事。`before_agent` / `after_agent` 只跑一次，`before_model` / `after_model` 每轮循环都跑。
|
||||
|
||||
硬依赖只有 2 处：
|
||||
|
||||
1. **ThreadData 在 Sandbox 之前** — sandbox 需要线程目录
|
||||
2. **Clarification 在列表最后** — `after_model` 反序时最先执行，第一个拦截 `ask_clarification`
|
||||
|
||||
### 结论
|
||||
|
||||
| | 真正的洋葱 | DeerFlow 实际 |
|
||||
|---|---|---|
|
||||
| 每个 middleware | before + after 对称 | 大多只用一个钩子 |
|
||||
| 激活条 | 嵌套（外长内短） | 不嵌套（串行） |
|
||||
| 反序的意义 | 清理与初始化配对 | 仅影响 after_model 的执行优先级 |
|
||||
| 典型例子 | Auth: 校验 token / 清理上下文 | ThreadData: 只创建目录，没有清理 |
|
||||
|
||||
## 关键设计点
|
||||
|
||||
### ClarificationMiddleware 为什么在列表最后？
|
||||
|
||||
位置最后 = `after_model` 最先执行。它需要**第一个**看到 model 输出，检查是否有 `ask_clarification` tool call。如果有，立即中断（`Command(goto=END)`），后续 middleware 的 `after_model` 不再执行。
|
||||
|
||||
### SandboxMiddleware 的对称性
|
||||
|
||||
`before_agent`（正序第 3 个）获取沙箱，`after_agent`（反序第 1 个）释放沙箱。外层进入 → 外层退出，天然的洋葱对称。
|
||||
|
||||
### 大部分 middleware 只用一个钩子
|
||||
|
||||
14 个 middleware 中，只有 SandboxMiddleware 同时用了 `before_agent` + `after_agent`（获取/释放）。其余都只在一个阶段执行。洋葱模型的反序特性主要影响 `after_model` 阶段的执行顺序。
|
||||
204
deer-flow/backend/docs/plan_mode_usage.md
Normal file
204
deer-flow/backend/docs/plan_mode_usage.md
Normal file
@@ -0,0 +1,204 @@
|
||||
# Plan Mode with TodoList Middleware
|
||||
|
||||
This document describes how to enable and use the Plan Mode feature with TodoList middleware in DeerFlow 2.0.
|
||||
|
||||
## Overview
|
||||
|
||||
Plan Mode adds a TodoList middleware to the agent, which provides a `write_todos` tool that helps the agent:
|
||||
- Break down complex tasks into smaller, manageable steps
|
||||
- Track progress as work progresses
|
||||
- Provide visibility to users about what's being done
|
||||
|
||||
The TodoList middleware is built on LangChain's `TodoListMiddleware`.
|
||||
|
||||
## Configuration
|
||||
|
||||
### Enabling Plan Mode
|
||||
|
||||
Plan mode is controlled via **runtime configuration** through the `is_plan_mode` parameter in the `configurable` section of `RunnableConfig`. This allows you to dynamically enable or disable plan mode on a per-request basis.
|
||||
|
||||
```python
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
|
||||
# Enable plan mode via runtime configuration
|
||||
config = RunnableConfig(
|
||||
configurable={
|
||||
"thread_id": "example-thread",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": True, # Enable plan mode
|
||||
}
|
||||
)
|
||||
|
||||
# Create agent with plan mode enabled
|
||||
agent = make_lead_agent(config)
|
||||
```
|
||||
|
||||
### Configuration Options
|
||||
|
||||
- **is_plan_mode** (bool): Whether to enable plan mode with TodoList middleware. Default: `False`
|
||||
- Pass via `config.get("configurable", {}).get("is_plan_mode", False)`
|
||||
- Can be set dynamically for each agent invocation
|
||||
- No global configuration needed
|
||||
|
||||
## Default Behavior
|
||||
|
||||
When plan mode is enabled with default settings, the agent will have access to a `write_todos` tool with the following behavior:
|
||||
|
||||
### When to Use TodoList
|
||||
|
||||
The agent will use the todo list for:
|
||||
1. Complex multi-step tasks (3+ distinct steps)
|
||||
2. Non-trivial tasks requiring careful planning
|
||||
3. When user explicitly requests a todo list
|
||||
4. When user provides multiple tasks
|
||||
|
||||
### When NOT to Use TodoList
|
||||
|
||||
The agent will skip using the todo list for:
|
||||
1. Single, straightforward tasks
|
||||
2. Trivial tasks (< 3 steps)
|
||||
3. Purely conversational or informational requests
|
||||
|
||||
### Task States
|
||||
|
||||
- **pending**: Task not yet started
|
||||
- **in_progress**: Currently working on (can have multiple parallel tasks)
|
||||
- **completed**: Task finished successfully
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
|
||||
# Create agent with plan mode ENABLED
|
||||
config_with_plan_mode = RunnableConfig(
|
||||
configurable={
|
||||
"thread_id": "example-thread",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": True, # TodoList middleware will be added
|
||||
}
|
||||
)
|
||||
agent_with_todos = make_lead_agent(config_with_plan_mode)
|
||||
|
||||
# Create agent with plan mode DISABLED (default)
|
||||
config_without_plan_mode = RunnableConfig(
|
||||
configurable={
|
||||
"thread_id": "another-thread",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": False, # No TodoList middleware
|
||||
}
|
||||
)
|
||||
agent_without_todos = make_lead_agent(config_without_plan_mode)
|
||||
```
|
||||
|
||||
### Dynamic Plan Mode per Request
|
||||
|
||||
You can enable/disable plan mode dynamically for different conversations or tasks:
|
||||
|
||||
```python
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
from deerflow.agents.lead_agent.agent import make_lead_agent
|
||||
|
||||
def create_agent_for_task(task_complexity: str):
|
||||
"""Create agent with plan mode based on task complexity."""
|
||||
is_complex = task_complexity in ["high", "very_high"]
|
||||
|
||||
config = RunnableConfig(
|
||||
configurable={
|
||||
"thread_id": f"task-{task_complexity}",
|
||||
"thinking_enabled": True,
|
||||
"is_plan_mode": is_complex, # Enable only for complex tasks
|
||||
}
|
||||
)
|
||||
|
||||
return make_lead_agent(config)
|
||||
|
||||
# Simple task - no TodoList needed
|
||||
simple_agent = create_agent_for_task("low")
|
||||
|
||||
# Complex task - TodoList enabled for better tracking
|
||||
complex_agent = create_agent_for_task("high")
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
1. When `make_lead_agent(config)` is called, it extracts `is_plan_mode` from `config.configurable`
|
||||
2. The config is passed to `_build_middlewares(config)`
|
||||
3. `_build_middlewares()` reads `is_plan_mode` and calls `_create_todo_list_middleware(is_plan_mode)`
|
||||
4. If `is_plan_mode=True`, a `TodoListMiddleware` instance is created and added to the middleware chain
|
||||
5. The middleware automatically adds a `write_todos` tool to the agent's toolset
|
||||
6. The agent can use this tool to manage tasks during execution
|
||||
7. The middleware handles the todo list state and provides it to the agent
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
make_lead_agent(config)
|
||||
│
|
||||
├─> Extracts: is_plan_mode = config.configurable.get("is_plan_mode", False)
|
||||
│
|
||||
└─> _build_middlewares(config)
|
||||
│
|
||||
├─> ThreadDataMiddleware
|
||||
├─> SandboxMiddleware
|
||||
├─> SummarizationMiddleware (if enabled via global config)
|
||||
├─> TodoListMiddleware (if is_plan_mode=True) ← NEW
|
||||
├─> TitleMiddleware
|
||||
└─> ClarificationMiddleware
|
||||
```
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Agent Module
|
||||
- **Location**: `packages/harness/deerflow/agents/lead_agent/agent.py`
|
||||
- **Function**: `_create_todo_list_middleware(is_plan_mode: bool)` - Creates TodoListMiddleware if plan mode is enabled
|
||||
- **Function**: `_build_middlewares(config: RunnableConfig)` - Builds middleware chain based on runtime config
|
||||
- **Function**: `make_lead_agent(config: RunnableConfig)` - Creates agent with appropriate middlewares
|
||||
|
||||
### Runtime Configuration
|
||||
Plan mode is controlled via the `is_plan_mode` parameter in `RunnableConfig.configurable`:
|
||||
```python
|
||||
config = RunnableConfig(
|
||||
configurable={
|
||||
"is_plan_mode": True, # Enable plan mode
|
||||
# ... other configurable options
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
## Key Benefits
|
||||
|
||||
1. **Dynamic Control**: Enable/disable plan mode per request without global state
|
||||
2. **Flexibility**: Different conversations can have different plan mode settings
|
||||
3. **Simplicity**: No need for global configuration management
|
||||
4. **Context-Aware**: Plan mode decision can be based on task complexity, user preferences, etc.
|
||||
|
||||
## Custom Prompts
|
||||
|
||||
DeerFlow uses custom `system_prompt` and `tool_description` for the TodoListMiddleware that match the overall DeerFlow prompt style:
|
||||
|
||||
### System Prompt Features
|
||||
- Uses XML tags (`<todo_list_system>`) for structure consistency with DeerFlow's main prompt
|
||||
- Emphasizes CRITICAL rules and best practices
|
||||
- Clear "When to Use" vs "When NOT to Use" guidelines
|
||||
- Focuses on real-time updates and immediate task completion
|
||||
|
||||
### Tool Description Features
|
||||
- Detailed usage scenarios with examples
|
||||
- Strong emphasis on NOT using for simple tasks
|
||||
- Clear task state definitions (pending, in_progress, completed)
|
||||
- Comprehensive best practices section
|
||||
- Task completion requirements to prevent premature marking
|
||||
|
||||
The custom prompts are defined in `_create_todo_list_middleware()` in `/Users/hetao/workspace/deer-flow/backend/packages/harness/deerflow/agents/lead_agent/agent.py:57`.
|
||||
|
||||
## Notes
|
||||
|
||||
- TodoList middleware uses LangChain's built-in `TodoListMiddleware` with **custom DeerFlow-style prompts**
|
||||
- Plan mode is **disabled by default** (`is_plan_mode=False`) to maintain backward compatibility
|
||||
- The middleware is positioned before `ClarificationMiddleware` to allow todo management during clarification flows
|
||||
- Custom prompts emphasize the same principles as DeerFlow's main system prompt (clarity, action-oriented, critical rules)
|
||||
503
deer-flow/backend/docs/rfc-create-deerflow-agent.md
Normal file
503
deer-flow/backend/docs/rfc-create-deerflow-agent.md
Normal file
@@ -0,0 +1,503 @@
|
||||
# RFC: `create_deerflow_agent` — 纯参数的 SDK 工厂 API
|
||||
|
||||
## 1. 问题
|
||||
|
||||
当前 harness 的唯一公开入口是 `make_lead_agent(config: RunnableConfig)`。它内部：
|
||||
|
||||
```
|
||||
make_lead_agent
|
||||
├─ get_app_config() ← 读 config.yaml
|
||||
├─ _resolve_model_name() ← 读 config.yaml
|
||||
├─ load_agent_config() ← 读 agents/{name}/config.yaml
|
||||
├─ create_chat_model(name) ← 读 config.yaml（反射加载 model class）
|
||||
├─ get_available_tools() ← 读 config.yaml + extensions_config.json
|
||||
├─ apply_prompt_template() ← 读 skills 目录 + memory.json
|
||||
└─ _build_middlewares() ← 读 config.yaml（summarization、model vision）
|
||||
```
|
||||
|
||||
**6 处隐式 I/O** — 全部依赖文件系统。如果你想把 `deerflow-harness` 当 Python 库嵌入自己的应用，你必须准备 `config.yaml` + `extensions_config.json` + skills 目录。这对 SDK 用户是不可接受的。
|
||||
|
||||
### 对比
|
||||
|
||||
| | `langchain.create_agent` | `make_lead_agent` | `DeerFlowClient`（增强后） |
|
||||
|---|---|---|---|
|
||||
| 定位 | 底层原语 | 内部工厂 | **唯一公开 API** |
|
||||
| 配置来源 | 纯参数 | YAML 文件 | **参数优先，config fallback** |
|
||||
| 内置能力 | 无 | Sandbox/Memory/Skills/Subagent/... | **按需组合 + 管理 API** |
|
||||
| 用户接口 | `graph.invoke(state)` | 内部使用 | **`client.chat("hello")`** |
|
||||
| 适合谁 | 写 LangChain 的人 | 内部使用 | **所有 DeerFlow 用户** |
|
||||
|
||||
## 2. 设计原则
|
||||
|
||||
### Python 中的 DI 最佳实践
|
||||
|
||||
1. **函数参数即注入** — 不读全局状态，所有依赖通过参数传入
|
||||
2. **Protocol 定义契约** — 不依赖具体类，依赖行为接口
|
||||
3. **合理默认值** — `sandbox=True` 等价于 `sandbox=LocalSandboxProvider()`
|
||||
4. **分层 API** — 简单用法一行搞定，复杂用法有逃生舱
|
||||
|
||||
### 分层架构
|
||||
|
||||
```
|
||||
┌──────────────────────┐
|
||||
│ DeerFlowClient │ ← 唯一公开 API（chat/stream + 管理）
|
||||
└──────────┬───────────┘
|
||||
┌──────────▼───────────┐
|
||||
│ make_lead_agent │ ← 内部：配置驱动工厂
|
||||
└──────────┬───────────┘
|
||||
┌──────────▼───────────┐
|
||||
│ create_deerflow_agent │ ← 内部：纯参数工厂
|
||||
└──────────┬───────────┘
|
||||
┌──────────▼───────────┐
|
||||
│ langchain.create_agent│ ← 底层原语
|
||||
└──────────────────────┘
|
||||
```
|
||||
|
||||
`DeerFlowClient` 是唯一公开 API。`create_deerflow_agent` 和 `make_lead_agent` 都是内部实现。
|
||||
|
||||
用户通过 `DeerFlowClient` 三个参数控制行为：
|
||||
|
||||
| 参数 | 类型 | 职责 |
|
||||
|------|------|------|
|
||||
| `config` | `dict` | 覆盖 config.yaml 的任意配置项 |
|
||||
| `features` | `RuntimeFeatures` | 替换内置 middleware 实现 |
|
||||
| `extra_middleware` | `list[AgentMiddleware]` | 新增用户 middleware |
|
||||
|
||||
不传参数 → 读 config.yaml（现有行为，完全兼容）。
|
||||
|
||||
### 核心约束
|
||||
|
||||
- **配置覆盖** — `config` dict > config.yaml > 默认值
|
||||
- **三层不重叠** — config 传参数，features 传实例，extra_middleware 传新增
|
||||
- **向前兼容** — 现有 `DeerFlowClient()` 无参构造行为不变
|
||||
- **harness 边界合规** — 不 import `app.*`（`test_harness_boundary.py` 强制）
|
||||
|
||||
## 3. API 设计
|
||||
|
||||
### 3.1 `DeerFlowClient` — 唯一公开 API
|
||||
|
||||
在现有构造函数上增加三个可选参数：
|
||||
|
||||
```python
|
||||
from deerflow.client import DeerFlowClient
|
||||
from deerflow.agents.features import RuntimeFeatures
|
||||
|
||||
client = DeerFlowClient(
|
||||
# 1. config — 覆盖 config.yaml 的任意 key（结构和 yaml 一致）
|
||||
config={
|
||||
"models": [{"name": "gpt-4o", "use": "langchain_openai:ChatOpenAI", "model": "gpt-4o", "api_key": "sk-..."}],
|
||||
"memory": {"max_facts": 50, "enabled": True},
|
||||
"title": {"enabled": False},
|
||||
"summarization": {"enabled": True, "trigger": [{"type": "tokens", "value": 10000}]},
|
||||
"sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"},
|
||||
},
|
||||
|
||||
# 2. features — 替换内置 middleware 实现
|
||||
features=RuntimeFeatures(
|
||||
memory=MyMemoryMiddleware(),
|
||||
auto_title=MyTitleMiddleware(),
|
||||
),
|
||||
|
||||
# 3. extra_middleware — 新增用户 middleware
|
||||
extra_middleware=[
|
||||
MyAuditMiddleware(), # @Next(SandboxMiddleware)
|
||||
MyFilterMiddleware(), # @Prev(ClarificationMiddleware)
|
||||
],
|
||||
)
|
||||
```
|
||||
|
||||
三种典型用法：
|
||||
|
||||
```python
|
||||
# 用法 1：全读 config.yaml（现有行为，不变）
|
||||
client = DeerFlowClient()
|
||||
|
||||
# 用法 2：只改参数，不换实现
|
||||
client = DeerFlowClient(config={"memory": {"max_facts": 50}})
|
||||
|
||||
# 用法 3：替换 middleware 实现
|
||||
client = DeerFlowClient(features=RuntimeFeatures(auto_title=MyTitleMiddleware()))
|
||||
|
||||
# 用法 4：添加自定义 middleware
|
||||
client = DeerFlowClient(extra_middleware=[MyAuditMiddleware()])
|
||||
|
||||
# 用法 5：纯 SDK（无 config.yaml）
|
||||
client = DeerFlowClient(config={
|
||||
"models": [{"name": "gpt-4o", "use": "langchain_openai:ChatOpenAI", ...}],
|
||||
"tools": [{"name": "bash", "use": "deerflow.sandbox.tools:bash_tool", "group": "bash"}],
|
||||
"memory": {"enabled": True},
|
||||
})
|
||||
```
|
||||
|
||||
内部实现：`final_config = deep_merge(file_config, code_config)`
|
||||
|
||||
### 3.2 `create_deerflow_agent` — 内部工厂（不公开）
|
||||
|
||||
```python
|
||||
def create_deerflow_agent(
|
||||
model: BaseChatModel,
|
||||
tools: list[BaseTool] | None = None,
|
||||
*,
|
||||
system_prompt: str | None = None,
|
||||
middleware: list[AgentMiddleware] | None = None,
|
||||
features: RuntimeFeatures | None = None,
|
||||
state_schema: type | None = None,
|
||||
checkpointer: BaseCheckpointSaver | None = None,
|
||||
name: str = "default",
|
||||
) -> CompiledStateGraph:
|
||||
...
|
||||
```
|
||||
|
||||
`DeerFlowClient` 内部调用此函数。
|
||||
|
||||
### 3.3 `RuntimeFeatures` — 内置 Middleware 替换
|
||||
|
||||
只做一件事：用自定义实例替换内置 middleware。不管配置参数（参数走 `config` dict）。
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class RuntimeFeatures:
|
||||
sandbox: bool | AgentMiddleware = True
|
||||
memory: bool | AgentMiddleware = False
|
||||
summarization: bool | AgentMiddleware = False
|
||||
subagent: bool | AgentMiddleware = False
|
||||
vision: bool | AgentMiddleware = False
|
||||
auto_title: bool | AgentMiddleware = False
|
||||
```
|
||||
|
||||
| 值 | 含义 |
|
||||
|---|---|
|
||||
| `True` | 使用默认 middleware（参数从 config 读） |
|
||||
| `False` | 关闭该功能 |
|
||||
| `AgentMiddleware` 实例 | 替换整个实现 |
|
||||
|
||||
不再有 `MemoryOptions`、`TitleOptions` 等。参数调整走 `config` dict：
|
||||
|
||||
```python
|
||||
# 改 memory 参数 → config
|
||||
client = DeerFlowClient(config={"memory": {"max_facts": 50}})
|
||||
|
||||
# 换 memory 实现 → features
|
||||
client = DeerFlowClient(features=RuntimeFeatures(memory=MyMemoryMiddleware()))
|
||||
|
||||
# 两者组合 — config 参数给默认 middleware，但 title 换实现
|
||||
client = DeerFlowClient(
|
||||
config={"memory": {"max_facts": 50}},
|
||||
features=RuntimeFeatures(auto_title=MyTitleMiddleware()),
|
||||
)
|
||||
```
|
||||
|
||||
### 3.4 Middleware 链组装
|
||||
|
||||
不使用 priority 数字排序。按固定顺序 append 构建列表：
|
||||
|
||||
```python
|
||||
def _resolve(spec, default_cls):
|
||||
"""bool → 默认实现 / AgentMiddleware → 替换"""
|
||||
if isinstance(spec, AgentMiddleware):
|
||||
return spec
|
||||
return default_cls()
|
||||
|
||||
def _assemble_from_features(feat: RuntimeFeatures, config: AppConfig) -> tuple[list, list]:
|
||||
chain = []
|
||||
extra_tools = []
|
||||
|
||||
if feat.sandbox:
|
||||
chain.append(_resolve(feat.sandbox, ThreadDataMiddleware))
|
||||
chain.append(UploadsMiddleware())
|
||||
chain.append(_resolve(feat.sandbox, SandboxMiddleware))
|
||||
|
||||
chain.append(DanglingToolCallMiddleware())
|
||||
chain.append(ToolErrorHandlingMiddleware())
|
||||
|
||||
if feat.summarization:
|
||||
chain.append(_resolve(feat.summarization, SummarizationMiddleware))
|
||||
if config.title.enabled and feat.auto_title is not False:
|
||||
chain.append(_resolve(feat.auto_title, TitleMiddleware))
|
||||
if feat.memory:
|
||||
chain.append(_resolve(feat.memory, MemoryMiddleware))
|
||||
if feat.vision:
|
||||
chain.append(ViewImageMiddleware())
|
||||
extra_tools.append(view_image_tool)
|
||||
if feat.subagent:
|
||||
chain.append(_resolve(feat.subagent, SubagentLimitMiddleware))
|
||||
extra_tools.append(task_tool)
|
||||
if feat.loop_detection:
|
||||
chain.append(_resolve(feat.loop_detection, LoopDetectionMiddleware))
|
||||
|
||||
# 插入 extra_middleware（按 @Next/@Prev 声明定位）
|
||||
_insert_extra(chain, extra_middleware)
|
||||
|
||||
# Clarification 永远最后
|
||||
chain.append(ClarificationMiddleware())
|
||||
extra_tools.append(ask_clarification_tool)
|
||||
|
||||
return chain, extra_tools
|
||||
```
|
||||
|
||||
### 3.6 Middleware 排序策略
|
||||
|
||||
**两阶段排序：内置固定 + 外置插入**
|
||||
|
||||
1. **内置链固定顺序** — 按代码中的 append 顺序确定，不参与 @Next/@Prev
|
||||
2. **外置 middleware 插入** — `extra_middleware` 中的 middleware 通过 @Next/@Prev 声明锚点，自由锚定任意 middleware（内置或其他外置均可）
|
||||
3. **冲突检测** — 两个外置 middleware 如果 @Next 或 @Prev 同一个目标 → `ValueError`
|
||||
|
||||
**这不是全排序。** 内置链的顺序在代码中已确定，外置 middleware 只做插入操作。这样可以避免内置和外置同时竞争同一个位置的问题。
|
||||
|
||||
### 3.7 `@Next` / `@Prev` 装饰器
|
||||
|
||||
用户自定义 middleware 通过装饰器声明在链中的位置，类型安全：
|
||||
|
||||
```python
|
||||
from deerflow.agents import Next, Prev
|
||||
|
||||
@Next(SandboxMiddleware)
|
||||
class MyAuditMiddleware(AgentMiddleware):
|
||||
"""排在 SandboxMiddleware 后面"""
|
||||
def before_agent(self, state, runtime):
|
||||
...
|
||||
|
||||
@Prev(ClarificationMiddleware)
|
||||
class MyFilterMiddleware(AgentMiddleware):
|
||||
"""排在 ClarificationMiddleware 前面"""
|
||||
def after_model(self, state, runtime):
|
||||
...
|
||||
```
|
||||
|
||||
实现：
|
||||
|
||||
```python
|
||||
def Next(anchor: type[AgentMiddleware]):
|
||||
"""装饰器：声明本 middleware 排在 anchor 的下一个位置。"""
|
||||
def decorator(cls: type[AgentMiddleware]) -> type[AgentMiddleware]:
|
||||
cls._next_anchor = anchor
|
||||
return cls
|
||||
return decorator
|
||||
|
||||
def Prev(anchor: type[AgentMiddleware]):
|
||||
"""装饰器：声明本 middleware 排在 anchor 的前一个位置。"""
|
||||
def decorator(cls: type[AgentMiddleware]) -> type[AgentMiddleware]:
|
||||
cls._prev_anchor = anchor
|
||||
return cls
|
||||
return decorator
|
||||
```
|
||||
|
||||
`_insert_extra` 算法：
|
||||
|
||||
1. 遍历 `extra_middleware`，读取每个 middleware 的 `_next_anchor` / `_prev_anchor`
|
||||
2. **冲突检测**：如果两个外置 middleware 的锚点相同（同方向同目标），抛出 `ValueError`
|
||||
3. 有锚点的 middleware 插入到目标位置（@Next → 目标之后，@Prev → 目标之前）
|
||||
4. 无声明的 middleware 追加到 Clarification 之前
|
||||
|
||||
## 4. Middleware 执行模型
|
||||
|
||||
### LangChain 的执行规则
|
||||
|
||||
```
|
||||
before_agent 正序 → [0] → [1] → ... → [N]
|
||||
before_model 正序 → [0] → [1] → ... → [N] ← 每轮循环
|
||||
MODEL
|
||||
after_model 反序 ← [N] → [N-1] → ... → [0] ← 每轮循环
|
||||
after_agent 反序 ← [N] → [N-1] → ... → [0]
|
||||
```
|
||||
|
||||
`before_agent` / `after_agent` 只跑一次。`before_model` / `after_model` 每轮 tool call 循环都跑。
|
||||
|
||||
### DeerFlow 的实际情况
|
||||
|
||||
**不是洋葱，是管道。** 11 个 middleware 中只有 SandboxMiddleware 有 before/after 对称（获取/释放），其余只用一个钩子。
|
||||
|
||||
硬依赖只有 2 处：
|
||||
1. **ThreadData 在 Sandbox 之前** — sandbox 需要线程目录
|
||||
2. **Clarification 在列表最后** — after_model 反序时最先执行，第一个拦截 `ask_clarification`
|
||||
|
||||
详见 [middleware-execution-flow.md](middleware-execution-flow.md)。
|
||||
|
||||
## 5. 使用示例
|
||||
|
||||
### 5.1 全读 config.yaml（现有行为不变）
|
||||
|
||||
```python
|
||||
from deerflow.client import DeerFlowClient
|
||||
|
||||
client = DeerFlowClient()
|
||||
response = client.chat("Hello")
|
||||
```
|
||||
|
||||
### 5.2 覆盖配置参数
|
||||
|
||||
```python
|
||||
client = DeerFlowClient(config={
|
||||
"memory": {"max_facts": 50},
|
||||
"title": {"enabled": False},
|
||||
"summarization": {"trigger": [{"type": "tokens", "value": 10000}]},
|
||||
})
|
||||
```
|
||||
|
||||
### 5.3 纯 SDK（无 config.yaml）
|
||||
|
||||
```python
|
||||
client = DeerFlowClient(config={
|
||||
"models": [{"name": "gpt-4o", "use": "langchain_openai:ChatOpenAI", "model": "gpt-4o", "api_key": "sk-..."}],
|
||||
"tools": [
|
||||
{"name": "bash", "group": "bash", "use": "deerflow.sandbox.tools:bash_tool"},
|
||||
{"name": "web_search", "group": "web", "use": "deerflow.community.tavily.tools:web_search_tool"},
|
||||
],
|
||||
"memory": {"enabled": True, "max_facts": 50},
|
||||
"sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"},
|
||||
})
|
||||
```
|
||||
|
||||
### 5.4 替换内置 middleware
|
||||
|
||||
```python
|
||||
from deerflow.agents.features import RuntimeFeatures
|
||||
|
||||
client = DeerFlowClient(
|
||||
features=RuntimeFeatures(
|
||||
memory=MyMemoryMiddleware(), # 替换
|
||||
auto_title=MyTitleMiddleware(), # 替换
|
||||
vision=False, # 关闭
|
||||
),
|
||||
)
|
||||
```
|
||||
|
||||
### 5.5 插入自定义 middleware
|
||||
|
||||
```python
|
||||
from deerflow.agents import Next, Prev
|
||||
from deerflow.sandbox.middleware import SandboxMiddleware
|
||||
from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
|
||||
|
||||
@Next(SandboxMiddleware)
|
||||
class MyAuditMiddleware(AgentMiddleware):
|
||||
def before_agent(self, state, runtime):
|
||||
log_sandbox_acquired(state)
|
||||
|
||||
@Prev(ClarificationMiddleware)
|
||||
class MyFilterMiddleware(AgentMiddleware):
|
||||
def after_model(self, state, runtime):
|
||||
filter_sensitive_output(state)
|
||||
|
||||
client = DeerFlowClient(
|
||||
extra_middleware=[MyAuditMiddleware(), MyFilterMiddleware()],
|
||||
)
|
||||
```
|
||||
|
||||
## 6. Phase 1 限制
|
||||
|
||||
当前实现中以下 middleware 内部仍读 `config.yaml`，SDK 用户需注意：
|
||||
|
||||
| Middleware | 读取内容 | Phase 2 解决方案 |
|
||||
|------------|---------|-----------------|
|
||||
| TitleMiddleware | `get_title_config()` + `create_chat_model()` | `TitleOptions(model=...)` 参数覆盖 |
|
||||
| MemoryMiddleware | `get_memory_config()` | `MemoryOptions(...)` 参数覆盖 |
|
||||
| SandboxMiddleware | `get_sandbox_provider()` | `SandboxProvider` 实例直传 |
|
||||
|
||||
Phase 1 中 `auto_title` 默认为 `False` 以避免无 config 时崩溃。其他有 config 依赖的 feature 默认也为 `False`。
|
||||
|
||||
## 7. 迁移路径
|
||||
|
||||
```
|
||||
Phase 1（当前 PR #1203）:
|
||||
✓ 新增 create_deerflow_agent + RuntimeFeatures（内部 API）
|
||||
✓ 不改 DeerFlowClient 和 make_lead_agent
|
||||
✗ middleware 内部仍读 config（已知限制）
|
||||
|
||||
Phase 2（#1380）:
|
||||
- DeerFlowClient 构造函数增加可选参数（model, tools, features, system_prompt）
|
||||
- Options 参数覆盖 config（MemoryOptions, TitleOptions 等）
|
||||
- @Next/@Prev 装饰器
|
||||
- 补缺失 middleware（Guardrail, TokenUsage, DeferredToolFilter）
|
||||
- make_lead_agent 改为薄壳调 create_deerflow_agent
|
||||
|
||||
Phase 3:
|
||||
- SDK 文档和示例
|
||||
- deerflow.client 稳定 API
|
||||
```
|
||||
|
||||
## 8. 设计决议
|
||||
|
||||
| 问题 | 决议 | 理由 |
|
||||
|------|------|------|
|
||||
| 公开 API | `DeerFlowClient` 唯一入口 | 自顶向下，先改现有 API 再抽底层 |
|
||||
| create_deerflow_agent | 内部实现，不公开 | 用户不需要接触 CompiledStateGraph |
|
||||
| 配置覆盖 | `config` dict，和 config.yaml 结构一致 | 无新概念，deep merge 覆盖 |
|
||||
| middleware 替换 | `features=RuntimeFeatures(memory=MyMW())` | bool 开关 + 实例替换 |
|
||||
| middleware 扩展 | `extra_middleware` 独立参数 | 和内置 features 分开 |
|
||||
| middleware 定位 | `@Next/@Prev` 装饰器 | 类型安全，不暴露排序细节 |
|
||||
| 排序机制 | 顺序 append + @Next/@Prev | priority 数字无功能意义 |
|
||||
| 运行时开关 | 保留 `RunnableConfig` | plan_mode、thread_id 等按请求切换 |
|
||||
|
||||
## 9. 附录：Middleware 链
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
subgraph BA ["before_agent 正序"]
|
||||
direction TB
|
||||
TD["ThreadData<br/>创建目录"] --> UL["Uploads<br/>扫描文件"] --> SB["Sandbox<br/>获取沙箱"]
|
||||
end
|
||||
|
||||
subgraph BM ["before_model 正序 每轮"]
|
||||
direction TB
|
||||
VI["ViewImage<br/>注入图片"]
|
||||
end
|
||||
|
||||
SB --> VI
|
||||
VI --> M["MODEL"]
|
||||
|
||||
subgraph AM ["after_model 反序 每轮"]
|
||||
direction TB
|
||||
CL["Clarification<br/>拦截中断"] --> LD["LoopDetection<br/>检测循环"] --> SL["SubagentLimit<br/>截断 task"] --> TI["Title<br/>生成标题"] --> DTC["DanglingToolCall<br/>补缺失消息"]
|
||||
end
|
||||
|
||||
M --> CL
|
||||
|
||||
subgraph AA ["after_agent 反序"]
|
||||
direction TB
|
||||
SBR["Sandbox<br/>释放沙箱"] --> MEM["Memory<br/>入队记忆"]
|
||||
end
|
||||
|
||||
DTC --> SBR
|
||||
|
||||
classDef beforeNode fill:#a0a8b5,stroke:#636b7a,color:#2d3239
|
||||
classDef modelNode fill:#b5a8a0,stroke:#7a6b63,color:#2d3239
|
||||
classDef afterModelNode fill:#b5a0a8,stroke:#7a636b,color:#2d3239
|
||||
classDef afterAgentNode fill:#a0b5a8,stroke:#637a6b,color:#2d3239
|
||||
|
||||
class TD,UL,SB,VI beforeNode
|
||||
class M modelNode
|
||||
class CL,LD,SL,TI,DTC afterModelNode
|
||||
class SBR,MEM afterAgentNode
|
||||
```
|
||||
|
||||
硬依赖：
|
||||
- ThreadData → Uploads → Sandbox（before_agent 阶段）
|
||||
- Clarification 必须在列表最后（after_model 反序时最先执行）
|
||||
|
||||
## 10. 主 Agent 与 Subagent 的 Middleware 差异
|
||||
|
||||
主 agent 和 subagent 共享基础 middleware 链（`_build_runtime_middlewares`），subagent 在此基础上做精简：
|
||||
|
||||
| Middleware | 主 Agent | Subagent | 说明 |
|
||||
|------------|:-------:|:--------:|------|
|
||||
| ThreadDataMiddleware | ✓ | ✓ | 共享：创建线程目录 |
|
||||
| UploadsMiddleware | ✓ | ✗ | 主 agent 独有：扫描上传文件 |
|
||||
| SandboxMiddleware | ✓ | ✓ | 共享：获取/释放沙箱 |
|
||||
| DanglingToolCallMiddleware | ✓ | ✗ | 主 agent 独有：补缺失 ToolMessage |
|
||||
| GuardrailMiddleware | ✓ | ✓ | 共享：工具调用授权（可选） |
|
||||
| ToolErrorHandlingMiddleware | ✓ | ✓ | 共享：工具异常处理 |
|
||||
| SummarizationMiddleware | ✓ | ✗ | |
|
||||
| TodoMiddleware | ✓ | ✗ | |
|
||||
| TitleMiddleware | ✓ | ✗ | |
|
||||
| MemoryMiddleware | ✓ | ✗ | |
|
||||
| ViewImageMiddleware | ✓ | ✗ | |
|
||||
| SubagentLimitMiddleware | ✓ | ✗ | |
|
||||
| LoopDetectionMiddleware | ✓ | ✗ | |
|
||||
| ClarificationMiddleware | ✓ | ✗ | |
|
||||
|
||||
**设计原则**：
|
||||
- `RuntimeFeatures`、`@Next/@Prev`、排序机制只作用于**主 agent**
|
||||
- Subagent 链短且固定（4 个），不需要动态组装
|
||||
- `extra_middleware` 当前只影响主 agent，不传递给 subagent
|
||||
190
deer-flow/backend/docs/rfc-extract-shared-modules.md
Normal file
190
deer-flow/backend/docs/rfc-extract-shared-modules.md
Normal file
@@ -0,0 +1,190 @@
|
||||
# RFC: Extract Shared Skill Installer and Upload Manager into Harness
|
||||
|
||||
## 1. Problem
|
||||
|
||||
Gateway (`app/gateway/routers/skills.py`, `uploads.py`) and Client (`deerflow/client.py`) each independently implement the same business logic:
|
||||
|
||||
### Skill Installation
|
||||
|
||||
| Logic | Gateway (`skills.py`) | Client (`client.py`) |
|
||||
|-------|----------------------|---------------------|
|
||||
| Zip safety check | `_is_unsafe_zip_member()` | Inline `Path(info.filename).is_absolute()` |
|
||||
| Symlink filtering | `_is_symlink_member()` | `p.is_symlink()` post-extraction delete |
|
||||
| Zip bomb defence | `total_size += info.file_size` (declared) | `total_size > 100MB` (declared) |
|
||||
| macOS metadata filter | `_should_ignore_archive_entry()` | None |
|
||||
| Frontmatter validation | `_validate_skill_frontmatter()` | `_validate_skill_frontmatter()` |
|
||||
| Duplicate detection | `HTTPException(409)` | `ValueError` |
|
||||
|
||||
**Two implementations, inconsistent behaviour**: Gateway streams writes and tracks real decompressed size; Client sums declared `file_size`. Gateway skips symlinks during extraction; Client extracts everything then walks and deletes symlinks.
|
||||
|
||||
### Upload Management
|
||||
|
||||
| Logic | Gateway (`uploads.py`) | Client (`client.py`) |
|
||||
|-------|----------------------|---------------------|
|
||||
| Directory access | `get_uploads_dir()` + `mkdir` | `_get_uploads_dir()` + `mkdir` |
|
||||
| Filename safety | Inline `Path(f).name` + manual checks | No checks, uses `src_path.name` directly |
|
||||
| Duplicate handling | None (overwrites) | None (overwrites) |
|
||||
| Listing | Inline `iterdir()` | Inline `os.scandir()` |
|
||||
| Deletion | Inline `unlink()` + traversal check | Inline `unlink()` + traversal check |
|
||||
| Path traversal | `resolve().relative_to()` | `resolve().relative_to()` |
|
||||
|
||||
**The same traversal check is written twice** — any security fix must be applied to both locations.
|
||||
|
||||
## 2. Design Principles
|
||||
|
||||
### Dependency Direction
|
||||
|
||||
```
|
||||
app.gateway.routers.skills ──┐
|
||||
app.gateway.routers.uploads ──┤── calls ──→ deerflow.skills.installer
|
||||
deerflow.client ──┘ deerflow.uploads.manager
|
||||
```
|
||||
|
||||
- Shared modules live in the harness layer (`deerflow.*`), pure business logic, no FastAPI dependency
|
||||
- Gateway handles HTTP adaptation (`UploadFile` → bytes, exceptions → `HTTPException`)
|
||||
- Client handles local adaptation (`Path` → copy, exceptions → Python exceptions)
|
||||
- Satisfies `test_harness_boundary.py` constraint: harness never imports app
|
||||
|
||||
### Exception Strategy
|
||||
|
||||
| Shared Layer Exception | Gateway Maps To | Client |
|
||||
|----------------------|-----------------|--------|
|
||||
| `FileNotFoundError` | `HTTPException(404)` | Propagates |
|
||||
| `ValueError` | `HTTPException(400)` | Propagates |
|
||||
| `SkillAlreadyExistsError` | `HTTPException(409)` | Propagates |
|
||||
| `PermissionError` | `HTTPException(403)` | Propagates |
|
||||
|
||||
Replaces stringly-typed routing (`"already exists" in str(e)`) with typed exception matching (`SkillAlreadyExistsError`).
|
||||
|
||||
## 3. New Modules
|
||||
|
||||
### 3.1 `deerflow.skills.installer`
|
||||
|
||||
```python
|
||||
# Safety checks
|
||||
is_unsafe_zip_member(info: ZipInfo) -> bool # Absolute path / .. traversal
|
||||
is_symlink_member(info: ZipInfo) -> bool # Unix symlink detection
|
||||
should_ignore_archive_entry(path: Path) -> bool # __MACOSX / dotfiles
|
||||
|
||||
# Extraction
|
||||
safe_extract_skill_archive(zip_ref, dest_path, max_total_size=512MB)
|
||||
# Streaming write, accumulates real bytes (vs declared file_size)
|
||||
# Dual traversal check: member-level + resolve-level
|
||||
|
||||
# Directory resolution
|
||||
resolve_skill_dir_from_archive(temp_path: Path) -> Path
|
||||
# Auto-enters single directory, filters macOS metadata
|
||||
|
||||
# Install entry point
|
||||
install_skill_from_archive(zip_path, *, skills_root=None) -> dict
|
||||
# is_file() pre-check before extension validation
|
||||
# SkillAlreadyExistsError replaces ValueError
|
||||
|
||||
# Exception
|
||||
class SkillAlreadyExistsError(ValueError)
|
||||
```
|
||||
|
||||
### 3.2 `deerflow.uploads.manager`
|
||||
|
||||
```python
|
||||
# Directory management
|
||||
get_uploads_dir(thread_id: str) -> Path # Pure path, no side effects
|
||||
ensure_uploads_dir(thread_id: str) -> Path # Creates directory (for write paths)
|
||||
|
||||
# Filename safety
|
||||
normalize_filename(filename: str) -> str
|
||||
# Path.name extraction + rejects ".." / "." / backslash / >255 bytes
|
||||
deduplicate_filename(name: str, seen: set) -> str
|
||||
# _N suffix increment for dedup, mutates seen in place
|
||||
|
||||
# Path safety
|
||||
validate_path_traversal(path: Path, base: Path) -> None
|
||||
# resolve().relative_to(), raises PermissionError on failure
|
||||
|
||||
# File operations
|
||||
list_files_in_dir(directory: Path) -> dict
|
||||
# scandir with stat inside context (no re-stat)
|
||||
# follow_symlinks=False to prevent metadata leakage
|
||||
# Non-existent directory returns empty list
|
||||
delete_file_safe(base_dir: Path, filename: str) -> dict
|
||||
# Validates traversal first, then unlinks
|
||||
|
||||
# URL helpers
|
||||
upload_artifact_url(thread_id, filename) -> str # Percent-encoded for HTTP safety
|
||||
upload_virtual_path(filename) -> str # Sandbox-internal path
|
||||
enrich_file_listing(result, thread_id) -> dict # Adds URLs, stringifies sizes
|
||||
```
|
||||
|
||||
## 4. Changes
|
||||
|
||||
### 4.1 Gateway Slimming
|
||||
|
||||
**`app/gateway/routers/skills.py`**:
|
||||
- Remove `_is_unsafe_zip_member`, `_is_symlink_member`, `_safe_extract_skill_archive`, `_should_ignore_archive_entry`, `_resolve_skill_dir_from_archive_root` (~80 lines)
|
||||
- `install_skill` route becomes a single call to `install_skill_from_archive(path)`
|
||||
- Exception mapping: `SkillAlreadyExistsError → 409`, `ValueError → 400`, `FileNotFoundError → 404`
|
||||
|
||||
**`app/gateway/routers/uploads.py`**:
|
||||
- Remove inline `get_uploads_dir` (replaced by `ensure_uploads_dir`/`get_uploads_dir`)
|
||||
- `upload_files` uses `normalize_filename()` instead of inline safety checks
|
||||
- `list_uploaded_files` uses `list_files_in_dir()` + enrichment
|
||||
- `delete_uploaded_file` uses `delete_file_safe()` + companion markdown cleanup
|
||||
|
||||
### 4.2 Client Slimming
|
||||
|
||||
**`deerflow/client.py`**:
|
||||
- Remove `_get_uploads_dir` static method
|
||||
- Remove ~50 lines of inline zip handling in `install_skill`
|
||||
- `install_skill` delegates to `install_skill_from_archive()`
|
||||
- `upload_files` uses `deduplicate_filename()` + `ensure_uploads_dir()`
|
||||
- `list_uploads` uses `get_uploads_dir()` + `list_files_in_dir()`
|
||||
- `delete_upload` uses `get_uploads_dir()` + `delete_file_safe()`
|
||||
- `update_mcp_config` / `update_skill` now reset `_agent_config_key = None`
|
||||
|
||||
### 4.3 Read/Write Path Separation
|
||||
|
||||
| Operation | Function | Creates dir? |
|
||||
|-----------|----------|:------------:|
|
||||
| upload (write) | `ensure_uploads_dir()` | Yes |
|
||||
| list (read) | `get_uploads_dir()` | No |
|
||||
| delete (read) | `get_uploads_dir()` | No |
|
||||
|
||||
Read paths no longer have `mkdir` side effects — non-existent directories return empty lists.
|
||||
|
||||
## 5. Security Improvements
|
||||
|
||||
| Improvement | Before | After |
|
||||
|-------------|--------|-------|
|
||||
| Zip bomb detection | Sum of declared `file_size` | Streaming write, accumulates real bytes |
|
||||
| Symlink handling | Gateway skips / Client deletes post-extract | Unified skip + log |
|
||||
| Traversal check | Member-level only | Member-level + `resolve().is_relative_to()` |
|
||||
| Filename backslash | Gateway checks / Client doesn't | Unified rejection |
|
||||
| Filename length | No check | Reject > 255 bytes (OS limit) |
|
||||
| thread_id validation | None | Reject unsafe filesystem characters |
|
||||
| Listing symlink leak | `follow_symlinks=True` (default) | `follow_symlinks=False` |
|
||||
| 409 status routing | `"already exists" in str(e)` | `SkillAlreadyExistsError` type match |
|
||||
| Artifact URL encoding | Raw filename in URL | `urllib.parse.quote()` |
|
||||
|
||||
## 6. Alternatives Considered
|
||||
|
||||
| Alternative | Why Not |
|
||||
|-------------|---------|
|
||||
| Keep logic in Gateway, Client calls Gateway via HTTP | Adds network dependency to embedded Client; defeats the purpose of `DeerFlowClient` as an in-process API |
|
||||
| Abstract base class with Gateway/Client subclasses | Over-engineered for what are pure functions; no polymorphism needed |
|
||||
| Move everything into `client.py` and have Gateway import it | Violates harness/app boundary — Client is in harness, but Gateway-specific models (Pydantic response types) should stay in app layer |
|
||||
| Merge Gateway and Client into one module | They serve different consumers (HTTP vs in-process) with different adaptation needs |
|
||||
|
||||
## 7. Breaking Changes
|
||||
|
||||
**None.** All public APIs (Gateway HTTP endpoints, `DeerFlowClient` methods) retain their existing signatures and return formats. The `SkillAlreadyExistsError` is a subclass of `ValueError`, so existing `except ValueError` handlers still catch it.
|
||||
|
||||
## 8. Tests
|
||||
|
||||
| Module | Test File | Count |
|
||||
|--------|-----------|:-----:|
|
||||
| `skills.installer` | `tests/test_skills_installer.py` | 22 |
|
||||
| `uploads.manager` | `tests/test_uploads_manager.py` | 20 |
|
||||
| `client` hardening | `tests/test_client.py` (new cases) | ~40 |
|
||||
| `client` e2e | `tests/test_client_e2e.py` (new file) | ~20 |
|
||||
|
||||
Coverage: unsafe zip / symlink / zip bomb / frontmatter / duplicate / extension / macOS filter / normalize / deduplicate / traversal / list / delete / agent invalidation / upload lifecycle / thread isolation / URL encoding / config pollution.
|
||||
446
deer-flow/backend/docs/rfc-grep-glob-tools.md
Normal file
446
deer-flow/backend/docs/rfc-grep-glob-tools.md
Normal file
@@ -0,0 +1,446 @@
|
||||
# [RFC] 在 DeerFlow 中增加 `grep` 与 `glob` 文件搜索工具
|
||||
|
||||
## Summary
|
||||
|
||||
我认为这个方向是对的，而且值得做。
|
||||
|
||||
如果 DeerFlow 想更接近 Claude Code 这类 coding agent 的实际工作流，仅有 `ls` / `read_file` / `write_file` / `str_replace` 还不够。模型在进入修改前，通常还需要两类能力：
|
||||
|
||||
- `glob`: 快速按路径模式找文件
|
||||
- `grep`: 快速按内容模式找候选位置
|
||||
|
||||
这两类工具的价值，不是“功能上 bash 也能做”，而是它们能以更低 token 成本、更强约束、更稳定的输出格式，替代模型频繁走 `bash find` / `bash grep` / `rg` 的习惯。
|
||||
|
||||
但前提是实现方式要对：**它们应该是只读、结构化、受限、可审计的原生工具，而不是对 shell 命令的简单包装。**
|
||||
|
||||
## Problem
|
||||
|
||||
当前 DeerFlow 的文件工具层主要覆盖：
|
||||
|
||||
- `ls`: 浏览目录结构
|
||||
- `read_file`: 读取文件内容
|
||||
- `write_file`: 写文件
|
||||
- `str_replace`: 做局部字符串替换
|
||||
- `bash`: 兜底执行命令
|
||||
|
||||
这套能力能完成任务，但在代码库探索阶段效率不高。
|
||||
|
||||
典型问题：
|
||||
|
||||
1. 模型想找 “所有 `*.tsx` 的 page 文件” 时，只能反复 `ls` 多层目录，或者退回 `bash find`
|
||||
2. 模型想找 “某个 symbol / 文案 / 配置键在哪里出现” 时，只能逐文件 `read_file`，或者退回 `bash grep` / `rg`
|
||||
3. 一旦退回 `bash`，工具调用就失去结构化输出，结果也更难做裁剪、分页、审计和跨 sandbox 一致化
|
||||
4. 对没有开启 host bash 的本地模式，`bash` 甚至可能不可用，此时缺少足够强的只读检索能力
|
||||
|
||||
结论：DeerFlow 现在缺的不是“再多一个 shell 命令”，而是**文件系统检索层**。
|
||||
|
||||
## Goals
|
||||
|
||||
- 为 agent 提供稳定的路径搜索和内容搜索能力
|
||||
- 减少对 `bash` 的依赖，特别是在仓库探索阶段
|
||||
- 保持与现有 sandbox 安全模型一致
|
||||
- 输出格式结构化，便于模型后续串联 `read_file` / `str_replace`
|
||||
- 让本地 sandbox、容器 sandbox、未来 MCP 文件系统工具都能遵守同一语义
|
||||
|
||||
## Non-Goals
|
||||
|
||||
- 不做通用 shell 兼容层
|
||||
- 不暴露完整 grep/find/rg CLI 语法
|
||||
- 不在第一版支持二进制检索、复杂 PCRE 特性、上下文窗口高亮渲染等重功能
|
||||
- 不把它做成“任意磁盘搜索”，仍然只允许在 DeerFlow 已授权的路径内执行
|
||||
|
||||
## Why This Is Worth Doing
|
||||
|
||||
参考 Claude Code 这一类 agent 的设计思路，`glob` 和 `grep` 的核心价值不是新能力本身，而是把“探索代码库”的常见动作从开放式 shell 降到受控工具层。
|
||||
|
||||
这样有几个直接收益：
|
||||
|
||||
1. **更低的模型负担**
|
||||
模型不需要自己拼 `find`, `grep`, `rg`, `xargs`, quoting 等命令细节。
|
||||
|
||||
2. **更稳定的跨环境行为**
|
||||
本地、Docker、AIO sandbox 不必依赖容器里是否装了 `rg`，也不会因为 shell 差异导致行为漂移。
|
||||
|
||||
3. **更强的安全与审计**
|
||||
调用参数就是“搜索什么、在哪搜、最多返回多少”，天然比任意命令更容易审计和限流。
|
||||
|
||||
4. **更好的 token 效率**
|
||||
`grep` 返回的是命中摘要而不是整段文件，模型只对少数候选路径再调用 `read_file`。
|
||||
|
||||
5. **对 `tool_search` 友好**
|
||||
当 DeerFlow 持续扩展工具集时，`grep` / `glob` 会成为非常高频的基础工具，值得保留为 built-in，而不是让模型总是退回通用 bash。
|
||||
|
||||
## Proposal
|
||||
|
||||
增加两个 built-in sandbox tools：
|
||||
|
||||
- `glob`
|
||||
- `grep`
|
||||
|
||||
推荐继续放在：
|
||||
|
||||
- `backend/packages/harness/deerflow/sandbox/tools.py`
|
||||
|
||||
并在 `config.example.yaml` 中默认加入 `file:read` 组。
|
||||
|
||||
### 1. `glob` 工具
|
||||
|
||||
用途：按路径模式查找文件或目录。
|
||||
|
||||
建议 schema：
|
||||
|
||||
```python
|
||||
@tool("glob", parse_docstring=True)
|
||||
def glob_tool(
|
||||
runtime: ToolRuntime[ContextT, ThreadState],
|
||||
description: str,
|
||||
pattern: str,
|
||||
path: str,
|
||||
include_dirs: bool = False,
|
||||
max_results: int = 200,
|
||||
) -> str:
|
||||
...
|
||||
```
|
||||
|
||||
参数语义：
|
||||
|
||||
- `description`: 与现有工具保持一致
|
||||
- `pattern`: glob 模式，例如 `**/*.py`、`src/**/test_*.ts`
|
||||
- `path`: 搜索根目录，必须是绝对路径
|
||||
- `include_dirs`: 是否返回目录
|
||||
- `max_results`: 最大返回条数，防止一次性打爆上下文
|
||||
|
||||
建议返回格式：
|
||||
|
||||
```text
|
||||
Found 3 paths under /mnt/user-data/workspace
|
||||
1. /mnt/user-data/workspace/backend/app.py
|
||||
2. /mnt/user-data/workspace/backend/tests/test_app.py
|
||||
3. /mnt/user-data/workspace/scripts/build.py
|
||||
```
|
||||
|
||||
如果后续想更适合前端消费，也可以改成 JSON 字符串；但第一版为了兼容现有工具风格，返回可读文本即可。
|
||||
|
||||
### 2. `grep` 工具
|
||||
|
||||
用途：按内容模式搜索文件，返回命中位置摘要。
|
||||
|
||||
建议 schema：
|
||||
|
||||
```python
|
||||
@tool("grep", parse_docstring=True)
|
||||
def grep_tool(
|
||||
runtime: ToolRuntime[ContextT, ThreadState],
|
||||
description: str,
|
||||
pattern: str,
|
||||
path: str,
|
||||
glob: str | None = None,
|
||||
literal: bool = False,
|
||||
case_sensitive: bool = False,
|
||||
max_results: int = 100,
|
||||
) -> str:
|
||||
...
|
||||
```
|
||||
|
||||
参数语义：
|
||||
|
||||
- `pattern`: 搜索词或正则
|
||||
- `path`: 搜索根目录，必须是绝对路径
|
||||
- `glob`: 可选路径过滤，例如 `**/*.py`
|
||||
- `literal`: 为 `True` 时按普通字符串匹配，不解释为正则
|
||||
- `case_sensitive`: 是否大小写敏感
|
||||
- `max_results`: 最大返回命中数，不是文件数
|
||||
|
||||
建议返回格式：
|
||||
|
||||
```text
|
||||
Found 4 matches under /mnt/user-data/workspace
|
||||
/mnt/user-data/workspace/backend/config.py:12: TOOL_GROUPS = [...]
|
||||
/mnt/user-data/workspace/backend/config.py:48: def load_tool_config(...):
|
||||
/mnt/user-data/workspace/backend/tools.py:91: "tool_groups"
|
||||
/mnt/user-data/workspace/backend/tests/test_config.py:22: assert "tool_groups" in data
|
||||
```
|
||||
|
||||
第一版建议只返回：
|
||||
|
||||
- 文件路径
|
||||
- 行号
|
||||
- 命中行摘要
|
||||
|
||||
不返回上下文块，避免结果过大。模型如果需要上下文，再调用 `read_file(path, start_line, end_line)`。
|
||||
|
||||
## Design Principles
|
||||
|
||||
### A. 不做 shell wrapper
|
||||
|
||||
不建议把 `grep` 实现为：
|
||||
|
||||
```python
|
||||
subprocess.run("grep ...")
|
||||
```
|
||||
|
||||
也不建议在容器里直接拼 `find` / `rg` 命令。
|
||||
|
||||
原因：
|
||||
|
||||
- 会引入 shell quoting 和注入面
|
||||
- 会依赖不同 sandbox 内镜像是否安装同一套命令
|
||||
- Windows / macOS / Linux 行为不一致
|
||||
- 很难稳定控制输出条数与格式
|
||||
|
||||
正确方向是：
|
||||
|
||||
- `glob` 使用 Python 标准库路径遍历
|
||||
- `grep` 使用 Python 逐文件扫描
|
||||
- 输出由 DeerFlow 自己格式化
|
||||
|
||||
如果未来为了性能考虑要优先调用 `rg`，也应该封装在 provider 内部，并保证外部语义不变，而不是把 CLI 暴露给模型。
|
||||
|
||||
### B. 继续沿用 DeerFlow 的路径权限模型
|
||||
|
||||
这两个工具必须复用当前 `ls` / `read_file` 的路径校验逻辑：
|
||||
|
||||
- 本地模式走 `validate_local_tool_path(..., read_only=True)`
|
||||
- 支持 `/mnt/skills/...`
|
||||
- 支持 `/mnt/acp-workspace/...`
|
||||
- 支持 thread workspace / uploads / outputs 的虚拟路径解析
|
||||
- 明确拒绝越权路径与 path traversal
|
||||
|
||||
也就是说，它们属于 **file:read**，不是 `bash` 的替代越权入口。
|
||||
|
||||
### C. 结果必须硬限制
|
||||
|
||||
没有硬限制的 `glob` / `grep` 很容易炸上下文。
|
||||
|
||||
建议第一版至少限制：
|
||||
|
||||
- `glob.max_results` 默认 200，最大 1000
|
||||
- `grep.max_results` 默认 100，最大 500
|
||||
- 单行摘要最大长度，例如 200 字符
|
||||
- 二进制文件跳过
|
||||
- 超大文件跳过，例如单文件大于 1 MB 或按配置控制
|
||||
|
||||
此外，命中数超过阈值时应返回：
|
||||
|
||||
- 已展示的条数
|
||||
- 被截断的事实
|
||||
- 建议用户缩小搜索范围
|
||||
|
||||
例如：
|
||||
|
||||
```text
|
||||
Found more than 100 matches, showing first 100. Narrow the path or add a glob filter.
|
||||
```
|
||||
|
||||
### D. 工具语义要彼此互补
|
||||
|
||||
推荐模型工作流应该是：
|
||||
|
||||
1. `glob` 找候选文件
|
||||
2. `grep` 找候选位置
|
||||
3. `read_file` 读局部上下文
|
||||
4. `str_replace` / `write_file` 执行修改
|
||||
|
||||
这样工具边界清晰，也更利于 prompt 中教模型形成稳定习惯。
|
||||
|
||||
## Implementation Approach
|
||||
|
||||
## Option A: 直接在 `sandbox/tools.py` 实现第一版
|
||||
|
||||
这是我推荐的起步方案。
|
||||
|
||||
做法：
|
||||
|
||||
- 在 `sandbox/tools.py` 新增 `glob_tool` 与 `grep_tool`
|
||||
- 在 local sandbox 场景直接使用 Python 文件系统 API
|
||||
- 在非 local sandbox 场景，优先也通过 DeerFlow 自己控制的路径访问层实现
|
||||
|
||||
优点：
|
||||
|
||||
- 改动小
|
||||
- 能尽快验证 agent 效果
|
||||
- 不需要先改 `Sandbox` 抽象
|
||||
|
||||
缺点：
|
||||
|
||||
- `tools.py` 会继续变胖
|
||||
- 如果未来想在 provider 侧做性能优化，需要再抽象一次
|
||||
|
||||
## Option B: 先扩展 `Sandbox` 抽象
|
||||
|
||||
例如新增：
|
||||
|
||||
```python
|
||||
class Sandbox(ABC):
|
||||
def glob(self, path: str, pattern: str, include_dirs: bool = False, max_results: int = 200) -> list[str]:
|
||||
...
|
||||
|
||||
def grep(
|
||||
self,
|
||||
path: str,
|
||||
pattern: str,
|
||||
*,
|
||||
glob: str | None = None,
|
||||
literal: bool = False,
|
||||
case_sensitive: bool = False,
|
||||
max_results: int = 100,
|
||||
) -> list[GrepMatch]:
|
||||
...
|
||||
```
|
||||
|
||||
优点：
|
||||
|
||||
- 抽象更干净
|
||||
- 容器 / 远程 sandbox 可以各自优化
|
||||
|
||||
缺点：
|
||||
|
||||
- 首次引入成本更高
|
||||
- 需要同步改所有 sandbox provider
|
||||
|
||||
结论：
|
||||
|
||||
**第一版建议走 Option A，等工具价值验证后再下沉到 `Sandbox` 抽象层。**
|
||||
|
||||
## Detailed Behavior
|
||||
|
||||
### `glob` 行为
|
||||
|
||||
- 输入根目录不存在：返回清晰错误
|
||||
- 根路径不是目录：返回清晰错误
|
||||
- 模式非法：返回清晰错误
|
||||
- 结果为空：返回 `No files matched`
|
||||
- 默认忽略项应尽量与当前 `list_dir` 对齐，例如：
|
||||
- `.git`
|
||||
- `node_modules`
|
||||
- `__pycache__`
|
||||
- `.venv`
|
||||
- 构建产物目录
|
||||
|
||||
这里建议抽一个共享 ignore 集，避免 `ls` 与 `glob` 结果风格不一致。
|
||||
|
||||
### `grep` 行为
|
||||
|
||||
- 默认只扫描文本文件
|
||||
- 检测到二进制文件直接跳过
|
||||
- 对超大文件直接跳过或只扫前 N KB
|
||||
- regex 编译失败时返回参数错误
|
||||
- 输出中的路径继续使用虚拟路径，而不是暴露宿主真实路径
|
||||
- 建议默认按文件路径、行号排序，保持稳定输出
|
||||
|
||||
## Prompting Guidance
|
||||
|
||||
如果引入这两个工具，建议同步更新系统提示中的文件操作建议：
|
||||
|
||||
- 查找文件名模式时优先用 `glob`
|
||||
- 查找代码符号、配置项、文案时优先用 `grep`
|
||||
- 只有在工具不足以完成目标时才退回 `bash`
|
||||
|
||||
否则模型仍会习惯性先调用 `bash`。
|
||||
|
||||
## Risks
|
||||
|
||||
### 1. 与 `bash` 能力重叠
|
||||
|
||||
这是事实，但不是问题。
|
||||
|
||||
`ls` 和 `read_file` 也都能被 `bash` 替代，但我们仍然保留它们，因为结构化工具更适合 agent。
|
||||
|
||||
### 2. 性能问题
|
||||
|
||||
在大仓库上，纯 Python `grep` 可能比 `rg` 慢。
|
||||
|
||||
缓解方式：
|
||||
|
||||
- 第一版先加结果上限和文件大小上限
|
||||
- 路径上强制要求 root path
|
||||
- 提供 `glob` 过滤缩小扫描范围
|
||||
- 后续如有必要，在 provider 内部做 `rg` 优化，但保持同一 schema
|
||||
|
||||
### 3. 忽略规则不一致
|
||||
|
||||
如果 `ls` 能看到的路径，`glob` 却看不到，模型会困惑。
|
||||
|
||||
缓解方式：
|
||||
|
||||
- 统一 ignore 规则
|
||||
- 在文档里明确“默认跳过常见依赖和构建目录”
|
||||
|
||||
### 4. 正则搜索过于复杂
|
||||
|
||||
如果第一版就支持大量 grep 方言，边界会很乱。
|
||||
|
||||
缓解方式：
|
||||
|
||||
- 第一版只支持 Python `re`
|
||||
- 并提供 `literal=True` 的简单模式
|
||||
|
||||
## Alternatives Considered
|
||||
|
||||
### A. 不增加工具，完全依赖 `bash`
|
||||
|
||||
不推荐。
|
||||
|
||||
这会让 DeerFlow 在代码探索体验上持续落后，也削弱无 bash 或受限 bash 场景下的能力。
|
||||
|
||||
### B. 只加 `glob`，不加 `grep`
|
||||
|
||||
不推荐。
|
||||
|
||||
只解决“找文件”，没有解决“找位置”。模型最终还是会退回 `bash grep`。
|
||||
|
||||
### C. 只加 `grep`，不加 `glob`
|
||||
|
||||
也不推荐。
|
||||
|
||||
`grep` 缺少路径模式过滤时，扫描范围经常太大；`glob` 是它的天然前置工具。
|
||||
|
||||
### D. 直接接入 MCP filesystem server 的搜索能力
|
||||
|
||||
短期不推荐作为主路径。
|
||||
|
||||
MCP 可以是补充，但 `glob` / `grep` 作为 DeerFlow 的基础 coding tool，最好仍然是 built-in，这样才能在默认安装中稳定可用。
|
||||
|
||||
## Acceptance Criteria
|
||||
|
||||
- `config.example.yaml` 中可默认启用 `glob` 与 `grep`
|
||||
- 两个工具归属 `file:read` 组
|
||||
- 本地 sandbox 下严格遵守现有路径权限
|
||||
- 输出不泄露宿主机真实路径
|
||||
- 大结果集会被截断并明确提示
|
||||
- 模型可以通过 `glob -> grep -> read_file -> str_replace` 完成典型改码流
|
||||
- 在禁用 host bash 的本地模式下，仓库探索能力明显提升
|
||||
|
||||
## Rollout Plan
|
||||
|
||||
1. 在 `sandbox/tools.py` 中实现 `glob_tool` 与 `grep_tool`
|
||||
2. 抽取与 `list_dir` 一致的 ignore 规则，避免行为漂移
|
||||
3. 在 `config.example.yaml` 默认加入工具配置
|
||||
4. 为本地路径校验、虚拟路径映射、结果截断、二进制跳过补测试
|
||||
5. 更新 README / backend docs / prompt guidance
|
||||
6. 收集实际 agent 调用数据，再决定是否下沉到 `Sandbox` 抽象
|
||||
|
||||
## Suggested Config
|
||||
|
||||
```yaml
|
||||
tools:
|
||||
- name: glob
|
||||
group: file:read
|
||||
use: deerflow.sandbox.tools:glob_tool
|
||||
|
||||
- name: grep
|
||||
group: file:read
|
||||
use: deerflow.sandbox.tools:grep_tool
|
||||
```
|
||||
|
||||
## Final Recommendation
|
||||
|
||||
结论是：**可以加，而且应该加。**
|
||||
|
||||
但我会明确卡三个边界：
|
||||
|
||||
1. `grep` / `glob` 必须是 built-in 的只读结构化工具
|
||||
2. 第一版不要做 shell wrapper，不要把 CLI 方言直接暴露给模型
|
||||
3. 先在 `sandbox/tools.py` 验证价值，再考虑是否下沉到 `Sandbox` provider 抽象
|
||||
|
||||
如果按这个方向做，它会明显提升 DeerFlow 在 coding / repo exploration 场景下的可用性，而且风险可控。
|
||||
353
deer-flow/backend/docs/summarization.md
Normal file
353
deer-flow/backend/docs/summarization.md
Normal file
@@ -0,0 +1,353 @@
|
||||
# Conversation Summarization
|
||||
|
||||
DeerFlow includes automatic conversation summarization to handle long conversations that approach model token limits. When enabled, the system automatically condenses older messages while preserving recent context.
|
||||
|
||||
## Overview
|
||||
|
||||
The summarization feature uses LangChain's `SummarizationMiddleware` to monitor conversation history and trigger summarization based on configurable thresholds. When activated, it:
|
||||
|
||||
1. Monitors message token counts in real-time
|
||||
2. Triggers summarization when thresholds are met
|
||||
3. Keeps recent messages intact while summarizing older exchanges
|
||||
4. Maintains AI/Tool message pairs together for context continuity
|
||||
5. Injects the summary back into the conversation
|
||||
|
||||
## Configuration
|
||||
|
||||
Summarization is configured in `config.yaml` under the `summarization` key:
|
||||
|
||||
```yaml
|
||||
summarization:
|
||||
enabled: true
|
||||
model_name: null # Use default model or specify a lightweight model
|
||||
|
||||
# Trigger conditions (OR logic - any condition triggers summarization)
|
||||
trigger:
|
||||
- type: tokens
|
||||
value: 4000
|
||||
# Additional triggers (optional)
|
||||
# - type: messages
|
||||
# value: 50
|
||||
# - type: fraction
|
||||
# value: 0.8 # 80% of model's max input tokens
|
||||
|
||||
# Context retention policy
|
||||
keep:
|
||||
type: messages
|
||||
value: 20
|
||||
|
||||
# Token trimming for summarization call
|
||||
trim_tokens_to_summarize: 4000
|
||||
|
||||
# Custom summary prompt (optional)
|
||||
summary_prompt: null
|
||||
```
|
||||
|
||||
### Configuration Options
|
||||
|
||||
#### `enabled`
|
||||
- **Type**: Boolean
|
||||
- **Default**: `false`
|
||||
- **Description**: Enable or disable automatic summarization
|
||||
|
||||
#### `model_name`
|
||||
- **Type**: String or null
|
||||
- **Default**: `null` (uses default model)
|
||||
- **Description**: Model to use for generating summaries. Recommended to use a lightweight, cost-effective model like `gpt-4o-mini` or equivalent.
|
||||
|
||||
#### `trigger`
|
||||
- **Type**: Single `ContextSize` or list of `ContextSize` objects
|
||||
- **Required**: At least one trigger must be specified when enabled
|
||||
- **Description**: Thresholds that trigger summarization. Uses OR logic - summarization runs when ANY threshold is met.
|
||||
|
||||
**ContextSize Types:**
|
||||
|
||||
1. **Token-based trigger**: Activates when token count reaches the specified value
|
||||
```yaml
|
||||
trigger:
|
||||
type: tokens
|
||||
value: 4000
|
||||
```
|
||||
|
||||
2. **Message-based trigger**: Activates when message count reaches the specified value
|
||||
```yaml
|
||||
trigger:
|
||||
type: messages
|
||||
value: 50
|
||||
```
|
||||
|
||||
3. **Fraction-based trigger**: Activates when token usage reaches a percentage of the model's maximum input tokens
|
||||
```yaml
|
||||
trigger:
|
||||
type: fraction
|
||||
value: 0.8 # 80% of max input tokens
|
||||
```
|
||||
|
||||
**Multiple Triggers:**
|
||||
```yaml
|
||||
trigger:
|
||||
- type: tokens
|
||||
value: 4000
|
||||
- type: messages
|
||||
value: 50
|
||||
```
|
||||
|
||||
#### `keep`
|
||||
- **Type**: `ContextSize` object
|
||||
- **Default**: `{type: messages, value: 20}`
|
||||
- **Description**: Specifies how much recent conversation history to preserve after summarization.
|
||||
|
||||
**Examples:**
|
||||
```yaml
|
||||
# Keep most recent 20 messages
|
||||
keep:
|
||||
type: messages
|
||||
value: 20
|
||||
|
||||
# Keep most recent 3000 tokens
|
||||
keep:
|
||||
type: tokens
|
||||
value: 3000
|
||||
|
||||
# Keep most recent 30% of model's max input tokens
|
||||
keep:
|
||||
type: fraction
|
||||
value: 0.3
|
||||
```
|
||||
|
||||
#### `trim_tokens_to_summarize`
|
||||
- **Type**: Integer or null
|
||||
- **Default**: `4000`
|
||||
- **Description**: Maximum tokens to include when preparing messages for the summarization call itself. Set to `null` to skip trimming (not recommended for very long conversations).
|
||||
|
||||
#### `summary_prompt`
|
||||
- **Type**: String or null
|
||||
- **Default**: `null` (uses LangChain's default prompt)
|
||||
- **Description**: Custom prompt template for generating summaries. The prompt should guide the model to extract the most important context.
|
||||
|
||||
**Default Prompt Behavior:**
|
||||
The default LangChain prompt instructs the model to:
|
||||
- Extract highest quality/most relevant context
|
||||
- Focus on information critical to the overall goal
|
||||
- Avoid repeating completed actions
|
||||
- Return only the extracted context
|
||||
|
||||
## How It Works
|
||||
|
||||
### Summarization Flow
|
||||
|
||||
1. **Monitoring**: Before each model call, the middleware counts tokens in the message history
|
||||
2. **Trigger Check**: If any configured threshold is met, summarization is triggered
|
||||
3. **Message Partitioning**: Messages are split into:
|
||||
- Messages to summarize (older messages beyond the `keep` threshold)
|
||||
- Messages to preserve (recent messages within the `keep` threshold)
|
||||
4. **Summary Generation**: The model generates a concise summary of the older messages
|
||||
5. **Context Replacement**: The message history is updated:
|
||||
- All old messages are removed
|
||||
- A single summary message is added
|
||||
- Recent messages are preserved
|
||||
6. **AI/Tool Pair Protection**: The system ensures AI messages and their corresponding tool messages stay together
|
||||
|
||||
### Token Counting
|
||||
|
||||
- Uses approximate token counting based on character count
|
||||
- For Anthropic models: ~3.3 characters per token
|
||||
- For other models: Uses LangChain's default estimation
|
||||
- Can be customized with a custom `token_counter` function
|
||||
|
||||
### Message Preservation
|
||||
|
||||
The middleware intelligently preserves message context:
|
||||
|
||||
- **Recent Messages**: Always kept intact based on `keep` configuration
|
||||
- **AI/Tool Pairs**: Never split - if a cutoff point falls within tool messages, the system adjusts to keep the entire AI + Tool message sequence together
|
||||
- **Summary Format**: Summary is injected as a HumanMessage with the format:
|
||||
```
|
||||
Here is a summary of the conversation to date:
|
||||
|
||||
[Generated summary text]
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Choosing Trigger Thresholds
|
||||
|
||||
1. **Token-based triggers**: Recommended for most use cases
|
||||
- Set to 60-80% of your model's context window
|
||||
- Example: For 8K context, use 4000-6000 tokens
|
||||
|
||||
2. **Message-based triggers**: Useful for controlling conversation length
|
||||
- Good for applications with many short messages
|
||||
- Example: 50-100 messages depending on average message length
|
||||
|
||||
3. **Fraction-based triggers**: Ideal when using multiple models
|
||||
- Automatically adapts to each model's capacity
|
||||
- Example: 0.8 (80% of model's max input tokens)
|
||||
|
||||
### Choosing Retention Policy (`keep`)
|
||||
|
||||
1. **Message-based retention**: Best for most scenarios
|
||||
- Preserves natural conversation flow
|
||||
- Recommended: 15-25 messages
|
||||
|
||||
2. **Token-based retention**: Use when precise control is needed
|
||||
- Good for managing exact token budgets
|
||||
- Recommended: 2000-4000 tokens
|
||||
|
||||
3. **Fraction-based retention**: For multi-model setups
|
||||
- Automatically scales with model capacity
|
||||
- Recommended: 0.2-0.4 (20-40% of max input)
|
||||
|
||||
### Model Selection
|
||||
|
||||
- **Recommended**: Use a lightweight, cost-effective model for summaries
|
||||
- Examples: `gpt-4o-mini`, `claude-haiku`, or equivalent
|
||||
- Summaries don't require the most powerful models
|
||||
- Significant cost savings on high-volume applications
|
||||
|
||||
- **Default**: If `model_name` is `null`, uses the default model
|
||||
- May be more expensive but ensures consistency
|
||||
- Good for simple setups
|
||||
|
||||
### Optimization Tips
|
||||
|
||||
1. **Balance triggers**: Combine token and message triggers for robust handling
|
||||
```yaml
|
||||
trigger:
|
||||
- type: tokens
|
||||
value: 4000
|
||||
- type: messages
|
||||
value: 50
|
||||
```
|
||||
|
||||
2. **Conservative retention**: Keep more messages initially, adjust based on performance
|
||||
```yaml
|
||||
keep:
|
||||
type: messages
|
||||
value: 25 # Start higher, reduce if needed
|
||||
```
|
||||
|
||||
3. **Trim strategically**: Limit tokens sent to summarization model
|
||||
```yaml
|
||||
trim_tokens_to_summarize: 4000 # Prevents expensive summarization calls
|
||||
```
|
||||
|
||||
4. **Monitor and iterate**: Track summary quality and adjust configuration
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Summary Quality Issues
|
||||
|
||||
**Problem**: Summaries losing important context
|
||||
|
||||
**Solutions**:
|
||||
1. Increase `keep` value to preserve more messages
|
||||
2. Decrease trigger thresholds to summarize earlier
|
||||
3. Customize `summary_prompt` to emphasize key information
|
||||
4. Use a more capable model for summarization
|
||||
|
||||
### Performance Issues
|
||||
|
||||
**Problem**: Summarization calls taking too long
|
||||
|
||||
**Solutions**:
|
||||
1. Use a faster model for summaries (e.g., `gpt-4o-mini`)
|
||||
2. Reduce `trim_tokens_to_summarize` to send less context
|
||||
3. Increase trigger thresholds to summarize less frequently
|
||||
|
||||
### Token Limit Errors
|
||||
|
||||
**Problem**: Still hitting token limits despite summarization
|
||||
|
||||
**Solutions**:
|
||||
1. Lower trigger thresholds to summarize earlier
|
||||
2. Reduce `keep` value to preserve fewer messages
|
||||
3. Check if individual messages are very large
|
||||
4. Consider using fraction-based triggers
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Code Structure
|
||||
|
||||
- **Configuration**: `packages/harness/deerflow/config/summarization_config.py`
|
||||
- **Integration**: `packages/harness/deerflow/agents/lead_agent/agent.py`
|
||||
- **Middleware**: Uses `langchain.agents.middleware.SummarizationMiddleware`
|
||||
|
||||
### Middleware Order
|
||||
|
||||
Summarization runs after ThreadData and Sandbox initialization but before Title and Clarification:
|
||||
|
||||
1. ThreadDataMiddleware
|
||||
2. SandboxMiddleware
|
||||
3. **SummarizationMiddleware** ← Runs here
|
||||
4. TitleMiddleware
|
||||
5. ClarificationMiddleware
|
||||
|
||||
### State Management
|
||||
|
||||
- Summarization is stateless - configuration is loaded once at startup
|
||||
- Summaries are added as regular messages in the conversation history
|
||||
- The checkpointer persists the summarized history automatically
|
||||
|
||||
## Example Configurations
|
||||
|
||||
### Minimal Configuration
|
||||
```yaml
|
||||
summarization:
|
||||
enabled: true
|
||||
trigger:
|
||||
type: tokens
|
||||
value: 4000
|
||||
keep:
|
||||
type: messages
|
||||
value: 20
|
||||
```
|
||||
|
||||
### Production Configuration
|
||||
```yaml
|
||||
summarization:
|
||||
enabled: true
|
||||
model_name: gpt-4o-mini # Lightweight model for cost efficiency
|
||||
trigger:
|
||||
- type: tokens
|
||||
value: 6000
|
||||
- type: messages
|
||||
value: 75
|
||||
keep:
|
||||
type: messages
|
||||
value: 25
|
||||
trim_tokens_to_summarize: 5000
|
||||
```
|
||||
|
||||
### Multi-Model Configuration
|
||||
```yaml
|
||||
summarization:
|
||||
enabled: true
|
||||
model_name: gpt-4o-mini
|
||||
trigger:
|
||||
type: fraction
|
||||
value: 0.7 # 70% of model's max input
|
||||
keep:
|
||||
type: fraction
|
||||
value: 0.3 # Keep 30% of max input
|
||||
trim_tokens_to_summarize: 4000
|
||||
```
|
||||
|
||||
### Conservative Configuration (High Quality)
|
||||
```yaml
|
||||
summarization:
|
||||
enabled: true
|
||||
model_name: gpt-4 # Use full model for high-quality summaries
|
||||
trigger:
|
||||
type: tokens
|
||||
value: 8000
|
||||
keep:
|
||||
type: messages
|
||||
value: 40 # Keep more context
|
||||
trim_tokens_to_summarize: null # No trimming
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
- [LangChain Summarization Middleware Documentation](https://docs.langchain.com/oss/python/langchain/middleware/built-in#summarization)
|
||||
- [LangChain Source Code](https://github.com/langchain-ai/langchain)
|
||||
174
deer-flow/backend/docs/task_tool_improvements.md
Normal file
174
deer-flow/backend/docs/task_tool_improvements.md
Normal file
@@ -0,0 +1,174 @@
|
||||
# Task Tool Improvements
|
||||
|
||||
## Overview
|
||||
|
||||
The task tool has been improved to eliminate wasteful LLM polling. Previously, when using background tasks, the LLM had to repeatedly call `task_status` to poll for completion, causing unnecessary API requests.
|
||||
|
||||
## Changes Made
|
||||
|
||||
### 1. Removed `run_in_background` Parameter
|
||||
|
||||
The `run_in_background` parameter has been removed from the `task` tool. All subagent tasks now run asynchronously by default, but the tool handles completion automatically.
|
||||
|
||||
**Before:**
|
||||
```python
|
||||
# LLM had to manage polling
|
||||
task_id = task(
|
||||
subagent_type="bash",
|
||||
prompt="Run tests",
|
||||
description="Run tests",
|
||||
run_in_background=True
|
||||
)
|
||||
# Then LLM had to poll repeatedly:
|
||||
while True:
|
||||
status = task_status(task_id)
|
||||
if completed:
|
||||
break
|
||||
```
|
||||
|
||||
**After:**
|
||||
```python
|
||||
# Tool blocks until complete, polling happens in backend
|
||||
result = task(
|
||||
subagent_type="bash",
|
||||
prompt="Run tests",
|
||||
description="Run tests"
|
||||
)
|
||||
# Result is available immediately after the call returns
|
||||
```
|
||||
|
||||
### 2. Backend Polling
|
||||
|
||||
The `task_tool` now:
|
||||
- Starts the subagent task asynchronously
|
||||
- Polls for completion in the backend (every 2 seconds)
|
||||
- Blocks the tool call until completion
|
||||
- Returns the final result directly
|
||||
|
||||
This means:
|
||||
- ✅ LLM makes only ONE tool call
|
||||
- ✅ No wasteful LLM polling requests
|
||||
- ✅ Backend handles all status checking
|
||||
- ✅ Timeout protection (5 minutes max)
|
||||
|
||||
### 3. Removed `task_status` from LLM Tools
|
||||
|
||||
The `task_status_tool` is no longer exposed to the LLM. It's kept in the codebase for potential internal/debugging use, but the LLM cannot call it.
|
||||
|
||||
### 4. Updated Documentation
|
||||
|
||||
- Updated `SUBAGENT_SECTION` in `prompt.py` to remove all references to background tasks and polling
|
||||
- Simplified usage examples
|
||||
- Made it clear that the tool automatically waits for completion
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Polling Logic
|
||||
|
||||
Located in `packages/harness/deerflow/tools/builtins/task_tool.py`:
|
||||
|
||||
```python
|
||||
# Start background execution
|
||||
task_id = executor.execute_async(prompt)
|
||||
|
||||
# Poll for task completion in backend
|
||||
while True:
|
||||
result = get_background_task_result(task_id)
|
||||
|
||||
# Check if task completed or failed
|
||||
if result.status == SubagentStatus.COMPLETED:
|
||||
return f"[Subagent: {subagent_type}]\n\n{result.result}"
|
||||
elif result.status == SubagentStatus.FAILED:
|
||||
return f"[Subagent: {subagent_type}] Task failed: {result.error}"
|
||||
|
||||
# Wait before next poll
|
||||
time.sleep(2)
|
||||
|
||||
# Timeout protection (5 minutes)
|
||||
if poll_count > 150:
|
||||
return "Task timed out after 5 minutes"
|
||||
```
|
||||
|
||||
### Execution Timeout
|
||||
|
||||
In addition to polling timeout, subagent execution now has a built-in timeout mechanism:
|
||||
|
||||
**Configuration** (`packages/harness/deerflow/subagents/config.py`):
|
||||
```python
|
||||
@dataclass
|
||||
class SubagentConfig:
|
||||
# ...
|
||||
timeout_seconds: int = 300 # 5 minutes default
|
||||
```
|
||||
|
||||
**Thread Pool Architecture**:
|
||||
|
||||
To avoid nested thread pools and resource waste, we use two dedicated thread pools:
|
||||
|
||||
1. **Scheduler Pool** (`_scheduler_pool`):
|
||||
- Max workers: 4
|
||||
- Purpose: Orchestrates background task execution
|
||||
- Runs `run_task()` function that manages task lifecycle
|
||||
|
||||
2. **Execution Pool** (`_execution_pool`):
|
||||
- Max workers: 8 (larger to avoid blocking)
|
||||
- Purpose: Actual subagent execution with timeout support
|
||||
- Runs `execute()` method that invokes the agent
|
||||
|
||||
**How it works**:
|
||||
```python
|
||||
# In execute_async():
|
||||
_scheduler_pool.submit(run_task) # Submit orchestration task
|
||||
|
||||
# In run_task():
|
||||
future = _execution_pool.submit(self.execute, task) # Submit execution
|
||||
exec_result = future.result(timeout=timeout_seconds) # Wait with timeout
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
- ✅ Clean separation of concerns (scheduling vs execution)
|
||||
- ✅ No nested thread pools
|
||||
- ✅ Timeout enforcement at the right level
|
||||
- ✅ Better resource utilization
|
||||
|
||||
**Two-Level Timeout Protection**:
|
||||
1. **Execution Timeout**: Subagent execution itself has a 5-minute timeout (configurable in SubagentConfig)
|
||||
2. **Polling Timeout**: Tool polling has a 5-minute timeout (30 polls × 10 seconds)
|
||||
|
||||
This ensures that even if subagent execution hangs, the system won't wait indefinitely.
|
||||
|
||||
### Benefits
|
||||
|
||||
1. **Reduced API Costs**: No more repeated LLM requests for polling
|
||||
2. **Simpler UX**: LLM doesn't need to manage polling logic
|
||||
3. **Better Reliability**: Backend handles all status checking consistently
|
||||
4. **Timeout Protection**: Two-level timeout prevents infinite waiting (execution + polling)
|
||||
|
||||
## Testing
|
||||
|
||||
To verify the changes work correctly:
|
||||
|
||||
1. Start a subagent task that takes a few seconds
|
||||
2. Verify the tool call blocks until completion
|
||||
3. Verify the result is returned directly
|
||||
4. Verify no `task_status` calls are made
|
||||
|
||||
Example test scenario:
|
||||
```python
|
||||
# This should block for ~10 seconds then return result
|
||||
result = task(
|
||||
subagent_type="bash",
|
||||
prompt="sleep 10 && echo 'Done'",
|
||||
description="Test task"
|
||||
)
|
||||
# result should contain "Done"
|
||||
```
|
||||
|
||||
## Migration Notes
|
||||
|
||||
For users/code that previously used `run_in_background=True`:
|
||||
- Simply remove the parameter
|
||||
- Remove any polling logic
|
||||
- The tool will automatically wait for completion
|
||||
|
||||
No other changes needed - the API is backward compatible (minus the removed parameter).
|
||||
14
deer-flow/backend/langgraph.json
Normal file
14
deer-flow/backend/langgraph.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"$schema": "https://langgra.ph/schema.json",
|
||||
"python_version": "3.12",
|
||||
"dependencies": [
|
||||
"."
|
||||
],
|
||||
"env": ".env",
|
||||
"graphs": {
|
||||
"lead_agent": "deerflow.agents:make_lead_agent"
|
||||
},
|
||||
"checkpointer": {
|
||||
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
from .checkpointer import get_checkpointer, make_checkpointer, reset_checkpointer
|
||||
from .factory import create_deerflow_agent
|
||||
from .features import Next, Prev, RuntimeFeatures
|
||||
from .lead_agent import make_lead_agent
|
||||
from .lead_agent.prompt import prime_enabled_skills_cache
|
||||
from .thread_state import SandboxState, ThreadState
|
||||
|
||||
# LangGraph imports deerflow.agents when registering the graph. Prime the
|
||||
# enabled-skills cache here so the request path can usually read a warm cache
|
||||
# without forcing synchronous filesystem work during prompt module import.
|
||||
prime_enabled_skills_cache()
|
||||
|
||||
__all__ = [
|
||||
"create_deerflow_agent",
|
||||
"RuntimeFeatures",
|
||||
"Next",
|
||||
"Prev",
|
||||
"make_lead_agent",
|
||||
"SandboxState",
|
||||
"ThreadState",
|
||||
"get_checkpointer",
|
||||
"reset_checkpointer",
|
||||
"make_checkpointer",
|
||||
]
|
||||
@@ -0,0 +1,9 @@
|
||||
from .async_provider import make_checkpointer
|
||||
from .provider import checkpointer_context, get_checkpointer, reset_checkpointer
|
||||
|
||||
__all__ = [
|
||||
"get_checkpointer",
|
||||
"reset_checkpointer",
|
||||
"checkpointer_context",
|
||||
"make_checkpointer",
|
||||
]
|
||||
@@ -0,0 +1,106 @@
|
||||
"""Async checkpointer factory.
|
||||
|
||||
Provides an **async context manager** for long-running async servers that need
|
||||
proper resource cleanup.
|
||||
|
||||
Supported backends: memory, sqlite, postgres.
|
||||
|
||||
Usage (e.g. FastAPI lifespan)::
|
||||
|
||||
from deerflow.agents.checkpointer.async_provider import make_checkpointer
|
||||
|
||||
async with make_checkpointer() as checkpointer:
|
||||
app.state.checkpointer = checkpointer # InMemorySaver if not configured
|
||||
|
||||
For sync usage see :mod:`deerflow.agents.checkpointer.provider`.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import contextlib
|
||||
import logging
|
||||
from collections.abc import AsyncIterator
|
||||
|
||||
from langgraph.types import Checkpointer
|
||||
|
||||
from deerflow.agents.checkpointer.provider import (
|
||||
POSTGRES_CONN_REQUIRED,
|
||||
POSTGRES_INSTALL,
|
||||
SQLITE_INSTALL,
|
||||
)
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Async factory
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def _async_checkpointer(config) -> AsyncIterator[Checkpointer]:
|
||||
"""Async context manager that constructs and tears down a checkpointer."""
|
||||
if config.type == "memory":
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
yield InMemorySaver()
|
||||
return
|
||||
|
||||
if config.type == "sqlite":
|
||||
try:
|
||||
from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
|
||||
except ImportError as exc:
|
||||
raise ImportError(SQLITE_INSTALL) from exc
|
||||
|
||||
conn_str = resolve_sqlite_conn_str(config.connection_string or "store.db")
|
||||
await asyncio.to_thread(ensure_sqlite_parent_dir, conn_str)
|
||||
async with AsyncSqliteSaver.from_conn_string(conn_str) as saver:
|
||||
await saver.setup()
|
||||
yield saver
|
||||
return
|
||||
|
||||
if config.type == "postgres":
|
||||
try:
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
except ImportError as exc:
|
||||
raise ImportError(POSTGRES_INSTALL) from exc
|
||||
|
||||
if not config.connection_string:
|
||||
raise ValueError(POSTGRES_CONN_REQUIRED)
|
||||
|
||||
async with AsyncPostgresSaver.from_conn_string(config.connection_string) as saver:
|
||||
await saver.setup()
|
||||
yield saver
|
||||
return
|
||||
|
||||
raise ValueError(f"Unknown checkpointer type: {config.type!r}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public async context manager
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def make_checkpointer() -> AsyncIterator[Checkpointer]:
|
||||
"""Async context manager that yields a checkpointer for the caller's lifetime.
|
||||
Resources are opened on enter and closed on exit — no global state::
|
||||
|
||||
async with make_checkpointer() as checkpointer:
|
||||
app.state.checkpointer = checkpointer
|
||||
|
||||
Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
|
||||
"""
|
||||
|
||||
config = get_app_config()
|
||||
|
||||
if config.checkpointer is None:
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
yield InMemorySaver()
|
||||
return
|
||||
|
||||
async with _async_checkpointer(config.checkpointer) as saver:
|
||||
yield saver
|
||||
@@ -0,0 +1,191 @@
|
||||
"""Sync checkpointer factory.
|
||||
|
||||
Provides a **sync singleton** and a **sync context manager** for LangGraph
|
||||
graph compilation and CLI tools.
|
||||
|
||||
Supported backends: memory, sqlite, postgres.
|
||||
|
||||
Usage::
|
||||
|
||||
from deerflow.agents.checkpointer.provider import get_checkpointer, checkpointer_context
|
||||
|
||||
# Singleton — reused across calls, closed on process exit
|
||||
cp = get_checkpointer()
|
||||
|
||||
# One-shot — fresh connection, closed on block exit
|
||||
with checkpointer_context() as cp:
|
||||
graph.invoke(input, config={"configurable": {"thread_id": "1"}})
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import logging
|
||||
from collections.abc import Iterator
|
||||
|
||||
from langgraph.types import Checkpointer
|
||||
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.config.checkpointer_config import CheckpointerConfig
|
||||
from deerflow.runtime.store._sqlite_utils import resolve_sqlite_conn_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Error message constants — imported by aio.provider too
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
SQLITE_INSTALL = "langgraph-checkpoint-sqlite is required for the SQLite checkpointer. Install it with: uv add langgraph-checkpoint-sqlite"
|
||||
POSTGRES_INSTALL = "langgraph-checkpoint-postgres is required for the PostgreSQL checkpointer. Install it with: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool"
|
||||
POSTGRES_CONN_REQUIRED = "checkpointer.connection_string is required for the postgres backend"
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sync factory
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@contextlib.contextmanager
|
||||
def _sync_checkpointer_cm(config: CheckpointerConfig) -> Iterator[Checkpointer]:
|
||||
"""Context manager that creates and tears down a sync checkpointer.
|
||||
|
||||
Returns a configured ``Checkpointer`` instance. Resource cleanup for any
|
||||
underlying connections or pools is handled by higher-level helpers in
|
||||
this module (such as the singleton factory or context manager); this
|
||||
function does not return a separate cleanup callback.
|
||||
"""
|
||||
if config.type == "memory":
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
|
||||
yield InMemorySaver()
|
||||
return
|
||||
|
||||
if config.type == "sqlite":
|
||||
try:
|
||||
from langgraph.checkpoint.sqlite import SqliteSaver
|
||||
except ImportError as exc:
|
||||
raise ImportError(SQLITE_INSTALL) from exc
|
||||
|
||||
conn_str = resolve_sqlite_conn_str(config.connection_string or "store.db")
|
||||
with SqliteSaver.from_conn_string(conn_str) as saver:
|
||||
saver.setup()
|
||||
logger.info("Checkpointer: using SqliteSaver (%s)", conn_str)
|
||||
yield saver
|
||||
return
|
||||
|
||||
if config.type == "postgres":
|
||||
try:
|
||||
from langgraph.checkpoint.postgres import PostgresSaver
|
||||
except ImportError as exc:
|
||||
raise ImportError(POSTGRES_INSTALL) from exc
|
||||
|
||||
if not config.connection_string:
|
||||
raise ValueError(POSTGRES_CONN_REQUIRED)
|
||||
|
||||
with PostgresSaver.from_conn_string(config.connection_string) as saver:
|
||||
saver.setup()
|
||||
logger.info("Checkpointer: using PostgresSaver")
|
||||
yield saver
|
||||
return
|
||||
|
||||
raise ValueError(f"Unknown checkpointer type: {config.type!r}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sync singleton
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_checkpointer: Checkpointer | None = None
|
||||
_checkpointer_ctx = None # open context manager keeping the connection alive
|
||||
|
||||
|
||||
def get_checkpointer() -> Checkpointer:
|
||||
"""Return the global sync checkpointer singleton, creating it on first call.
|
||||
|
||||
Returns an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
|
||||
|
||||
Raises:
|
||||
ImportError: If the required package for the configured backend is not installed.
|
||||
ValueError: If ``connection_string`` is missing for a backend that requires it.
|
||||
"""
|
||||
global _checkpointer, _checkpointer_ctx
|
||||
|
||||
if _checkpointer is not None:
|
||||
return _checkpointer
|
||||
|
||||
# Ensure app config is loaded before checking checkpointer config
|
||||
# This prevents returning InMemorySaver when config.yaml actually has a checkpointer section
|
||||
# but hasn't been loaded yet
|
||||
from deerflow.config.app_config import _app_config
|
||||
from deerflow.config.checkpointer_config import get_checkpointer_config
|
||||
|
||||
config = get_checkpointer_config()
|
||||
|
||||
if config is None and _app_config is None:
|
||||
# Only load app config lazily when neither the app config nor an explicit
|
||||
# checkpointer config has been initialized yet. This keeps tests that
|
||||
# intentionally set the global checkpointer config isolated from any
|
||||
# ambient config.yaml on disk.
|
||||
try:
|
||||
get_app_config()
|
||||
except FileNotFoundError:
|
||||
# In test environments without config.yaml, this is expected.
|
||||
pass
|
||||
config = get_checkpointer_config()
|
||||
if config is None:
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
|
||||
_checkpointer = InMemorySaver()
|
||||
return _checkpointer
|
||||
|
||||
_checkpointer_ctx = _sync_checkpointer_cm(config)
|
||||
_checkpointer = _checkpointer_ctx.__enter__()
|
||||
|
||||
return _checkpointer
|
||||
|
||||
|
||||
def reset_checkpointer() -> None:
|
||||
"""Reset the sync singleton, forcing recreation on the next call.
|
||||
|
||||
Closes any open backend connections and clears the cached instance.
|
||||
Useful in tests or after a configuration change.
|
||||
"""
|
||||
global _checkpointer, _checkpointer_ctx
|
||||
if _checkpointer_ctx is not None:
|
||||
try:
|
||||
_checkpointer_ctx.__exit__(None, None, None)
|
||||
except Exception:
|
||||
logger.warning("Error during checkpointer cleanup", exc_info=True)
|
||||
_checkpointer_ctx = None
|
||||
_checkpointer = None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sync context manager
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@contextlib.contextmanager
|
||||
def checkpointer_context() -> Iterator[Checkpointer]:
|
||||
"""Sync context manager that yields a checkpointer and cleans up on exit.
|
||||
|
||||
Unlike :func:`get_checkpointer`, this does **not** cache the instance —
|
||||
each ``with`` block creates and destroys its own connection. Use it in
|
||||
CLI scripts or tests where you want deterministic cleanup::
|
||||
|
||||
with checkpointer_context() as cp:
|
||||
graph.invoke(input, config={"configurable": {"thread_id": "1"}})
|
||||
|
||||
Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
|
||||
"""
|
||||
|
||||
config = get_app_config()
|
||||
if config.checkpointer is None:
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
yield InMemorySaver()
|
||||
return
|
||||
|
||||
with _sync_checkpointer_cm(config.checkpointer) as saver:
|
||||
yield saver
|
||||
372
deer-flow/backend/packages/harness/deerflow/agents/factory.py
Normal file
372
deer-flow/backend/packages/harness/deerflow/agents/factory.py
Normal file
@@ -0,0 +1,372 @@
|
||||
"""Pure-argument factory for DeerFlow agents.
|
||||
|
||||
``create_deerflow_agent`` accepts plain Python arguments — no YAML files, no
|
||||
global singletons. It is the SDK-level entry point sitting between the raw
|
||||
``langchain.agents.create_agent`` primitive and the config-driven
|
||||
``make_lead_agent`` application factory.
|
||||
|
||||
Note: the factory assembly itself is config-free, but some injected runtime
|
||||
components (e.g. ``task_tool`` for subagent) may still read global config at
|
||||
invocation time. Full config-free runtime is a Phase 2 goal.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
|
||||
from deerflow.agents.features import RuntimeFeatures
|
||||
from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
|
||||
from deerflow.agents.middlewares.dangling_tool_call_middleware import DanglingToolCallMiddleware
|
||||
from deerflow.agents.middlewares.tool_error_handling_middleware import ToolErrorHandlingMiddleware
|
||||
from deerflow.agents.thread_state import ThreadState
|
||||
from deerflow.tools.builtins import ask_clarification_tool
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langchain_core.language_models import BaseChatModel
|
||||
from langchain_core.tools import BaseTool
|
||||
from langgraph.checkpoint.base import BaseCheckpointSaver
|
||||
from langgraph.graph.state import CompiledStateGraph
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# TodoMiddleware prompts (minimal SDK version)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_TODO_SYSTEM_PROMPT = """
|
||||
<todo_list_system>
|
||||
You have access to the `write_todos` tool to help you manage and track complex multi-step objectives.
|
||||
|
||||
**CRITICAL RULES:**
|
||||
- Mark todos as completed IMMEDIATELY after finishing each step - do NOT batch completions
|
||||
- Keep EXACTLY ONE task as `in_progress` at any time (unless tasks can run in parallel)
|
||||
- Update the todo list in REAL-TIME as you work - this gives users visibility into your progress
|
||||
- DO NOT use this tool for simple tasks (< 3 steps) - just complete them directly
|
||||
</todo_list_system>
|
||||
"""
|
||||
|
||||
_TODO_TOOL_DESCRIPTION = "Use this tool to create and manage a structured task list for complex work sessions. Only use for complex tasks (3+ steps)."
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def create_deerflow_agent(
|
||||
model: BaseChatModel,
|
||||
tools: list[BaseTool] | None = None,
|
||||
*,
|
||||
system_prompt: str | None = None,
|
||||
middleware: list[AgentMiddleware] | None = None,
|
||||
features: RuntimeFeatures | None = None,
|
||||
extra_middleware: list[AgentMiddleware] | None = None,
|
||||
plan_mode: bool = False,
|
||||
state_schema: type | None = None,
|
||||
checkpointer: BaseCheckpointSaver | None = None,
|
||||
name: str = "default",
|
||||
) -> CompiledStateGraph:
|
||||
"""Create a DeerFlow agent from plain Python arguments.
|
||||
|
||||
The factory assembly itself reads no config files. Some injected runtime
|
||||
components (e.g. ``task_tool``) may still depend on global config at
|
||||
invocation time — see Phase 2 roadmap for full config-free runtime.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model:
|
||||
Chat model instance.
|
||||
tools:
|
||||
User-provided tools. Feature-injected tools are appended automatically.
|
||||
system_prompt:
|
||||
System message. ``None`` uses a minimal default.
|
||||
middleware:
|
||||
**Full takeover** — if provided, this exact list is used.
|
||||
Cannot be combined with *features* or *extra_middleware*.
|
||||
features:
|
||||
Declarative feature flags. Cannot be combined with *middleware*.
|
||||
extra_middleware:
|
||||
Additional middlewares inserted into the auto-assembled chain via
|
||||
``@Next``/``@Prev`` positioning. Cannot be used with *middleware*.
|
||||
plan_mode:
|
||||
Enable TodoMiddleware for task tracking.
|
||||
state_schema:
|
||||
LangGraph state type. Defaults to ``ThreadState``.
|
||||
checkpointer:
|
||||
Optional persistence backend.
|
||||
name:
|
||||
Agent name (passed to middleware that cares, e.g. ``MemoryMiddleware``).
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If both *middleware* and *features*/*extra_middleware* are provided.
|
||||
"""
|
||||
if middleware is not None and features is not None:
|
||||
raise ValueError("Cannot specify both 'middleware' and 'features'. Use one or the other.")
|
||||
if middleware is not None and extra_middleware:
|
||||
raise ValueError("Cannot use 'extra_middleware' with 'middleware' (full takeover).")
|
||||
if extra_middleware:
|
||||
for mw in extra_middleware:
|
||||
if not isinstance(mw, AgentMiddleware):
|
||||
raise TypeError(f"extra_middleware items must be AgentMiddleware instances, got {type(mw).__name__}")
|
||||
|
||||
effective_tools: list[BaseTool] = list(tools or [])
|
||||
effective_state = state_schema or ThreadState
|
||||
|
||||
if middleware is not None:
|
||||
effective_middleware = list(middleware)
|
||||
else:
|
||||
feat = features or RuntimeFeatures()
|
||||
effective_middleware, extra_tools = _assemble_from_features(
|
||||
feat,
|
||||
name=name,
|
||||
plan_mode=plan_mode,
|
||||
extra_middleware=extra_middleware or [],
|
||||
)
|
||||
# Deduplicate by tool name — user-provided tools take priority.
|
||||
existing_names = {t.name for t in effective_tools}
|
||||
for t in extra_tools:
|
||||
if t.name not in existing_names:
|
||||
effective_tools.append(t)
|
||||
existing_names.add(t.name)
|
||||
|
||||
return create_agent(
|
||||
model=model,
|
||||
tools=effective_tools or None,
|
||||
middleware=effective_middleware,
|
||||
system_prompt=system_prompt,
|
||||
state_schema=effective_state,
|
||||
checkpointer=checkpointer,
|
||||
name=name,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Internal: feature-driven middleware assembly
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _assemble_from_features(
|
||||
feat: RuntimeFeatures,
|
||||
*,
|
||||
name: str = "default",
|
||||
plan_mode: bool = False,
|
||||
extra_middleware: list[AgentMiddleware] | None = None,
|
||||
) -> tuple[list[AgentMiddleware], list[BaseTool]]:
|
||||
"""Build an ordered middleware chain + extra tools from *feat*.
|
||||
|
||||
Middleware order matches ``make_lead_agent`` (14 middlewares):
|
||||
|
||||
0-2. Sandbox infrastructure (ThreadData → Uploads → Sandbox)
|
||||
3. DanglingToolCallMiddleware (always)
|
||||
4. GuardrailMiddleware (guardrail feature)
|
||||
5. ToolErrorHandlingMiddleware (always)
|
||||
6. SummarizationMiddleware (summarization feature)
|
||||
7. TodoMiddleware (plan_mode parameter)
|
||||
8. TitleMiddleware (auto_title feature)
|
||||
9. MemoryMiddleware (memory feature)
|
||||
10. ViewImageMiddleware (vision feature)
|
||||
11. SubagentLimitMiddleware (subagent feature)
|
||||
12. LoopDetectionMiddleware (always)
|
||||
13. ClarificationMiddleware (always last)
|
||||
|
||||
Two-phase ordering:
|
||||
1. Built-in chain — fixed sequential append.
|
||||
2. Extra middleware — inserted via @Next/@Prev.
|
||||
|
||||
Each feature value is handled as:
|
||||
- ``False``: skip
|
||||
- ``True``: create the built-in default middleware (not available for
|
||||
``summarization`` and ``guardrail`` — these require a custom instance)
|
||||
- ``AgentMiddleware`` instance: use directly (custom replacement)
|
||||
"""
|
||||
chain: list[AgentMiddleware] = []
|
||||
extra_tools: list[BaseTool] = []
|
||||
|
||||
# --- [0-2] Sandbox infrastructure ---
|
||||
if feat.sandbox is not False:
|
||||
if isinstance(feat.sandbox, AgentMiddleware):
|
||||
chain.append(feat.sandbox)
|
||||
else:
|
||||
from deerflow.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
|
||||
from deerflow.agents.middlewares.uploads_middleware import UploadsMiddleware
|
||||
from deerflow.sandbox.middleware import SandboxMiddleware
|
||||
|
||||
chain.append(ThreadDataMiddleware(lazy_init=True))
|
||||
chain.append(UploadsMiddleware())
|
||||
chain.append(SandboxMiddleware(lazy_init=True))
|
||||
|
||||
# --- [3] DanglingToolCall (always) ---
|
||||
chain.append(DanglingToolCallMiddleware())
|
||||
|
||||
# --- [4] Guardrail ---
|
||||
if feat.guardrail is not False:
|
||||
if isinstance(feat.guardrail, AgentMiddleware):
|
||||
chain.append(feat.guardrail)
|
||||
else:
|
||||
raise ValueError("guardrail=True requires a custom AgentMiddleware instance (no built-in GuardrailMiddleware yet)")
|
||||
|
||||
# --- [5] ToolErrorHandling (always) ---
|
||||
chain.append(ToolErrorHandlingMiddleware())
|
||||
|
||||
# --- [6] Summarization ---
|
||||
if feat.summarization is not False:
|
||||
if isinstance(feat.summarization, AgentMiddleware):
|
||||
chain.append(feat.summarization)
|
||||
else:
|
||||
raise ValueError("summarization=True requires a custom AgentMiddleware instance (SummarizationMiddleware needs a model argument)")
|
||||
|
||||
# --- [7] TodoMiddleware (plan_mode) ---
|
||||
if plan_mode:
|
||||
from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
|
||||
|
||||
chain.append(TodoMiddleware(system_prompt=_TODO_SYSTEM_PROMPT, tool_description=_TODO_TOOL_DESCRIPTION))
|
||||
|
||||
# --- [8] Auto Title ---
|
||||
if feat.auto_title is not False:
|
||||
if isinstance(feat.auto_title, AgentMiddleware):
|
||||
chain.append(feat.auto_title)
|
||||
else:
|
||||
from deerflow.agents.middlewares.title_middleware import TitleMiddleware
|
||||
|
||||
chain.append(TitleMiddleware())
|
||||
|
||||
# --- [9] Memory ---
|
||||
if feat.memory is not False:
|
||||
if isinstance(feat.memory, AgentMiddleware):
|
||||
chain.append(feat.memory)
|
||||
else:
|
||||
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
|
||||
|
||||
chain.append(MemoryMiddleware(agent_name=name))
|
||||
|
||||
# --- [10] Vision ---
|
||||
if feat.vision is not False:
|
||||
if isinstance(feat.vision, AgentMiddleware):
|
||||
chain.append(feat.vision)
|
||||
else:
|
||||
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
|
||||
|
||||
chain.append(ViewImageMiddleware())
|
||||
from deerflow.tools.builtins import view_image_tool
|
||||
|
||||
extra_tools.append(view_image_tool)
|
||||
|
||||
# --- [11] Subagent ---
|
||||
if feat.subagent is not False:
|
||||
if isinstance(feat.subagent, AgentMiddleware):
|
||||
chain.append(feat.subagent)
|
||||
else:
|
||||
from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
|
||||
|
||||
chain.append(SubagentLimitMiddleware())
|
||||
from deerflow.tools.builtins import task_tool
|
||||
|
||||
extra_tools.append(task_tool)
|
||||
|
||||
# --- [12] LoopDetection (always) ---
|
||||
from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
|
||||
|
||||
chain.append(LoopDetectionMiddleware())
|
||||
|
||||
# --- [13] Clarification (always last among built-ins) ---
|
||||
chain.append(ClarificationMiddleware())
|
||||
extra_tools.append(ask_clarification_tool)
|
||||
|
||||
# --- Insert extra_middleware via @Next/@Prev ---
|
||||
if extra_middleware:
|
||||
_insert_extra(chain, extra_middleware)
|
||||
# Invariant: ClarificationMiddleware must always be last.
|
||||
# @Next(ClarificationMiddleware) could push it off the tail.
|
||||
clar_idx = next(i for i, m in enumerate(chain) if isinstance(m, ClarificationMiddleware))
|
||||
if clar_idx != len(chain) - 1:
|
||||
chain.append(chain.pop(clar_idx))
|
||||
|
||||
return chain, extra_tools
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Internal: extra middleware insertion with @Next/@Prev
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _insert_extra(chain: list[AgentMiddleware], extras: list[AgentMiddleware]) -> None:
|
||||
"""Insert extra middlewares into *chain* using ``@Next``/``@Prev`` anchors.
|
||||
|
||||
Algorithm:
|
||||
1. Validate: no middleware has both @Next and @Prev.
|
||||
2. Conflict detection: two extras targeting same anchor (same or opposite direction) → error.
|
||||
3. Insert unanchored extras before ClarificationMiddleware.
|
||||
4. Insert anchored extras iteratively (supports cross-external anchoring).
|
||||
5. If an anchor cannot be resolved after all rounds → error.
|
||||
"""
|
||||
next_targets: dict[type, type] = {}
|
||||
prev_targets: dict[type, type] = {}
|
||||
|
||||
anchored: list[tuple[AgentMiddleware, str, type]] = []
|
||||
unanchored: list[AgentMiddleware] = []
|
||||
|
||||
for mw in extras:
|
||||
next_anchor = getattr(type(mw), "_next_anchor", None)
|
||||
prev_anchor = getattr(type(mw), "_prev_anchor", None)
|
||||
|
||||
if next_anchor and prev_anchor:
|
||||
raise ValueError(f"{type(mw).__name__} cannot have both @Next and @Prev")
|
||||
|
||||
if next_anchor:
|
||||
if next_anchor in next_targets:
|
||||
raise ValueError(f"Conflict: {type(mw).__name__} and {next_targets[next_anchor].__name__} both @Next({next_anchor.__name__})")
|
||||
if next_anchor in prev_targets:
|
||||
raise ValueError(f"Conflict: {type(mw).__name__} @Next({next_anchor.__name__}) and {prev_targets[next_anchor].__name__} @Prev({next_anchor.__name__}) — use cross-anchoring between extras instead")
|
||||
next_targets[next_anchor] = type(mw)
|
||||
anchored.append((mw, "next", next_anchor))
|
||||
elif prev_anchor:
|
||||
if prev_anchor in prev_targets:
|
||||
raise ValueError(f"Conflict: {type(mw).__name__} and {prev_targets[prev_anchor].__name__} both @Prev({prev_anchor.__name__})")
|
||||
if prev_anchor in next_targets:
|
||||
raise ValueError(f"Conflict: {type(mw).__name__} @Prev({prev_anchor.__name__}) and {next_targets[prev_anchor].__name__} @Next({prev_anchor.__name__}) — use cross-anchoring between extras instead")
|
||||
prev_targets[prev_anchor] = type(mw)
|
||||
anchored.append((mw, "prev", prev_anchor))
|
||||
else:
|
||||
unanchored.append(mw)
|
||||
|
||||
# Unanchored → before ClarificationMiddleware
|
||||
clarification_idx = next(i for i, m in enumerate(chain) if isinstance(m, ClarificationMiddleware))
|
||||
for mw in unanchored:
|
||||
chain.insert(clarification_idx, mw)
|
||||
clarification_idx += 1
|
||||
|
||||
# Anchored → iterative insertion (supports external-to-external anchoring)
|
||||
pending = list(anchored)
|
||||
max_rounds = len(pending) + 1
|
||||
for _ in range(max_rounds):
|
||||
if not pending:
|
||||
break
|
||||
remaining = []
|
||||
for mw, direction, anchor in pending:
|
||||
idx = next(
|
||||
(i for i, m in enumerate(chain) if isinstance(m, anchor)),
|
||||
None,
|
||||
)
|
||||
if idx is None:
|
||||
remaining.append((mw, direction, anchor))
|
||||
continue
|
||||
if direction == "next":
|
||||
chain.insert(idx + 1, mw)
|
||||
else:
|
||||
chain.insert(idx, mw)
|
||||
if len(remaining) == len(pending):
|
||||
names = [type(m).__name__ for m, _, _ in remaining]
|
||||
anchor_types = {a for _, _, a in remaining}
|
||||
remaining_types = {type(m) for m, _, _ in remaining}
|
||||
circular = anchor_types & remaining_types
|
||||
if circular:
|
||||
raise ValueError(f"Circular dependency among extra middlewares: {', '.join(t.__name__ for t in circular)}")
|
||||
raise ValueError(f"Cannot resolve positions for {', '.join(names)} — anchors {', '.join(a.__name__ for _, _, a in remaining)} not found in chain")
|
||||
pending = remaining
|
||||
@@ -0,0 +1,62 @@
|
||||
"""Declarative feature flags and middleware positioning for create_deerflow_agent.
|
||||
|
||||
Pure data classes and decorators — no I/O, no side effects.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Literal
|
||||
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
|
||||
|
||||
@dataclass
|
||||
class RuntimeFeatures:
|
||||
"""Declarative feature flags for ``create_deerflow_agent``.
|
||||
|
||||
Most features accept:
|
||||
- ``True``: use the built-in default middleware
|
||||
- ``False``: disable
|
||||
- An ``AgentMiddleware`` instance: use this custom implementation instead
|
||||
|
||||
``summarization`` and ``guardrail`` have no built-in default — they only
|
||||
accept ``False`` (disable) or an ``AgentMiddleware`` instance (custom).
|
||||
"""
|
||||
|
||||
sandbox: bool | AgentMiddleware = True
|
||||
memory: bool | AgentMiddleware = False
|
||||
summarization: Literal[False] | AgentMiddleware = False
|
||||
subagent: bool | AgentMiddleware = False
|
||||
vision: bool | AgentMiddleware = False
|
||||
auto_title: bool | AgentMiddleware = False
|
||||
guardrail: Literal[False] | AgentMiddleware = False
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Middleware positioning decorators
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def Next(anchor: type[AgentMiddleware]):
|
||||
"""Declare this middleware should be placed after *anchor* in the chain."""
|
||||
if not (isinstance(anchor, type) and issubclass(anchor, AgentMiddleware)):
|
||||
raise TypeError(f"@Next expects an AgentMiddleware subclass, got {anchor!r}")
|
||||
|
||||
def decorator(cls: type[AgentMiddleware]) -> type[AgentMiddleware]:
|
||||
cls._next_anchor = anchor # type: ignore[attr-defined]
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def Prev(anchor: type[AgentMiddleware]):
|
||||
"""Declare this middleware should be placed before *anchor* in the chain."""
|
||||
if not (isinstance(anchor, type) and issubclass(anchor, AgentMiddleware)):
|
||||
raise TypeError(f"@Prev expects an AgentMiddleware subclass, got {anchor!r}")
|
||||
|
||||
def decorator(cls: type[AgentMiddleware]) -> type[AgentMiddleware]:
|
||||
cls._prev_anchor = anchor # type: ignore[attr-defined]
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
@@ -0,0 +1,3 @@
|
||||
from .agent import make_lead_agent
|
||||
|
||||
__all__ = ["make_lead_agent"]
|
||||
@@ -0,0 +1,350 @@
|
||||
import logging
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langchain.agents.middleware import AgentMiddleware, SummarizationMiddleware
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
|
||||
from deerflow.agents.lead_agent.prompt import apply_prompt_template
|
||||
from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
|
||||
from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
|
||||
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
|
||||
from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
|
||||
from deerflow.agents.middlewares.title_middleware import TitleMiddleware
|
||||
from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
|
||||
from deerflow.agents.middlewares.token_usage_middleware import TokenUsageMiddleware
|
||||
from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares
|
||||
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
|
||||
from deerflow.agents.thread_state import ThreadState
|
||||
from deerflow.config.agents_config import load_agent_config
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.config.summarization_config import get_summarization_config
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _resolve_model_name(requested_model_name: str | None = None) -> str:
|
||||
"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
|
||||
app_config = get_app_config()
|
||||
default_model_name = app_config.models[0].name if app_config.models else None
|
||||
if default_model_name is None:
|
||||
raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
|
||||
|
||||
if requested_model_name and app_config.get_model_config(requested_model_name):
|
||||
return requested_model_name
|
||||
|
||||
if requested_model_name and requested_model_name != default_model_name:
|
||||
logger.warning(f"Model '{requested_model_name}' not found in config; fallback to default model '{default_model_name}'.")
|
||||
return default_model_name
|
||||
|
||||
|
||||
def _create_summarization_middleware() -> SummarizationMiddleware | None:
|
||||
"""Create and configure the summarization middleware from config."""
|
||||
config = get_summarization_config()
|
||||
|
||||
if not config.enabled:
|
||||
return None
|
||||
|
||||
# Prepare trigger parameter
|
||||
trigger = None
|
||||
if config.trigger is not None:
|
||||
if isinstance(config.trigger, list):
|
||||
trigger = [t.to_tuple() for t in config.trigger]
|
||||
else:
|
||||
trigger = config.trigger.to_tuple()
|
||||
|
||||
# Prepare keep parameter
|
||||
keep = config.keep.to_tuple()
|
||||
|
||||
# Prepare model parameter
|
||||
if config.model_name:
|
||||
model = create_chat_model(name=config.model_name, thinking_enabled=False)
|
||||
else:
|
||||
# Use a lightweight model for summarization to save costs
|
||||
# Falls back to default model if not explicitly specified
|
||||
model = create_chat_model(thinking_enabled=False)
|
||||
|
||||
# Prepare kwargs
|
||||
kwargs = {
|
||||
"model": model,
|
||||
"trigger": trigger,
|
||||
"keep": keep,
|
||||
}
|
||||
|
||||
if config.trim_tokens_to_summarize is not None:
|
||||
kwargs["trim_tokens_to_summarize"] = config.trim_tokens_to_summarize
|
||||
|
||||
if config.summary_prompt is not None:
|
||||
kwargs["summary_prompt"] = config.summary_prompt
|
||||
|
||||
return SummarizationMiddleware(**kwargs)
|
||||
|
||||
|
||||
def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
|
||||
"""Create and configure the TodoList middleware.
|
||||
|
||||
Args:
|
||||
is_plan_mode: Whether to enable plan mode with TodoList middleware.
|
||||
|
||||
Returns:
|
||||
TodoMiddleware instance if plan mode is enabled, None otherwise.
|
||||
"""
|
||||
if not is_plan_mode:
|
||||
return None
|
||||
|
||||
# Custom prompts matching DeerFlow's style
|
||||
system_prompt = """
|
||||
<todo_list_system>
|
||||
You have access to the `write_todos` tool to help you manage and track complex multi-step objectives.
|
||||
|
||||
**CRITICAL RULES:**
|
||||
- Mark todos as completed IMMEDIATELY after finishing each step - do NOT batch completions
|
||||
- Keep EXACTLY ONE task as `in_progress` at any time (unless tasks can run in parallel)
|
||||
- Update the todo list in REAL-TIME as you work - this gives users visibility into your progress
|
||||
- DO NOT use this tool for simple tasks (< 3 steps) - just complete them directly
|
||||
|
||||
**When to Use:**
|
||||
This tool is designed for complex objectives that require systematic tracking:
|
||||
- Complex multi-step tasks requiring 3+ distinct steps
|
||||
- Non-trivial tasks needing careful planning and execution
|
||||
- User explicitly requests a todo list
|
||||
- User provides multiple tasks (numbered or comma-separated list)
|
||||
- The plan may need revisions based on intermediate results
|
||||
|
||||
**When NOT to Use:**
|
||||
- Single, straightforward tasks
|
||||
- Trivial tasks (< 3 steps)
|
||||
- Purely conversational or informational requests
|
||||
- Simple tool calls where the approach is obvious
|
||||
|
||||
**Best Practices:**
|
||||
- Break down complex tasks into smaller, actionable steps
|
||||
- Use clear, descriptive task names
|
||||
- Remove tasks that become irrelevant
|
||||
- Add new tasks discovered during implementation
|
||||
- Don't be afraid to revise the todo list as you learn more
|
||||
|
||||
**Task Management:**
|
||||
Writing todos takes time and tokens - use it when helpful for managing complex problems, not for simple requests.
|
||||
</todo_list_system>
|
||||
"""
|
||||
|
||||
tool_description = """Use this tool to create and manage a structured task list for complex work sessions.
|
||||
|
||||
**IMPORTANT: Only use this tool for complex tasks (3+ steps). For simple requests, just do the work directly.**
|
||||
|
||||
## When to Use
|
||||
|
||||
Use this tool in these scenarios:
|
||||
1. **Complex multi-step tasks**: When a task requires 3 or more distinct steps or actions
|
||||
2. **Non-trivial tasks**: Tasks requiring careful planning or multiple operations
|
||||
3. **User explicitly requests todo list**: When the user directly asks you to track tasks
|
||||
4. **Multiple tasks**: When users provide a list of things to be done
|
||||
5. **Dynamic planning**: When the plan may need updates based on intermediate results
|
||||
|
||||
## When NOT to Use
|
||||
|
||||
Skip this tool when:
|
||||
1. The task is straightforward and takes less than 3 steps
|
||||
2. The task is trivial and tracking provides no benefit
|
||||
3. The task is purely conversational or informational
|
||||
4. It's clear what needs to be done and you can just do it
|
||||
|
||||
## How to Use
|
||||
|
||||
1. **Starting a task**: Mark it as `in_progress` BEFORE beginning work
|
||||
2. **Completing a task**: Mark it as `completed` IMMEDIATELY after finishing
|
||||
3. **Updating the list**: Add new tasks, remove irrelevant ones, or update descriptions as needed
|
||||
4. **Multiple updates**: You can make several updates at once (e.g., complete one task and start the next)
|
||||
|
||||
## Task States
|
||||
|
||||
- `pending`: Task not yet started
|
||||
- `in_progress`: Currently working on (can have multiple if tasks run in parallel)
|
||||
- `completed`: Task finished successfully
|
||||
|
||||
## Task Completion Requirements
|
||||
|
||||
**CRITICAL: Only mark a task as completed when you have FULLY accomplished it.**
|
||||
|
||||
Never mark a task as completed if:
|
||||
- There are unresolved issues or errors
|
||||
- Work is partial or incomplete
|
||||
- You encountered blockers preventing completion
|
||||
- You couldn't find necessary resources or dependencies
|
||||
- Quality standards haven't been met
|
||||
|
||||
If blocked, keep the task as `in_progress` and create a new task describing what needs to be resolved.
|
||||
|
||||
## Best Practices
|
||||
|
||||
- Create specific, actionable items
|
||||
- Break complex tasks into smaller, manageable steps
|
||||
- Use clear, descriptive task names
|
||||
- Update task status in real-time as you work
|
||||
- Mark tasks complete IMMEDIATELY after finishing (don't batch completions)
|
||||
- Remove tasks that are no longer relevant
|
||||
- **IMPORTANT**: When you write the todo list, mark your first task(s) as `in_progress` immediately
|
||||
- **IMPORTANT**: Unless all tasks are completed, always have at least one task `in_progress` to show progress
|
||||
|
||||
Being proactive with task management demonstrates thoroughness and ensures all requirements are completed successfully.
|
||||
|
||||
**Remember**: If you only need a few tool calls to complete a task and it's clear what to do, it's better to just do the task directly and NOT use this tool at all.
|
||||
"""
|
||||
|
||||
return TodoMiddleware(system_prompt=system_prompt, tool_description=tool_description)
|
||||
|
||||
|
||||
# ThreadDataMiddleware must be before SandboxMiddleware to ensure thread_id is available
|
||||
# UploadsMiddleware should be after ThreadDataMiddleware to access thread_id
|
||||
# DanglingToolCallMiddleware patches missing ToolMessages before model sees the history
|
||||
# SummarizationMiddleware should be early to reduce context before other processing
|
||||
# TodoListMiddleware should be before ClarificationMiddleware to allow todo management
|
||||
# TitleMiddleware generates title after first exchange
|
||||
# MemoryMiddleware queues conversation for memory update (after TitleMiddleware)
|
||||
# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
|
||||
# ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
|
||||
# ClarificationMiddleware should be last to intercept clarification requests after model calls
|
||||
def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = None):
|
||||
"""Build middleware chain based on runtime configuration.
|
||||
|
||||
Args:
|
||||
config: Runtime configuration containing configurable options like is_plan_mode.
|
||||
agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
|
||||
custom_middlewares: Optional list of custom middlewares to inject into the chain.
|
||||
|
||||
Returns:
|
||||
List of middleware instances.
|
||||
"""
|
||||
middlewares = build_lead_runtime_middlewares(lazy_init=True)
|
||||
|
||||
# Add summarization middleware if enabled
|
||||
summarization_middleware = _create_summarization_middleware()
|
||||
if summarization_middleware is not None:
|
||||
middlewares.append(summarization_middleware)
|
||||
|
||||
# Add TodoList middleware if plan mode is enabled
|
||||
is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
|
||||
todo_list_middleware = _create_todo_list_middleware(is_plan_mode)
|
||||
if todo_list_middleware is not None:
|
||||
middlewares.append(todo_list_middleware)
|
||||
|
||||
# Add TokenUsageMiddleware when token_usage tracking is enabled
|
||||
if get_app_config().token_usage.enabled:
|
||||
middlewares.append(TokenUsageMiddleware())
|
||||
|
||||
# Add TitleMiddleware
|
||||
middlewares.append(TitleMiddleware())
|
||||
|
||||
# Add MemoryMiddleware (after TitleMiddleware)
|
||||
middlewares.append(MemoryMiddleware(agent_name=agent_name))
|
||||
|
||||
# Add ViewImageMiddleware only if the current model supports vision.
|
||||
# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
|
||||
app_config = get_app_config()
|
||||
model_config = app_config.get_model_config(model_name) if model_name else None
|
||||
if model_config is not None and model_config.supports_vision:
|
||||
middlewares.append(ViewImageMiddleware())
|
||||
|
||||
# Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
|
||||
if app_config.tool_search.enabled:
|
||||
from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
|
||||
|
||||
middlewares.append(DeferredToolFilterMiddleware())
|
||||
|
||||
# Add SubagentLimitMiddleware to truncate excess parallel task calls
|
||||
subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
|
||||
if subagent_enabled:
|
||||
max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
|
||||
middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
|
||||
|
||||
# LoopDetectionMiddleware — detect and break repetitive tool call loops
|
||||
middlewares.append(LoopDetectionMiddleware())
|
||||
|
||||
# Inject custom middlewares before ClarificationMiddleware
|
||||
if custom_middlewares:
|
||||
middlewares.extend(custom_middlewares)
|
||||
|
||||
# ClarificationMiddleware should always be last
|
||||
middlewares.append(ClarificationMiddleware())
|
||||
return middlewares
|
||||
|
||||
|
||||
def make_lead_agent(config: RunnableConfig):
|
||||
# Lazy import to avoid circular dependency
|
||||
from deerflow.tools import get_available_tools
|
||||
from deerflow.tools.builtins import setup_agent
|
||||
|
||||
cfg = config.get("configurable", {})
|
||||
|
||||
thinking_enabled = cfg.get("thinking_enabled", True)
|
||||
reasoning_effort = cfg.get("reasoning_effort", None)
|
||||
requested_model_name: str | None = cfg.get("model_name") or cfg.get("model")
|
||||
is_plan_mode = cfg.get("is_plan_mode", False)
|
||||
subagent_enabled = cfg.get("subagent_enabled", False)
|
||||
max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
|
||||
is_bootstrap = cfg.get("is_bootstrap", False)
|
||||
agent_name = cfg.get("agent_name")
|
||||
|
||||
agent_config = load_agent_config(agent_name) if not is_bootstrap else None
|
||||
# Custom agent model from agent config (if any), or None to let _resolve_model_name pick the default
|
||||
agent_model_name = agent_config.model if agent_config and agent_config.model else None
|
||||
|
||||
# Final model name resolution: request → agent config → global default, with fallback for unknown names
|
||||
model_name = _resolve_model_name(requested_model_name or agent_model_name)
|
||||
|
||||
app_config = get_app_config()
|
||||
model_config = app_config.get_model_config(model_name)
|
||||
|
||||
if model_config is None:
|
||||
raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
|
||||
if thinking_enabled and not model_config.supports_thinking:
|
||||
logger.warning(f"Thinking mode is enabled but model '{model_name}' does not support it; fallback to non-thinking mode.")
|
||||
thinking_enabled = False
|
||||
|
||||
logger.info(
|
||||
"Create Agent(%s) -> thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s",
|
||||
agent_name or "default",
|
||||
thinking_enabled,
|
||||
reasoning_effort,
|
||||
model_name,
|
||||
is_plan_mode,
|
||||
subagent_enabled,
|
||||
max_concurrent_subagents,
|
||||
)
|
||||
|
||||
# Inject run metadata for LangSmith trace tagging
|
||||
if "metadata" not in config:
|
||||
config["metadata"] = {}
|
||||
|
||||
config["metadata"].update(
|
||||
{
|
||||
"agent_name": agent_name or "default",
|
||||
"model_name": model_name or "default",
|
||||
"thinking_enabled": thinking_enabled,
|
||||
"reasoning_effort": reasoning_effort,
|
||||
"is_plan_mode": is_plan_mode,
|
||||
"subagent_enabled": subagent_enabled,
|
||||
}
|
||||
)
|
||||
|
||||
if is_bootstrap:
|
||||
# Special bootstrap agent with minimal prompt for initial custom agent creation flow
|
||||
return create_agent(
|
||||
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
|
||||
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
|
||||
middleware=_build_middlewares(config, model_name=model_name),
|
||||
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
|
||||
state_schema=ThreadState,
|
||||
)
|
||||
|
||||
# Default lead agent (unchanged behavior)
|
||||
return create_agent(
|
||||
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
|
||||
tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
|
||||
middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
|
||||
system_prompt=apply_prompt_template(
|
||||
subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
|
||||
),
|
||||
state_schema=ThreadState,
|
||||
)
|
||||
@@ -0,0 +1,727 @@
|
||||
import asyncio
|
||||
import logging
|
||||
import threading
|
||||
from datetime import datetime
|
||||
from functools import lru_cache
|
||||
|
||||
from deerflow.config.agents_config import load_agent_soul
|
||||
from deerflow.skills import load_skills
|
||||
from deerflow.skills.types import Skill
|
||||
from deerflow.subagents import get_available_subagent_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
|
||||
_enabled_skills_lock = threading.Lock()
|
||||
_enabled_skills_cache: list[Skill] | None = None
|
||||
_enabled_skills_refresh_active = False
|
||||
_enabled_skills_refresh_version = 0
|
||||
_enabled_skills_refresh_event = threading.Event()
|
||||
|
||||
|
||||
def _load_enabled_skills_sync() -> list[Skill]:
|
||||
return list(load_skills(enabled_only=True))
|
||||
|
||||
|
||||
def _start_enabled_skills_refresh_thread() -> None:
|
||||
threading.Thread(
|
||||
target=_refresh_enabled_skills_cache_worker,
|
||||
name="deerflow-enabled-skills-loader",
|
||||
daemon=True,
|
||||
).start()
|
||||
|
||||
|
||||
def _refresh_enabled_skills_cache_worker() -> None:
|
||||
global _enabled_skills_cache, _enabled_skills_refresh_active
|
||||
|
||||
while True:
|
||||
with _enabled_skills_lock:
|
||||
target_version = _enabled_skills_refresh_version
|
||||
|
||||
try:
|
||||
skills = _load_enabled_skills_sync()
|
||||
except Exception:
|
||||
logger.exception("Failed to load enabled skills for prompt injection")
|
||||
skills = []
|
||||
|
||||
with _enabled_skills_lock:
|
||||
if _enabled_skills_refresh_version == target_version:
|
||||
_enabled_skills_cache = skills
|
||||
_enabled_skills_refresh_active = False
|
||||
_enabled_skills_refresh_event.set()
|
||||
return
|
||||
|
||||
# A newer invalidation happened while loading. Keep the worker alive
|
||||
# and loop again so the cache always converges on the latest version.
|
||||
_enabled_skills_cache = None
|
||||
|
||||
|
||||
def _ensure_enabled_skills_cache() -> threading.Event:
|
||||
global _enabled_skills_refresh_active
|
||||
|
||||
with _enabled_skills_lock:
|
||||
if _enabled_skills_cache is not None:
|
||||
_enabled_skills_refresh_event.set()
|
||||
return _enabled_skills_refresh_event
|
||||
if _enabled_skills_refresh_active:
|
||||
return _enabled_skills_refresh_event
|
||||
_enabled_skills_refresh_active = True
|
||||
_enabled_skills_refresh_event.clear()
|
||||
|
||||
_start_enabled_skills_refresh_thread()
|
||||
return _enabled_skills_refresh_event
|
||||
|
||||
|
||||
def _invalidate_enabled_skills_cache() -> threading.Event:
|
||||
global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
|
||||
|
||||
_get_cached_skills_prompt_section.cache_clear()
|
||||
with _enabled_skills_lock:
|
||||
_enabled_skills_cache = None
|
||||
_enabled_skills_refresh_version += 1
|
||||
_enabled_skills_refresh_event.clear()
|
||||
if _enabled_skills_refresh_active:
|
||||
return _enabled_skills_refresh_event
|
||||
_enabled_skills_refresh_active = True
|
||||
|
||||
_start_enabled_skills_refresh_thread()
|
||||
return _enabled_skills_refresh_event
|
||||
|
||||
|
||||
def prime_enabled_skills_cache() -> None:
|
||||
_ensure_enabled_skills_cache()
|
||||
|
||||
|
||||
def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
|
||||
if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
|
||||
return True
|
||||
|
||||
logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
|
||||
return False
|
||||
|
||||
|
||||
def _get_enabled_skills():
|
||||
with _enabled_skills_lock:
|
||||
cached = _enabled_skills_cache
|
||||
|
||||
if cached is not None:
|
||||
return list(cached)
|
||||
|
||||
_ensure_enabled_skills_cache()
|
||||
return []
|
||||
|
||||
|
||||
def _skill_mutability_label(category: str) -> str:
|
||||
return "[custom, editable]" if category == "custom" else "[built-in]"
|
||||
|
||||
|
||||
def clear_skills_system_prompt_cache() -> None:
|
||||
_invalidate_enabled_skills_cache()
|
||||
|
||||
|
||||
async def refresh_skills_system_prompt_cache_async() -> None:
|
||||
await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
|
||||
|
||||
|
||||
def _reset_skills_system_prompt_cache_state() -> None:
|
||||
global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
|
||||
|
||||
_get_cached_skills_prompt_section.cache_clear()
|
||||
with _enabled_skills_lock:
|
||||
_enabled_skills_cache = None
|
||||
_enabled_skills_refresh_active = False
|
||||
_enabled_skills_refresh_version = 0
|
||||
_enabled_skills_refresh_event.clear()
|
||||
|
||||
|
||||
def _refresh_enabled_skills_cache() -> None:
|
||||
"""Backward-compatible test helper for direct synchronous reload."""
|
||||
try:
|
||||
skills = _load_enabled_skills_sync()
|
||||
except Exception:
|
||||
logger.exception("Failed to load enabled skills for prompt injection")
|
||||
skills = []
|
||||
|
||||
with _enabled_skills_lock:
|
||||
_enabled_skills_cache = skills
|
||||
_enabled_skills_refresh_active = False
|
||||
_enabled_skills_refresh_event.set()
|
||||
|
||||
|
||||
def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
|
||||
if not skill_evolution_enabled:
|
||||
return ""
|
||||
return """
|
||||
## Skill Self-Evolution
|
||||
After completing a task, consider creating or updating a skill when:
|
||||
- The task required 5+ tool calls to resolve
|
||||
- You overcame non-obvious errors or pitfalls
|
||||
- The user corrected your approach and the corrected version worked
|
||||
- You discovered a non-trivial, recurring workflow
|
||||
If you used a skill and encountered issues not covered by it, patch it immediately.
|
||||
Prefer patch over edit. Before creating a new skill, confirm with the user first.
|
||||
Skip simple one-off tasks.
|
||||
"""
|
||||
|
||||
|
||||
def _build_subagent_section(max_concurrent: int) -> str:
|
||||
"""Build the subagent system prompt section with dynamic concurrency limit.
|
||||
|
||||
Args:
|
||||
max_concurrent: Maximum number of concurrent subagent calls allowed per response.
|
||||
|
||||
Returns:
|
||||
Formatted subagent section string.
|
||||
"""
|
||||
n = max_concurrent
|
||||
bash_available = "bash" in get_available_subagent_names()
|
||||
available_subagents = (
|
||||
"- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n- **bash**: For command execution (git, build, test, deploy operations)"
|
||||
if bash_available
|
||||
else "- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.\n"
|
||||
"- **bash**: Not available in the current sandbox configuration. Use direct file/web tools or switch to AioSandboxProvider for isolated shell access."
|
||||
)
|
||||
direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
|
||||
direct_execution_example = (
|
||||
'# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test") # Direct execution, not task()'
|
||||
if bash_available
|
||||
else '# User asks: "Read the README"\n# Thinking: Single straightforward file read\n# → Execute directly\n\nread_file("/mnt/user-data/workspace/README.md") # Direct execution, not task()'
|
||||
)
|
||||
return f"""<subagent_system>
|
||||
**🚀 SUBAGENT MODE ACTIVE - DECOMPOSE, DELEGATE, SYNTHESIZE**
|
||||
|
||||
You are running with subagent capabilities enabled. Your role is to be a **task orchestrator**:
|
||||
1. **DECOMPOSE**: Break complex tasks into parallel sub-tasks
|
||||
2. **DELEGATE**: Launch multiple subagents simultaneously using parallel `task` calls
|
||||
3. **SYNTHESIZE**: Collect and integrate results into a coherent answer
|
||||
|
||||
**CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**
|
||||
|
||||
**⛔ HARD CONCURRENCY LIMIT: MAXIMUM {n} `task` CALLS PER RESPONSE. THIS IS NOT OPTIONAL.**
|
||||
- Each response, you may include **at most {n}** `task` tool calls. Any excess calls are **silently discarded** by the system — you will lose that work.
|
||||
- **Before launching subagents, you MUST count your sub-tasks in your thinking:**
|
||||
- If count ≤ {n}: Launch all in this response.
|
||||
- If count > {n}: **Pick the {n} most important/foundational sub-tasks for this turn.** Save the rest for the next turn.
|
||||
- **Multi-batch execution** (for >{n} sub-tasks):
|
||||
- Turn 1: Launch sub-tasks 1-{n} in parallel → wait for results
|
||||
- Turn 2: Launch next batch in parallel → wait for results
|
||||
- ... continue until all sub-tasks are complete
|
||||
- Final turn: Synthesize ALL results into a coherent answer
|
||||
- **Example thinking pattern**: "I identified 6 sub-tasks. Since the limit is {n} per turn, I will launch the first {n} now, and the rest in the next turn."
|
||||
|
||||
**Available Subagents:**
|
||||
{available_subagents}
|
||||
|
||||
**Your Orchestration Strategy:**
|
||||
|
||||
✅ **DECOMPOSE + PARALLEL EXECUTION (Preferred Approach):**
|
||||
|
||||
For complex queries, break them down into focused sub-tasks and execute in parallel batches (max {n} per turn):
|
||||
|
||||
**Example 1: "Why is Tencent's stock price declining?" (3 sub-tasks → 1 batch)**
|
||||
→ Turn 1: Launch 3 subagents in parallel:
|
||||
- Subagent 1: Recent financial reports, earnings data, and revenue trends
|
||||
- Subagent 2: Negative news, controversies, and regulatory issues
|
||||
- Subagent 3: Industry trends, competitor performance, and market sentiment
|
||||
→ Turn 2: Synthesize results
|
||||
|
||||
**Example 2: "Compare 5 cloud providers" (5 sub-tasks → multi-batch)**
|
||||
→ Turn 1: Launch {n} subagents in parallel (first batch)
|
||||
→ Turn 2: Launch remaining subagents in parallel
|
||||
→ Final turn: Synthesize ALL results into comprehensive comparison
|
||||
|
||||
**Example 3: "Refactor the authentication system"**
|
||||
→ Turn 1: Launch 3 subagents in parallel:
|
||||
- Subagent 1: Analyze current auth implementation and technical debt
|
||||
- Subagent 2: Research best practices and security patterns
|
||||
- Subagent 3: Review related tests, documentation, and vulnerabilities
|
||||
→ Turn 2: Synthesize results
|
||||
|
||||
✅ **USE Parallel Subagents (max {n} per turn) when:**
|
||||
- **Complex research questions**: Requires multiple information sources or perspectives
|
||||
- **Multi-aspect analysis**: Task has several independent dimensions to explore
|
||||
- **Large codebases**: Need to analyze different parts simultaneously
|
||||
- **Comprehensive investigations**: Questions requiring thorough coverage from multiple angles
|
||||
|
||||
❌ **DO NOT use subagents (execute directly) when:**
|
||||
- **Task cannot be decomposed**: If you can't break it into 2+ meaningful parallel sub-tasks, execute directly
|
||||
- **Ultra-simple actions**: Read one file, quick edits, single commands
|
||||
- **Need immediate clarification**: Must ask user before proceeding
|
||||
- **Meta conversation**: Questions about conversation history
|
||||
- **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)
|
||||
|
||||
**CRITICAL WORKFLOW** (STRICTLY follow this before EVERY action):
|
||||
1. **COUNT**: In your thinking, list all sub-tasks and count them explicitly: "I have N sub-tasks"
|
||||
2. **PLAN BATCHES**: If N > {n}, explicitly plan which sub-tasks go in which batch:
|
||||
- "Batch 1 (this turn): first {n} sub-tasks"
|
||||
- "Batch 2 (next turn): next batch of sub-tasks"
|
||||
3. **EXECUTE**: Launch ONLY the current batch (max {n} `task` calls). Do NOT launch sub-tasks from future batches.
|
||||
4. **REPEAT**: After results return, launch the next batch. Continue until all batches complete.
|
||||
5. **SYNTHESIZE**: After ALL batches are done, synthesize all results.
|
||||
6. **Cannot decompose** → Execute directly using available tools ({direct_tool_examples})
|
||||
|
||||
**⛔ VIOLATION: Launching more than {n} `task` calls in a single response is a HARD ERROR. The system WILL discard excess calls and you WILL lose work. Always batch.**
|
||||
|
||||
**Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**
|
||||
|
||||
**How It Works:**
|
||||
- The task tool runs subagents asynchronously in the background
|
||||
- The backend automatically polls for completion (you don't need to poll)
|
||||
- The tool call will block until the subagent completes its work
|
||||
- Once complete, the result is returned to you directly
|
||||
|
||||
**Usage Example 1 - Single Batch (≤{n} sub-tasks):**
|
||||
|
||||
```python
|
||||
# User asks: "Why is Tencent's stock price declining?"
|
||||
# Thinking: 3 sub-tasks → fits in 1 batch
|
||||
|
||||
# Turn 1: Launch 3 subagents in parallel
|
||||
task(description="Tencent financial data", prompt="...", subagent_type="general-purpose")
|
||||
task(description="Tencent news & regulation", prompt="...", subagent_type="general-purpose")
|
||||
task(description="Industry & market trends", prompt="...", subagent_type="general-purpose")
|
||||
# All 3 run in parallel → synthesize results
|
||||
```
|
||||
|
||||
**Usage Example 2 - Multiple Batches (>{n} sub-tasks):**
|
||||
|
||||
```python
|
||||
# User asks: "Compare AWS, Azure, GCP, Alibaba Cloud, and Oracle Cloud"
|
||||
# Thinking: 5 sub-tasks → need multiple batches (max {n} per batch)
|
||||
|
||||
# Turn 1: Launch first batch of {n}
|
||||
task(description="AWS analysis", prompt="...", subagent_type="general-purpose")
|
||||
task(description="Azure analysis", prompt="...", subagent_type="general-purpose")
|
||||
task(description="GCP analysis", prompt="...", subagent_type="general-purpose")
|
||||
|
||||
# Turn 2: Launch remaining batch (after first batch completes)
|
||||
task(description="Alibaba Cloud analysis", prompt="...", subagent_type="general-purpose")
|
||||
task(description="Oracle Cloud analysis", prompt="...", subagent_type="general-purpose")
|
||||
|
||||
# Turn 3: Synthesize ALL results from both batches
|
||||
```
|
||||
|
||||
**Counter-Example - Direct Execution (NO subagents):**
|
||||
|
||||
```python
|
||||
{direct_execution_example}
|
||||
```
|
||||
|
||||
**CRITICAL**:
|
||||
- **Max {n} `task` calls per turn** - the system enforces this, excess calls are discarded
|
||||
- Only use `task` when you can launch 2+ subagents in parallel
|
||||
- Single task = No value from subagents = Execute directly
|
||||
- For >{n} sub-tasks, use sequential batches of {n} across multiple turns
|
||||
</subagent_system>"""
|
||||
|
||||
|
||||
SYSTEM_PROMPT_TEMPLATE = """
|
||||
<role>
|
||||
You are {agent_name}, an open-source super agent.
|
||||
</role>
|
||||
|
||||
{soul}
|
||||
{memory_context}
|
||||
|
||||
<thinking_style>
|
||||
- Think concisely and strategically about the user's request BEFORE taking action
|
||||
- Break down the task: What is clear? What is ambiguous? What is missing?
|
||||
- **PRIORITY CHECK: If anything is unclear, missing, or has multiple interpretations, you MUST ask for clarification FIRST - do NOT proceed with work**
|
||||
{subagent_thinking}- Never write down your full final answer or report in thinking process, but only outline
|
||||
- CRITICAL: After thinking, you MUST provide your actual response to the user. Thinking is for planning, the response is for delivery.
|
||||
- Your response must contain the actual answer, not just a reference to what you thought about
|
||||
</thinking_style>
|
||||
|
||||
<clarification_system>
|
||||
**WORKFLOW PRIORITY: CLARIFY → PLAN → ACT**
|
||||
1. **FIRST**: Analyze the request in your thinking - identify what's unclear, missing, or ambiguous
|
||||
2. **SECOND**: If clarification is needed, call `ask_clarification` tool IMMEDIATELY - do NOT start working
|
||||
3. **THIRD**: Only after all clarifications are resolved, proceed with planning and execution
|
||||
|
||||
**CRITICAL RULE: Clarification ALWAYS comes BEFORE action. Never start working and clarify mid-execution.**
|
||||
|
||||
**MANDATORY Clarification Scenarios - You MUST call ask_clarification BEFORE starting work when:**
|
||||
|
||||
1. **Missing Information** (`missing_info`): Required details not provided
|
||||
- Example: User says "create a web scraper" but doesn't specify the target website
|
||||
- Example: "Deploy the app" without specifying environment
|
||||
- **REQUIRED ACTION**: Call ask_clarification to get the missing information
|
||||
|
||||
2. **Ambiguous Requirements** (`ambiguous_requirement`): Multiple valid interpretations exist
|
||||
- Example: "Optimize the code" could mean performance, readability, or memory usage
|
||||
- Example: "Make it better" is unclear what aspect to improve
|
||||
- **REQUIRED ACTION**: Call ask_clarification to clarify the exact requirement
|
||||
|
||||
3. **Approach Choices** (`approach_choice`): Several valid approaches exist
|
||||
- Example: "Add authentication" could use JWT, OAuth, session-based, or API keys
|
||||
- Example: "Store data" could use database, files, cache, etc.
|
||||
- **REQUIRED ACTION**: Call ask_clarification to let user choose the approach
|
||||
|
||||
4. **Risky Operations** (`risk_confirmation`): Destructive actions need confirmation
|
||||
- Example: Deleting files, modifying production configs, database operations
|
||||
- Example: Overwriting existing code or data
|
||||
- **REQUIRED ACTION**: Call ask_clarification to get explicit confirmation
|
||||
|
||||
5. **Suggestions** (`suggestion`): You have a recommendation but want approval
|
||||
- Example: "I recommend refactoring this code. Should I proceed?"
|
||||
- **REQUIRED ACTION**: Call ask_clarification to get approval
|
||||
|
||||
**STRICT ENFORCEMENT:**
|
||||
- ❌ DO NOT start working and then ask for clarification mid-execution - clarify FIRST
|
||||
- ❌ DO NOT skip clarification for "efficiency" - accuracy matters more than speed
|
||||
- ❌ DO NOT make assumptions when information is missing - ALWAYS ask
|
||||
- ❌ DO NOT proceed with guesses - STOP and call ask_clarification first
|
||||
- ✅ Analyze the request in thinking → Identify unclear aspects → Ask BEFORE any action
|
||||
- ✅ If you identify the need for clarification in your thinking, you MUST call the tool IMMEDIATELY
|
||||
- ✅ After calling ask_clarification, execution will be interrupted automatically
|
||||
- ✅ Wait for user response - do NOT continue with assumptions
|
||||
|
||||
**How to Use:**
|
||||
```python
|
||||
ask_clarification(
|
||||
question="Your specific question here?",
|
||||
clarification_type="missing_info", # or other type
|
||||
context="Why you need this information", # optional but recommended
|
||||
options=["option1", "option2"] # optional, for choices
|
||||
)
|
||||
```
|
||||
|
||||
**Example:**
|
||||
User: "Deploy the application"
|
||||
You (thinking): Missing environment info - I MUST ask for clarification
|
||||
You (action): ask_clarification(
|
||||
question="Which environment should I deploy to?",
|
||||
clarification_type="approach_choice",
|
||||
context="I need to know the target environment for proper configuration",
|
||||
options=["development", "staging", "production"]
|
||||
)
|
||||
[Execution stops - wait for user response]
|
||||
|
||||
User: "staging"
|
||||
You: "Deploying to staging..." [proceed]
|
||||
</clarification_system>
|
||||
|
||||
{skills_section}
|
||||
|
||||
{deferred_tools_section}
|
||||
|
||||
{subagent_section}
|
||||
|
||||
<working_directory existed="true">
|
||||
- User uploads: `/mnt/user-data/uploads` - Files uploaded by the user (automatically listed in context)
|
||||
- User workspace: `/mnt/user-data/workspace` - Working directory for temporary files
|
||||
- Output files: `/mnt/user-data/outputs` - Final deliverables must be saved here
|
||||
|
||||
**File Management:**
|
||||
- Uploaded files are automatically listed in the <uploaded_files> section before each request
|
||||
- Use `read_file` tool to read uploaded files using their paths from the list
|
||||
- For PDF, PPT, Excel, and Word files, converted Markdown versions (*.md) are available alongside originals
|
||||
- All temporary work happens in `/mnt/user-data/workspace`
|
||||
- Treat `/mnt/user-data/workspace` as your default current working directory for coding and file-editing tasks
|
||||
- When writing scripts or commands that create/read files from the workspace, prefer relative paths such as `hello.txt`, `../uploads/data.csv`, and `../outputs/report.md`
|
||||
- Avoid hardcoding `/mnt/user-data/...` inside generated scripts when a relative path from the workspace is enough
|
||||
- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_file` tool
|
||||
{acp_section}
|
||||
</working_directory>
|
||||
|
||||
<response_style>
|
||||
- Clear and Concise: Avoid over-formatting unless requested
|
||||
- Natural Tone: Use paragraphs and prose, not bullet points by default
|
||||
- Action-Oriented: Focus on delivering results, not explaining processes
|
||||
</response_style>
|
||||
|
||||
<citations>
|
||||
**CRITICAL: Always include citations when using web search results**
|
||||
|
||||
- **When to Use**: MANDATORY after web_search, web_fetch, or any external information source
|
||||
- **Format**: Use Markdown link format `[citation:TITLE](URL)` immediately after the claim
|
||||
- **Placement**: Inline citations should appear right after the sentence or claim they support
|
||||
- **Sources Section**: Also collect all citations in a "Sources" section at the end of reports
|
||||
|
||||
**Example - Inline Citations:**
|
||||
```markdown
|
||||
The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration
|
||||
[citation:AI Trends 2026](https://techcrunch.com/ai-trends).
|
||||
Recent breakthroughs in language models have also accelerated progress
|
||||
[citation:OpenAI Research](https://openai.com/research).
|
||||
```
|
||||
|
||||
**Example - Deep Research Report with Citations:**
|
||||
```markdown
|
||||
## Executive Summary
|
||||
|
||||
DeerFlow is an open-source AI agent framework that gained significant traction in early 2026
|
||||
[citation:GitHub Repository](https://github.com/bytedance/deer-flow). The project focuses on
|
||||
providing a production-ready agent system with sandbox execution and memory management
|
||||
[citation:DeerFlow Documentation](https://deer-flow.dev/docs).
|
||||
|
||||
## Key Analysis
|
||||
|
||||
### Architecture Design
|
||||
|
||||
The system uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph),
|
||||
combined with a FastAPI gateway for REST API access [citation:FastAPI](https://fastapi.tiangolo.com).
|
||||
|
||||
## Sources
|
||||
|
||||
### Primary Sources
|
||||
- [GitHub Repository](https://github.com/bytedance/deer-flow) - Official source code and documentation
|
||||
- [DeerFlow Documentation](https://deer-flow.dev/docs) - Technical specifications
|
||||
|
||||
### Media Coverage
|
||||
- [AI Trends 2026](https://techcrunch.com/ai-trends) - Industry analysis
|
||||
```
|
||||
|
||||
**CRITICAL: Sources section format:**
|
||||
- Every item in the Sources section MUST be a clickable markdown link with URL
|
||||
- Use standard markdown link `[Title](URL) - Description` format (NOT `[citation:...]` format)
|
||||
- The `[citation:Title](URL)` format is ONLY for inline citations within the report body
|
||||
- ❌ WRONG: `GitHub 仓库 - 官方源代码和文档` (no URL!)
|
||||
- ❌ WRONG in Sources: `[citation:GitHub Repository](url)` (citation prefix is for inline only!)
|
||||
- ✅ RIGHT in Sources: `[GitHub Repository](https://github.com/bytedance/deer-flow) - 官方源代码和文档`
|
||||
|
||||
**WORKFLOW for Research Tasks:**
|
||||
1. Use web_search to find sources → Extract {{title, url, snippet}} from results
|
||||
2. Write content with inline citations: `claim [citation:Title](url)`
|
||||
3. Collect all citations in a "Sources" section at the end
|
||||
4. NEVER write claims without citations when sources are available
|
||||
|
||||
**CRITICAL RULES:**
|
||||
- ❌ DO NOT write research content without citations
|
||||
- ❌ DO NOT forget to extract URLs from search results
|
||||
- ✅ ALWAYS add `[citation:Title](URL)` after claims from external sources
|
||||
- ✅ ALWAYS include a "Sources" section listing all references
|
||||
</citations>
|
||||
|
||||
<critical_reminders>
|
||||
- **Clarification First**: ALWAYS clarify unclear/missing/ambiguous requirements BEFORE starting work - never assume or guess
|
||||
{subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
|
||||
- Progressive Loading: Load resources incrementally as referenced in skills
|
||||
- Output Files: Final deliverables must be in `/mnt/user-data/outputs`
|
||||
- Clarity: Be direct and helpful, avoid unnecessary meta-commentary
|
||||
- Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `\n\n` or "```mermaid" to display images in response or Markdown files
|
||||
- Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
|
||||
- Language Consistency: Keep using the same language as user's
|
||||
- Always Respond: Your thinking is internal. You MUST always provide a visible response to the user after thinking.
|
||||
</critical_reminders>
|
||||
"""
|
||||
|
||||
|
||||
def _get_memory_context(agent_name: str | None = None) -> str:
|
||||
"""Get memory context for injection into system prompt.
|
||||
|
||||
Args:
|
||||
agent_name: If provided, loads per-agent memory. If None, loads global memory.
|
||||
|
||||
Returns:
|
||||
Formatted memory context string wrapped in XML tags, or empty string if disabled.
|
||||
"""
|
||||
try:
|
||||
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
|
||||
config = get_memory_config()
|
||||
if not config.enabled or not config.injection_enabled:
|
||||
return ""
|
||||
|
||||
memory_data = get_memory_data(agent_name)
|
||||
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
|
||||
|
||||
if not memory_content.strip():
|
||||
return ""
|
||||
|
||||
return f"""<memory>
|
||||
{memory_content}
|
||||
</memory>
|
||||
"""
|
||||
except Exception as e:
|
||||
logger.error("Failed to load memory context: %s", e)
|
||||
return ""
|
||||
|
||||
|
||||
@lru_cache(maxsize=32)
|
||||
def _get_cached_skills_prompt_section(
|
||||
skill_signature: tuple[tuple[str, str, str, str], ...],
|
||||
available_skills_key: tuple[str, ...] | None,
|
||||
container_base_path: str,
|
||||
skill_evolution_section: str,
|
||||
) -> str:
|
||||
filtered = [(name, description, category, location) for name, description, category, location in skill_signature if available_skills_key is None or name in available_skills_key]
|
||||
skills_list = ""
|
||||
if filtered:
|
||||
skill_items = "\n".join(
|
||||
f" <skill>\n <name>{name}</name>\n <description>{description} {_skill_mutability_label(category)}</description>\n <location>{location}</location>\n </skill>"
|
||||
for name, description, category, location in filtered
|
||||
)
|
||||
skills_list = f"<available_skills>\n{skill_items}\n</available_skills>"
|
||||
return f"""<skill_system>
|
||||
You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
|
||||
|
||||
**Progressive Loading Pattern:**
|
||||
1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
|
||||
2. Read and understand the skill's workflow and instructions
|
||||
3. The skill file contains references to external resources under the same folder
|
||||
4. Load referenced resources only when needed during execution
|
||||
5. Follow the skill's instructions precisely
|
||||
|
||||
**Skills are located at:** {container_base_path}
|
||||
{skill_evolution_section}
|
||||
{skills_list}
|
||||
|
||||
</skill_system>"""
|
||||
|
||||
|
||||
def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
|
||||
"""Generate the skills prompt section with available skills list."""
|
||||
skills = _get_enabled_skills()
|
||||
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
config = get_app_config()
|
||||
container_base_path = config.skills.container_path
|
||||
skill_evolution_enabled = config.skill_evolution.enabled
|
||||
except Exception:
|
||||
container_base_path = "/mnt/skills"
|
||||
skill_evolution_enabled = False
|
||||
|
||||
if not skills and not skill_evolution_enabled:
|
||||
return ""
|
||||
|
||||
if available_skills is not None and not any(skill.name in available_skills for skill in skills):
|
||||
return ""
|
||||
|
||||
skill_signature = tuple((skill.name, skill.description, skill.category, skill.get_container_file_path(container_base_path)) for skill in skills)
|
||||
available_key = tuple(sorted(available_skills)) if available_skills is not None else None
|
||||
if not skill_signature and available_key is not None:
|
||||
return ""
|
||||
skill_evolution_section = _build_skill_evolution_section(skill_evolution_enabled)
|
||||
return _get_cached_skills_prompt_section(skill_signature, available_key, container_base_path, skill_evolution_section)
|
||||
|
||||
|
||||
def get_agent_soul(agent_name: str | None) -> str:
|
||||
# Append SOUL.md (agent personality) if present
|
||||
soul = load_agent_soul(agent_name)
|
||||
if soul:
|
||||
return f"<soul>\n{soul}\n</soul>\n" if soul else ""
|
||||
return ""
|
||||
|
||||
|
||||
def get_deferred_tools_prompt_section() -> str:
|
||||
"""Generate <available-deferred-tools> block for the system prompt.
|
||||
|
||||
Lists only deferred tool names so the agent knows what exists
|
||||
and can use tool_search to load them.
|
||||
Returns empty string when tool_search is disabled or no tools are deferred.
|
||||
"""
|
||||
from deerflow.tools.builtins.tool_search import get_deferred_registry
|
||||
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
if not get_app_config().tool_search.enabled:
|
||||
return ""
|
||||
except Exception:
|
||||
return ""
|
||||
|
||||
registry = get_deferred_registry()
|
||||
if not registry:
|
||||
return ""
|
||||
|
||||
names = "\n".join(e.name for e in registry.entries)
|
||||
return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
|
||||
|
||||
|
||||
def _build_acp_section() -> str:
|
||||
"""Build the ACP agent prompt section, only if ACP agents are configured."""
|
||||
try:
|
||||
from deerflow.config.acp_config import get_acp_agents
|
||||
|
||||
agents = get_acp_agents()
|
||||
if not agents:
|
||||
return ""
|
||||
except Exception:
|
||||
return ""
|
||||
|
||||
return (
|
||||
"\n**ACP Agent Tasks (invoke_acp_agent):**\n"
|
||||
"- ACP agents (e.g. codex, claude_code) run in their own independent workspace — NOT in `/mnt/user-data/`\n"
|
||||
"- When writing prompts for ACP agents, describe the task only — do NOT reference `/mnt/user-data` paths\n"
|
||||
"- ACP agent results are accessible at `/mnt/acp-workspace/` (read-only) — use `ls`, `read_file`, or `bash cp` to retrieve output files\n"
|
||||
"- To deliver ACP output to the user: copy from `/mnt/acp-workspace/<file>` to `/mnt/user-data/outputs/<file>`, then use `present_file`"
|
||||
)
|
||||
|
||||
|
||||
def _build_custom_mounts_section() -> str:
|
||||
"""Build a prompt section for explicitly configured sandbox mounts."""
|
||||
try:
|
||||
from deerflow.config import get_app_config
|
||||
|
||||
mounts = get_app_config().sandbox.mounts or []
|
||||
except Exception:
|
||||
logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
|
||||
return ""
|
||||
|
||||
if not mounts:
|
||||
return ""
|
||||
|
||||
lines = []
|
||||
for mount in mounts:
|
||||
access = "read-only" if mount.read_only else "read-write"
|
||||
lines.append(f"- Custom mount: `{mount.container_path}` - Host directory mapped into the sandbox ({access})")
|
||||
|
||||
mounts_list = "\n".join(lines)
|
||||
return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"
|
||||
|
||||
|
||||
def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
|
||||
# Get memory context
|
||||
memory_context = _get_memory_context(agent_name)
|
||||
|
||||
# Include subagent section only if enabled (from runtime parameter)
|
||||
n = max_concurrent_subagents
|
||||
subagent_section = _build_subagent_section(n) if subagent_enabled else ""
|
||||
|
||||
# Add subagent reminder to critical_reminders if enabled
|
||||
subagent_reminder = (
|
||||
"- **Orchestrator Mode**: You are a task orchestrator - decompose complex tasks into parallel sub-tasks. "
|
||||
f"**HARD LIMIT: max {n} `task` calls per response.** "
|
||||
f"If >{n} sub-tasks, split into sequential batches of ≤{n}. Synthesize after ALL batches complete.\n"
|
||||
if subagent_enabled
|
||||
else ""
|
||||
)
|
||||
|
||||
# Add subagent thinking guidance if enabled
|
||||
subagent_thinking = (
|
||||
"- **DECOMPOSITION CHECK: Can this task be broken into 2+ parallel sub-tasks? If YES, COUNT them. "
|
||||
f"If count > {n}, you MUST plan batches of ≤{n} and only launch the FIRST batch now. "
|
||||
f"NEVER launch more than {n} `task` calls in one response.**\n"
|
||||
if subagent_enabled
|
||||
else ""
|
||||
)
|
||||
|
||||
# Get skills section
|
||||
skills_section = get_skills_prompt_section(available_skills)
|
||||
|
||||
# Get deferred tools section (tool_search)
|
||||
deferred_tools_section = get_deferred_tools_prompt_section()
|
||||
|
||||
# Build ACP agent section only if ACP agents are configured
|
||||
acp_section = _build_acp_section()
|
||||
custom_mounts_section = _build_custom_mounts_section()
|
||||
acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
|
||||
|
||||
# Format the prompt with dynamic skills and memory
|
||||
prompt = SYSTEM_PROMPT_TEMPLATE.format(
|
||||
agent_name=agent_name or "DeerFlow 2.0",
|
||||
soul=get_agent_soul(agent_name),
|
||||
skills_section=skills_section,
|
||||
deferred_tools_section=deferred_tools_section,
|
||||
memory_context=memory_context,
|
||||
subagent_section=subagent_section,
|
||||
subagent_reminder=subagent_reminder,
|
||||
subagent_thinking=subagent_thinking,
|
||||
acp_section=acp_and_mounts_section,
|
||||
)
|
||||
|
||||
return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"
|
||||
@@ -0,0 +1,57 @@
|
||||
"""Memory module for DeerFlow.
|
||||
|
||||
This module provides a global memory mechanism that:
|
||||
- Stores user context and conversation history in memory.json
|
||||
- Uses LLM to summarize and extract facts from conversations
|
||||
- Injects relevant memory into system prompts for personalized responses
|
||||
"""
|
||||
|
||||
from deerflow.agents.memory.prompt import (
|
||||
FACT_EXTRACTION_PROMPT,
|
||||
MEMORY_UPDATE_PROMPT,
|
||||
format_conversation_for_update,
|
||||
format_memory_for_injection,
|
||||
)
|
||||
from deerflow.agents.memory.queue import (
|
||||
ConversationContext,
|
||||
MemoryUpdateQueue,
|
||||
get_memory_queue,
|
||||
reset_memory_queue,
|
||||
)
|
||||
from deerflow.agents.memory.storage import (
|
||||
FileMemoryStorage,
|
||||
MemoryStorage,
|
||||
get_memory_storage,
|
||||
)
|
||||
from deerflow.agents.memory.updater import (
|
||||
MemoryUpdater,
|
||||
clear_memory_data,
|
||||
delete_memory_fact,
|
||||
get_memory_data,
|
||||
reload_memory_data,
|
||||
update_memory_from_conversation,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Prompt utilities
|
||||
"MEMORY_UPDATE_PROMPT",
|
||||
"FACT_EXTRACTION_PROMPT",
|
||||
"format_memory_for_injection",
|
||||
"format_conversation_for_update",
|
||||
# Queue
|
||||
"ConversationContext",
|
||||
"MemoryUpdateQueue",
|
||||
"get_memory_queue",
|
||||
"reset_memory_queue",
|
||||
# Storage
|
||||
"MemoryStorage",
|
||||
"FileMemoryStorage",
|
||||
"get_memory_storage",
|
||||
# Updater
|
||||
"MemoryUpdater",
|
||||
"clear_memory_data",
|
||||
"delete_memory_fact",
|
||||
"get_memory_data",
|
||||
"reload_memory_data",
|
||||
"update_memory_from_conversation",
|
||||
]
|
||||
@@ -0,0 +1,363 @@
|
||||
"""Prompt templates for memory update and injection."""
|
||||
|
||||
import math
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
try:
|
||||
import tiktoken
|
||||
|
||||
TIKTOKEN_AVAILABLE = True
|
||||
except ImportError:
|
||||
TIKTOKEN_AVAILABLE = False
|
||||
|
||||
# Prompt template for updating memory based on conversation
|
||||
MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
|
||||
|
||||
Current Memory State:
|
||||
<current_memory>
|
||||
{current_memory}
|
||||
</current_memory>
|
||||
|
||||
New Conversation to Process:
|
||||
<conversation>
|
||||
{conversation}
|
||||
</conversation>
|
||||
|
||||
Instructions:
|
||||
1. Analyze the conversation for important information about the user
|
||||
2. Extract relevant facts, preferences, and context with specific details (numbers, names, technologies)
|
||||
3. Update the memory sections as needed following the detailed length guidelines below
|
||||
|
||||
Before extracting facts, perform a structured reflection on the conversation:
|
||||
1. Error/Retry Detection: Did the agent encounter errors, require retries, or produce incorrect results?
|
||||
If yes, record the root cause and correct approach as a high-confidence fact with category "correction".
|
||||
2. User Correction Detection: Did the user correct the agent's direction, understanding, or output?
|
||||
If yes, record the correct interpretation or approach as a high-confidence fact with category "correction".
|
||||
Include what went wrong in "sourceError" only when category is "correction" and the mistake is explicit in the conversation.
|
||||
3. Project Constraint Discovery: Were any project-specific constraints discovered during the conversation?
|
||||
If yes, record them as facts with the most appropriate category and confidence.
|
||||
|
||||
{correction_hint}
|
||||
|
||||
Memory Section Guidelines:
|
||||
|
||||
**User Context** (Current state - concise summaries):
|
||||
- workContext: Professional role, company, key projects, main technologies (2-3 sentences)
|
||||
Example: Core contributor, project names with metrics (16k+ stars), technical stack
|
||||
- personalContext: Languages, communication preferences, key interests (1-2 sentences)
|
||||
Example: Bilingual capabilities, specific interest areas, expertise domains
|
||||
- topOfMind: Multiple ongoing focus areas and priorities (3-5 sentences, detailed paragraph)
|
||||
Example: Primary project work, parallel technical investigations, ongoing learning/tracking
|
||||
Include: Active implementation work, troubleshooting issues, market/research interests
|
||||
Note: This captures SEVERAL concurrent focus areas, not just one task
|
||||
|
||||
**History** (Temporal context - rich paragraphs):
|
||||
- recentMonths: Detailed summary of recent activities (4-6 sentences or 1-2 paragraphs)
|
||||
Timeline: Last 1-3 months of interactions
|
||||
Include: Technologies explored, projects worked on, problems solved, interests demonstrated
|
||||
- earlierContext: Important historical patterns (3-5 sentences or 1 paragraph)
|
||||
Timeline: 3-12 months ago
|
||||
Include: Past projects, learning journeys, established patterns
|
||||
- longTermBackground: Persistent background and foundational context (2-4 sentences)
|
||||
Timeline: Overall/foundational information
|
||||
Include: Core expertise, longstanding interests, fundamental working style
|
||||
|
||||
**Facts Extraction**:
|
||||
- Extract specific, quantifiable details (e.g., "16k+ GitHub stars", "200+ datasets")
|
||||
- Include proper nouns (company names, project names, technology names)
|
||||
- Preserve technical terminology and version numbers
|
||||
- Categories:
|
||||
* preference: Tools, styles, approaches user prefers/dislikes
|
||||
* knowledge: Specific expertise, technologies mastered, domain knowledge
|
||||
* context: Background facts (job title, projects, locations, languages)
|
||||
* behavior: Working patterns, communication habits, problem-solving approaches
|
||||
* goal: Stated objectives, learning targets, project ambitions
|
||||
* correction: Explicit agent mistakes or user corrections, including the correct approach
|
||||
- Confidence levels:
|
||||
* 0.9-1.0: Explicitly stated facts ("I work on X", "My role is Y")
|
||||
* 0.7-0.8: Strongly implied from actions/discussions
|
||||
* 0.5-0.6: Inferred patterns (use sparingly, only for clear patterns)
|
||||
|
||||
**What Goes Where**:
|
||||
- workContext: Current job, active projects, primary tech stack
|
||||
- personalContext: Languages, personality, interests outside direct work tasks
|
||||
- topOfMind: Multiple ongoing priorities and focus areas user cares about recently (gets updated most frequently)
|
||||
Should capture 3-5 concurrent themes: main work, side explorations, learning/tracking interests
|
||||
- recentMonths: Detailed account of recent technical explorations and work
|
||||
- earlierContext: Patterns from slightly older interactions still relevant
|
||||
- longTermBackground: Unchanging foundational facts about the user
|
||||
|
||||
**Multilingual Content**:
|
||||
- Preserve original language for proper nouns and company names
|
||||
- Keep technical terms in their original form (DeepSeek, LangGraph, etc.)
|
||||
- Note language capabilities in personalContext
|
||||
|
||||
Output Format (JSON):
|
||||
{{
|
||||
"user": {{
|
||||
"workContext": {{ "summary": "...", "shouldUpdate": true/false }},
|
||||
"personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
|
||||
"topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
|
||||
}},
|
||||
"history": {{
|
||||
"recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
|
||||
"earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
|
||||
"longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
|
||||
}},
|
||||
"newFacts": [
|
||||
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal|correction", "confidence": 0.0-1.0 }}
|
||||
],
|
||||
"factsToRemove": ["fact_id_1", "fact_id_2"]
|
||||
}}
|
||||
|
||||
Important Rules:
|
||||
- Only set shouldUpdate=true if there's meaningful new information
|
||||
- Follow length guidelines: workContext/personalContext are concise (1-3 sentences), topOfMind and history sections are detailed (paragraphs)
|
||||
- Include specific metrics, version numbers, and proper nouns in facts
|
||||
- Only add facts that are clearly stated (0.9+) or strongly implied (0.7+)
|
||||
- Use category "correction" for explicit agent mistakes or user corrections; assign confidence >= 0.95 when the correction is explicit
|
||||
- Include "sourceError" only for explicit correction facts when the prior mistake or wrong approach is clearly stated; omit it otherwise
|
||||
- Remove facts that are contradicted by new information
|
||||
- When updating topOfMind, integrate new focus areas while removing completed/abandoned ones
|
||||
Keep 3-5 concurrent focus themes that are still active and relevant
|
||||
- For history sections, integrate new information chronologically into appropriate time period
|
||||
- Preserve technical accuracy - keep exact names of technologies, companies, projects
|
||||
- Focus on information useful for future interactions and personalization
|
||||
- IMPORTANT: Do NOT record file upload events in memory. Uploaded files are
|
||||
session-specific and ephemeral — they will not be accessible in future sessions.
|
||||
Recording upload events causes confusion in subsequent conversations.
|
||||
|
||||
Return ONLY valid JSON, no explanation or markdown."""
|
||||
|
||||
|
||||
# Prompt template for extracting facts from a single message
|
||||
FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
|
||||
|
||||
Message:
|
||||
{message}
|
||||
|
||||
Extract facts in this JSON format:
|
||||
{{
|
||||
"facts": [
|
||||
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal|correction", "confidence": 0.0-1.0 }}
|
||||
]
|
||||
}}
|
||||
|
||||
Categories:
|
||||
- preference: User preferences (likes/dislikes, styles, tools)
|
||||
- knowledge: User's expertise or knowledge areas
|
||||
- context: Background context (location, job, projects)
|
||||
- behavior: Behavioral patterns
|
||||
- goal: User's goals or objectives
|
||||
- correction: Explicit corrections or mistakes to avoid repeating
|
||||
|
||||
Rules:
|
||||
- Only extract clear, specific facts
|
||||
- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
|
||||
- Skip vague or temporary information
|
||||
|
||||
Return ONLY valid JSON."""
|
||||
|
||||
|
||||
def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
|
||||
"""Count tokens in text using tiktoken.
|
||||
|
||||
Args:
|
||||
text: The text to count tokens for.
|
||||
encoding_name: The encoding to use (default: cl100k_base for GPT-4/3.5).
|
||||
|
||||
Returns:
|
||||
The number of tokens in the text.
|
||||
"""
|
||||
if not TIKTOKEN_AVAILABLE:
|
||||
# Fallback to character-based estimation if tiktoken is not available
|
||||
return len(text) // 4
|
||||
|
||||
try:
|
||||
encoding = tiktoken.get_encoding(encoding_name)
|
||||
return len(encoding.encode(text))
|
||||
except Exception:
|
||||
# Fallback to character-based estimation on error
|
||||
return len(text) // 4
|
||||
|
||||
|
||||
def _coerce_confidence(value: Any, default: float = 0.0) -> float:
|
||||
"""Coerce a confidence-like value to a bounded float in [0, 1].
|
||||
|
||||
Non-finite values (NaN, inf, -inf) are treated as invalid and fall back
|
||||
to the default before clamping, preventing them from dominating ranking.
|
||||
The ``default`` parameter is assumed to be a finite value.
|
||||
"""
|
||||
try:
|
||||
confidence = float(value)
|
||||
except (TypeError, ValueError):
|
||||
return max(0.0, min(1.0, default))
|
||||
if not math.isfinite(confidence):
|
||||
return max(0.0, min(1.0, default))
|
||||
return max(0.0, min(1.0, confidence))
|
||||
|
||||
|
||||
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
|
||||
"""Format memory data for injection into system prompt.
|
||||
|
||||
Args:
|
||||
memory_data: The memory data dictionary.
|
||||
max_tokens: Maximum tokens to use (counted via tiktoken for accuracy).
|
||||
|
||||
Returns:
|
||||
Formatted memory string for system prompt injection.
|
||||
"""
|
||||
if not memory_data:
|
||||
return ""
|
||||
|
||||
sections = []
|
||||
|
||||
# Format user context
|
||||
user_data = memory_data.get("user", {})
|
||||
if user_data:
|
||||
user_sections = []
|
||||
|
||||
work_ctx = user_data.get("workContext", {})
|
||||
if work_ctx.get("summary"):
|
||||
user_sections.append(f"Work: {work_ctx['summary']}")
|
||||
|
||||
personal_ctx = user_data.get("personalContext", {})
|
||||
if personal_ctx.get("summary"):
|
||||
user_sections.append(f"Personal: {personal_ctx['summary']}")
|
||||
|
||||
top_of_mind = user_data.get("topOfMind", {})
|
||||
if top_of_mind.get("summary"):
|
||||
user_sections.append(f"Current Focus: {top_of_mind['summary']}")
|
||||
|
||||
if user_sections:
|
||||
sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
|
||||
|
||||
# Format history
|
||||
history_data = memory_data.get("history", {})
|
||||
if history_data:
|
||||
history_sections = []
|
||||
|
||||
recent = history_data.get("recentMonths", {})
|
||||
if recent.get("summary"):
|
||||
history_sections.append(f"Recent: {recent['summary']}")
|
||||
|
||||
earlier = history_data.get("earlierContext", {})
|
||||
if earlier.get("summary"):
|
||||
history_sections.append(f"Earlier: {earlier['summary']}")
|
||||
|
||||
background = history_data.get("longTermBackground", {})
|
||||
if background.get("summary"):
|
||||
history_sections.append(f"Background: {background['summary']}")
|
||||
|
||||
if history_sections:
|
||||
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
|
||||
|
||||
# Format facts (sorted by confidence; include as many as token budget allows)
|
||||
facts_data = memory_data.get("facts", [])
|
||||
if isinstance(facts_data, list) and facts_data:
|
||||
ranked_facts = sorted(
|
||||
(f for f in facts_data if isinstance(f, dict) and isinstance(f.get("content"), str) and f.get("content").strip()),
|
||||
key=lambda fact: _coerce_confidence(fact.get("confidence"), default=0.0),
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
# Compute token count for existing sections once, then account
|
||||
# incrementally for each fact line to avoid full-string re-tokenization.
|
||||
base_text = "\n\n".join(sections)
|
||||
base_tokens = _count_tokens(base_text) if base_text else 0
|
||||
# Account for the separator between existing sections and the facts section.
|
||||
facts_header = "Facts:\n"
|
||||
separator_tokens = _count_tokens("\n\n" + facts_header) if base_text else _count_tokens(facts_header)
|
||||
running_tokens = base_tokens + separator_tokens
|
||||
|
||||
fact_lines: list[str] = []
|
||||
for fact in ranked_facts:
|
||||
content_value = fact.get("content")
|
||||
if not isinstance(content_value, str):
|
||||
continue
|
||||
content = content_value.strip()
|
||||
if not content:
|
||||
continue
|
||||
category = str(fact.get("category", "context")).strip() or "context"
|
||||
confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
|
||||
source_error = fact.get("sourceError")
|
||||
if category == "correction" and isinstance(source_error, str) and source_error.strip():
|
||||
line = f"- [{category} | {confidence:.2f}] {content} (avoid: {source_error.strip()})"
|
||||
else:
|
||||
line = f"- [{category} | {confidence:.2f}] {content}"
|
||||
|
||||
# Each additional line is preceded by a newline (except the first).
|
||||
line_text = ("\n" + line) if fact_lines else line
|
||||
line_tokens = _count_tokens(line_text)
|
||||
|
||||
if running_tokens + line_tokens <= max_tokens:
|
||||
fact_lines.append(line)
|
||||
running_tokens += line_tokens
|
||||
else:
|
||||
break
|
||||
|
||||
if fact_lines:
|
||||
sections.append("Facts:\n" + "\n".join(fact_lines))
|
||||
|
||||
if not sections:
|
||||
return ""
|
||||
|
||||
result = "\n\n".join(sections)
|
||||
|
||||
# Use accurate token counting with tiktoken
|
||||
token_count = _count_tokens(result)
|
||||
if token_count > max_tokens:
|
||||
# Truncate to fit within token limit
|
||||
# Estimate characters to remove based on token ratio
|
||||
char_per_token = len(result) / token_count
|
||||
target_chars = int(max_tokens * char_per_token * 0.95) # 95% to leave margin
|
||||
result = result[:target_chars] + "\n..."
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def format_conversation_for_update(messages: list[Any]) -> str:
|
||||
"""Format conversation messages for memory update prompt.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages.
|
||||
|
||||
Returns:
|
||||
Formatted conversation string.
|
||||
"""
|
||||
lines = []
|
||||
for msg in messages:
|
||||
role = getattr(msg, "type", "unknown")
|
||||
content = getattr(msg, "content", str(msg))
|
||||
|
||||
# Handle content that might be a list (multimodal)
|
||||
if isinstance(content, list):
|
||||
text_parts = []
|
||||
for p in content:
|
||||
if isinstance(p, str):
|
||||
text_parts.append(p)
|
||||
elif isinstance(p, dict):
|
||||
text_val = p.get("text")
|
||||
if isinstance(text_val, str):
|
||||
text_parts.append(text_val)
|
||||
content = " ".join(text_parts) if text_parts else str(content)
|
||||
|
||||
# Strip uploaded_files tags from human messages to avoid persisting
|
||||
# ephemeral file path info into long-term memory. Skip the turn entirely
|
||||
# when nothing remains after stripping (upload-only message).
|
||||
if role == "human":
|
||||
content = re.sub(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", "", str(content)).strip()
|
||||
if not content:
|
||||
continue
|
||||
|
||||
# Truncate very long messages
|
||||
if len(str(content)) > 1000:
|
||||
content = str(content)[:1000] + "..."
|
||||
|
||||
if role == "human":
|
||||
lines.append(f"User: {content}")
|
||||
elif role == "ai":
|
||||
lines.append(f"Assistant: {content}")
|
||||
|
||||
return "\n\n".join(lines)
|
||||
@@ -0,0 +1,219 @@
|
||||
"""Memory update queue with debounce mechanism."""
|
||||
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConversationContext:
|
||||
"""Context for a conversation to be processed for memory update."""
|
||||
|
||||
thread_id: str
|
||||
messages: list[Any]
|
||||
timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
|
||||
agent_name: str | None = None
|
||||
correction_detected: bool = False
|
||||
reinforcement_detected: bool = False
|
||||
|
||||
|
||||
class MemoryUpdateQueue:
|
||||
"""Queue for memory updates with debounce mechanism.
|
||||
|
||||
This queue collects conversation contexts and processes them after
|
||||
a configurable debounce period. Multiple conversations received within
|
||||
the debounce window are batched together.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the memory update queue."""
|
||||
self._queue: list[ConversationContext] = []
|
||||
self._lock = threading.Lock()
|
||||
self._timer: threading.Timer | None = None
|
||||
self._processing = False
|
||||
|
||||
def add(
|
||||
self,
|
||||
thread_id: str,
|
||||
messages: list[Any],
|
||||
agent_name: str | None = None,
|
||||
correction_detected: bool = False,
|
||||
reinforcement_detected: bool = False,
|
||||
) -> None:
|
||||
"""Add a conversation to the update queue.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
messages: The conversation messages.
|
||||
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
|
||||
correction_detected: Whether recent turns include an explicit correction signal.
|
||||
reinforcement_detected: Whether recent turns include a positive reinforcement signal.
|
||||
"""
|
||||
config = get_memory_config()
|
||||
if not config.enabled:
|
||||
return
|
||||
|
||||
with self._lock:
|
||||
existing_context = next(
|
||||
(context for context in self._queue if context.thread_id == thread_id),
|
||||
None,
|
||||
)
|
||||
merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
|
||||
merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
|
||||
context = ConversationContext(
|
||||
thread_id=thread_id,
|
||||
messages=messages,
|
||||
agent_name=agent_name,
|
||||
correction_detected=merged_correction_detected,
|
||||
reinforcement_detected=merged_reinforcement_detected,
|
||||
)
|
||||
|
||||
# Check if this thread already has a pending update
|
||||
# If so, replace it with the newer one
|
||||
self._queue = [c for c in self._queue if c.thread_id != thread_id]
|
||||
self._queue.append(context)
|
||||
|
||||
# Reset or start the debounce timer
|
||||
self._reset_timer()
|
||||
|
||||
logger.info("Memory update queued for thread %s, queue size: %d", thread_id, len(self._queue))
|
||||
|
||||
def _reset_timer(self) -> None:
|
||||
"""Reset the debounce timer."""
|
||||
config = get_memory_config()
|
||||
|
||||
# Cancel existing timer if any
|
||||
if self._timer is not None:
|
||||
self._timer.cancel()
|
||||
|
||||
# Start new timer
|
||||
self._timer = threading.Timer(
|
||||
config.debounce_seconds,
|
||||
self._process_queue,
|
||||
)
|
||||
self._timer.daemon = True
|
||||
self._timer.start()
|
||||
|
||||
logger.debug("Memory update timer set for %ss", config.debounce_seconds)
|
||||
|
||||
def _process_queue(self) -> None:
|
||||
"""Process all queued conversation contexts."""
|
||||
# Import here to avoid circular dependency
|
||||
from deerflow.agents.memory.updater import MemoryUpdater
|
||||
|
||||
with self._lock:
|
||||
if self._processing:
|
||||
# Already processing, reschedule
|
||||
self._reset_timer()
|
||||
return
|
||||
|
||||
if not self._queue:
|
||||
return
|
||||
|
||||
self._processing = True
|
||||
contexts_to_process = self._queue.copy()
|
||||
self._queue.clear()
|
||||
self._timer = None
|
||||
|
||||
logger.info("Processing %d queued memory updates", len(contexts_to_process))
|
||||
|
||||
try:
|
||||
updater = MemoryUpdater()
|
||||
|
||||
for context in contexts_to_process:
|
||||
try:
|
||||
logger.info("Updating memory for thread %s", context.thread_id)
|
||||
success = updater.update_memory(
|
||||
messages=context.messages,
|
||||
thread_id=context.thread_id,
|
||||
agent_name=context.agent_name,
|
||||
correction_detected=context.correction_detected,
|
||||
reinforcement_detected=context.reinforcement_detected,
|
||||
)
|
||||
if success:
|
||||
logger.info("Memory updated successfully for thread %s", context.thread_id)
|
||||
else:
|
||||
logger.warning("Memory update skipped/failed for thread %s", context.thread_id)
|
||||
except Exception as e:
|
||||
logger.error("Error updating memory for thread %s: %s", context.thread_id, e)
|
||||
|
||||
# Small delay between updates to avoid rate limiting
|
||||
if len(contexts_to_process) > 1:
|
||||
time.sleep(0.5)
|
||||
|
||||
finally:
|
||||
with self._lock:
|
||||
self._processing = False
|
||||
|
||||
def flush(self) -> None:
|
||||
"""Force immediate processing of the queue.
|
||||
|
||||
This is useful for testing or graceful shutdown.
|
||||
"""
|
||||
with self._lock:
|
||||
if self._timer is not None:
|
||||
self._timer.cancel()
|
||||
self._timer = None
|
||||
|
||||
self._process_queue()
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear the queue without processing.
|
||||
|
||||
This is useful for testing.
|
||||
"""
|
||||
with self._lock:
|
||||
if self._timer is not None:
|
||||
self._timer.cancel()
|
||||
self._timer = None
|
||||
self._queue.clear()
|
||||
self._processing = False
|
||||
|
||||
@property
|
||||
def pending_count(self) -> int:
|
||||
"""Get the number of pending updates."""
|
||||
with self._lock:
|
||||
return len(self._queue)
|
||||
|
||||
@property
|
||||
def is_processing(self) -> bool:
|
||||
"""Check if the queue is currently being processed."""
|
||||
with self._lock:
|
||||
return self._processing
|
||||
|
||||
|
||||
# Global singleton instance
|
||||
_memory_queue: MemoryUpdateQueue | None = None
|
||||
_queue_lock = threading.Lock()
|
||||
|
||||
|
||||
def get_memory_queue() -> MemoryUpdateQueue:
|
||||
"""Get the global memory update queue singleton.
|
||||
|
||||
Returns:
|
||||
The memory update queue instance.
|
||||
"""
|
||||
global _memory_queue
|
||||
with _queue_lock:
|
||||
if _memory_queue is None:
|
||||
_memory_queue = MemoryUpdateQueue()
|
||||
return _memory_queue
|
||||
|
||||
|
||||
def reset_memory_queue() -> None:
|
||||
"""Reset the global memory queue.
|
||||
|
||||
This is useful for testing.
|
||||
"""
|
||||
global _memory_queue
|
||||
with _queue_lock:
|
||||
if _memory_queue is not None:
|
||||
_memory_queue.clear()
|
||||
_memory_queue = None
|
||||
@@ -0,0 +1,205 @@
|
||||
"""Memory storage providers."""
|
||||
|
||||
import abc
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from deerflow.config.agents_config import AGENT_NAME_PATTERN
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def utc_now_iso_z() -> str:
|
||||
"""Current UTC time as ISO-8601 with ``Z`` suffix (matches prior naive-UTC output)."""
|
||||
return datetime.now(UTC).isoformat().removesuffix("+00:00") + "Z"
|
||||
|
||||
|
||||
def create_empty_memory() -> dict[str, Any]:
|
||||
"""Create an empty memory structure."""
|
||||
return {
|
||||
"version": "1.0",
|
||||
"lastUpdated": utc_now_iso_z(),
|
||||
"user": {
|
||||
"workContext": {"summary": "", "updatedAt": ""},
|
||||
"personalContext": {"summary": "", "updatedAt": ""},
|
||||
"topOfMind": {"summary": "", "updatedAt": ""},
|
||||
},
|
||||
"history": {
|
||||
"recentMonths": {"summary": "", "updatedAt": ""},
|
||||
"earlierContext": {"summary": "", "updatedAt": ""},
|
||||
"longTermBackground": {"summary": "", "updatedAt": ""},
|
||||
},
|
||||
"facts": [],
|
||||
}
|
||||
|
||||
|
||||
class MemoryStorage(abc.ABC):
|
||||
"""Abstract base class for memory storage providers."""
|
||||
|
||||
@abc.abstractmethod
|
||||
def load(self, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Load memory data for the given agent."""
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
def reload(self, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Force reload memory data for the given agent."""
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
def save(self, memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
|
||||
"""Save memory data for the given agent."""
|
||||
pass
|
||||
|
||||
|
||||
class FileMemoryStorage(MemoryStorage):
|
||||
"""File-based memory storage provider."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the file memory storage."""
|
||||
# Per-agent memory cache: keyed by agent_name (None = global)
|
||||
# Value: (memory_data, file_mtime)
|
||||
self._memory_cache: dict[str | None, tuple[dict[str, Any], float | None]] = {}
|
||||
|
||||
def _validate_agent_name(self, agent_name: str) -> None:
|
||||
"""Validate that the agent name is safe to use in filesystem paths.
|
||||
|
||||
Uses the repository's established AGENT_NAME_PATTERN to ensure consistency
|
||||
across the codebase and prevent path traversal or other problematic characters.
|
||||
"""
|
||||
if not agent_name:
|
||||
raise ValueError("Agent name must be a non-empty string.")
|
||||
if not AGENT_NAME_PATTERN.match(agent_name):
|
||||
raise ValueError(f"Invalid agent name {agent_name!r}: names must match {AGENT_NAME_PATTERN.pattern}")
|
||||
|
||||
def _get_memory_file_path(self, agent_name: str | None = None) -> Path:
|
||||
"""Get the path to the memory file."""
|
||||
if agent_name is not None:
|
||||
self._validate_agent_name(agent_name)
|
||||
return get_paths().agent_memory_file(agent_name)
|
||||
|
||||
config = get_memory_config()
|
||||
if config.storage_path:
|
||||
p = Path(config.storage_path)
|
||||
return p if p.is_absolute() else get_paths().base_dir / p
|
||||
return get_paths().memory_file
|
||||
|
||||
def _load_memory_from_file(self, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Load memory data from file."""
|
||||
file_path = self._get_memory_file_path(agent_name)
|
||||
|
||||
if not file_path.exists():
|
||||
return create_empty_memory()
|
||||
|
||||
try:
|
||||
with open(file_path, encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
return data
|
||||
except (json.JSONDecodeError, OSError) as e:
|
||||
logger.warning("Failed to load memory file: %s", e)
|
||||
return create_empty_memory()
|
||||
|
||||
def load(self, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Load memory data (cached with file modification time check)."""
|
||||
file_path = self._get_memory_file_path(agent_name)
|
||||
|
||||
try:
|
||||
current_mtime = file_path.stat().st_mtime if file_path.exists() else None
|
||||
except OSError:
|
||||
current_mtime = None
|
||||
|
||||
cached = self._memory_cache.get(agent_name)
|
||||
|
||||
if cached is None or cached[1] != current_mtime:
|
||||
memory_data = self._load_memory_from_file(agent_name)
|
||||
self._memory_cache[agent_name] = (memory_data, current_mtime)
|
||||
return memory_data
|
||||
|
||||
return cached[0]
|
||||
|
||||
def reload(self, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Reload memory data from file, forcing cache invalidation."""
|
||||
file_path = self._get_memory_file_path(agent_name)
|
||||
memory_data = self._load_memory_from_file(agent_name)
|
||||
|
||||
try:
|
||||
mtime = file_path.stat().st_mtime if file_path.exists() else None
|
||||
except OSError:
|
||||
mtime = None
|
||||
|
||||
self._memory_cache[agent_name] = (memory_data, mtime)
|
||||
return memory_data
|
||||
|
||||
def save(self, memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
|
||||
"""Save memory data to file and update cache."""
|
||||
file_path = self._get_memory_file_path(agent_name)
|
||||
|
||||
try:
|
||||
file_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
memory_data["lastUpdated"] = utc_now_iso_z()
|
||||
|
||||
temp_path = file_path.with_suffix(".tmp")
|
||||
with open(temp_path, "w", encoding="utf-8") as f:
|
||||
json.dump(memory_data, f, indent=2, ensure_ascii=False)
|
||||
|
||||
temp_path.replace(file_path)
|
||||
|
||||
try:
|
||||
mtime = file_path.stat().st_mtime
|
||||
except OSError:
|
||||
mtime = None
|
||||
|
||||
self._memory_cache[agent_name] = (memory_data, mtime)
|
||||
logger.info("Memory saved to %s", file_path)
|
||||
return True
|
||||
except OSError as e:
|
||||
logger.error("Failed to save memory file: %s", e)
|
||||
return False
|
||||
|
||||
|
||||
_storage_instance: MemoryStorage | None = None
|
||||
_storage_lock = threading.Lock()
|
||||
|
||||
|
||||
def get_memory_storage() -> MemoryStorage:
|
||||
"""Get the configured memory storage instance."""
|
||||
global _storage_instance
|
||||
if _storage_instance is not None:
|
||||
return _storage_instance
|
||||
|
||||
with _storage_lock:
|
||||
if _storage_instance is not None:
|
||||
return _storage_instance
|
||||
|
||||
config = get_memory_config()
|
||||
storage_class_path = config.storage_class
|
||||
|
||||
try:
|
||||
module_path, class_name = storage_class_path.rsplit(".", 1)
|
||||
import importlib
|
||||
|
||||
module = importlib.import_module(module_path)
|
||||
storage_class = getattr(module, class_name)
|
||||
|
||||
# Validate that the configured storage is a MemoryStorage implementation
|
||||
if not isinstance(storage_class, type):
|
||||
raise TypeError(f"Configured memory storage '{storage_class_path}' is not a class: {storage_class!r}")
|
||||
if not issubclass(storage_class, MemoryStorage):
|
||||
raise TypeError(f"Configured memory storage '{storage_class_path}' is not a subclass of MemoryStorage")
|
||||
|
||||
_storage_instance = storage_class()
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Failed to load memory storage %s, falling back to FileMemoryStorage: %s",
|
||||
storage_class_path,
|
||||
e,
|
||||
)
|
||||
_storage_instance = FileMemoryStorage()
|
||||
|
||||
return _storage_instance
|
||||
@@ -0,0 +1,472 @@
|
||||
"""Memory updater for reading, writing, and updating memory data."""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
import re
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from deerflow.agents.memory.prompt import (
|
||||
MEMORY_UPDATE_PROMPT,
|
||||
format_conversation_for_update,
|
||||
)
|
||||
from deerflow.agents.memory.storage import (
|
||||
create_empty_memory,
|
||||
get_memory_storage,
|
||||
utc_now_iso_z,
|
||||
)
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _create_empty_memory() -> dict[str, Any]:
|
||||
"""Backward-compatible wrapper around the storage-layer empty-memory factory."""
|
||||
return create_empty_memory()
|
||||
|
||||
|
||||
def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
|
||||
"""Backward-compatible wrapper around the configured memory storage save path."""
|
||||
return get_memory_storage().save(memory_data, agent_name)
|
||||
|
||||
|
||||
def get_memory_data(agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Get the current memory data via storage provider."""
|
||||
return get_memory_storage().load(agent_name)
|
||||
|
||||
|
||||
def reload_memory_data(agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Reload memory data via storage provider."""
|
||||
return get_memory_storage().reload(agent_name)
|
||||
|
||||
|
||||
def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Persist imported memory data via storage provider.
|
||||
|
||||
Args:
|
||||
memory_data: Full memory payload to persist.
|
||||
agent_name: If provided, imports into per-agent memory.
|
||||
|
||||
Returns:
|
||||
The saved memory data after storage normalization.
|
||||
|
||||
Raises:
|
||||
OSError: If persisting the imported memory fails.
|
||||
"""
|
||||
storage = get_memory_storage()
|
||||
if not storage.save(memory_data, agent_name):
|
||||
raise OSError("Failed to save imported memory data")
|
||||
return storage.load(agent_name)
|
||||
|
||||
|
||||
def clear_memory_data(agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Clear all stored memory data and persist an empty structure."""
|
||||
cleared_memory = create_empty_memory()
|
||||
if not _save_memory_to_file(cleared_memory, agent_name):
|
||||
raise OSError("Failed to save cleared memory data")
|
||||
return cleared_memory
|
||||
|
||||
|
||||
def _validate_confidence(confidence: float) -> float:
|
||||
"""Validate persisted fact confidence so stored JSON stays standards-compliant."""
|
||||
if not math.isfinite(confidence) or confidence < 0 or confidence > 1:
|
||||
raise ValueError("confidence")
|
||||
return confidence
|
||||
|
||||
|
||||
def create_memory_fact(
|
||||
content: str,
|
||||
category: str = "context",
|
||||
confidence: float = 0.5,
|
||||
agent_name: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Create a new fact and persist the updated memory data."""
|
||||
normalized_content = content.strip()
|
||||
if not normalized_content:
|
||||
raise ValueError("content")
|
||||
|
||||
normalized_category = category.strip() or "context"
|
||||
validated_confidence = _validate_confidence(confidence)
|
||||
now = utc_now_iso_z()
|
||||
memory_data = get_memory_data(agent_name)
|
||||
updated_memory = dict(memory_data)
|
||||
facts = list(memory_data.get("facts", []))
|
||||
facts.append(
|
||||
{
|
||||
"id": f"fact_{uuid.uuid4().hex[:8]}",
|
||||
"content": normalized_content,
|
||||
"category": normalized_category,
|
||||
"confidence": validated_confidence,
|
||||
"createdAt": now,
|
||||
"source": "manual",
|
||||
}
|
||||
)
|
||||
updated_memory["facts"] = facts
|
||||
|
||||
if not _save_memory_to_file(updated_memory, agent_name):
|
||||
raise OSError("Failed to save memory data after creating fact")
|
||||
|
||||
return updated_memory
|
||||
|
||||
|
||||
def delete_memory_fact(fact_id: str, agent_name: str | None = None) -> dict[str, Any]:
|
||||
"""Delete a fact by its id and persist the updated memory data."""
|
||||
memory_data = get_memory_data(agent_name)
|
||||
facts = memory_data.get("facts", [])
|
||||
updated_facts = [fact for fact in facts if fact.get("id") != fact_id]
|
||||
if len(updated_facts) == len(facts):
|
||||
raise KeyError(fact_id)
|
||||
|
||||
updated_memory = dict(memory_data)
|
||||
updated_memory["facts"] = updated_facts
|
||||
|
||||
if not _save_memory_to_file(updated_memory, agent_name):
|
||||
raise OSError(f"Failed to save memory data after deleting fact '{fact_id}'")
|
||||
|
||||
return updated_memory
|
||||
|
||||
|
||||
def update_memory_fact(
|
||||
fact_id: str,
|
||||
content: str | None = None,
|
||||
category: str | None = None,
|
||||
confidence: float | None = None,
|
||||
agent_name: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Update an existing fact and persist the updated memory data."""
|
||||
memory_data = get_memory_data(agent_name)
|
||||
updated_memory = dict(memory_data)
|
||||
updated_facts: list[dict[str, Any]] = []
|
||||
found = False
|
||||
|
||||
for fact in memory_data.get("facts", []):
|
||||
if fact.get("id") == fact_id:
|
||||
found = True
|
||||
updated_fact = dict(fact)
|
||||
if content is not None:
|
||||
normalized_content = content.strip()
|
||||
if not normalized_content:
|
||||
raise ValueError("content")
|
||||
updated_fact["content"] = normalized_content
|
||||
if category is not None:
|
||||
updated_fact["category"] = category.strip() or "context"
|
||||
if confidence is not None:
|
||||
updated_fact["confidence"] = _validate_confidence(confidence)
|
||||
updated_facts.append(updated_fact)
|
||||
else:
|
||||
updated_facts.append(fact)
|
||||
|
||||
if not found:
|
||||
raise KeyError(fact_id)
|
||||
|
||||
updated_memory["facts"] = updated_facts
|
||||
|
||||
if not _save_memory_to_file(updated_memory, agent_name):
|
||||
raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")
|
||||
|
||||
return updated_memory
|
||||
|
||||
|
||||
def _extract_text(content: Any) -> str:
|
||||
"""Extract plain text from LLM response content (str or list of content blocks).
|
||||
|
||||
Modern LLMs may return structured content as a list of blocks instead of a
|
||||
plain string, e.g. [{"type": "text", "text": "..."}]. Using str() on such
|
||||
content produces Python repr instead of the actual text, breaking JSON
|
||||
parsing downstream.
|
||||
|
||||
String chunks are concatenated without separators to avoid corrupting
|
||||
chunked JSON/text payloads. Dict-based text blocks are treated as full text
|
||||
blocks and joined with newlines for readability.
|
||||
"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
pieces: list[str] = []
|
||||
pending_str_parts: list[str] = []
|
||||
|
||||
def flush_pending_str_parts() -> None:
|
||||
if pending_str_parts:
|
||||
pieces.append("".join(pending_str_parts))
|
||||
pending_str_parts.clear()
|
||||
|
||||
for block in content:
|
||||
if isinstance(block, str):
|
||||
pending_str_parts.append(block)
|
||||
elif isinstance(block, dict):
|
||||
flush_pending_str_parts()
|
||||
text_val = block.get("text")
|
||||
if isinstance(text_val, str):
|
||||
pieces.append(text_val)
|
||||
|
||||
flush_pending_str_parts()
|
||||
return "\n".join(pieces)
|
||||
return str(content)
|
||||
|
||||
|
||||
# Matches sentences that describe a file-upload *event* rather than general
|
||||
# file-related work. Deliberately narrow to avoid removing legitimate facts
|
||||
# such as "User works with CSV files" or "prefers PDF export".
|
||||
_UPLOAD_SENTENCE_RE = re.compile(
|
||||
r"[^.!?]*\b(?:"
|
||||
r"upload(?:ed|ing)?(?:\s+\w+){0,3}\s+(?:file|files?|document|documents?|attachment|attachments?)"
|
||||
r"|file\s+upload"
|
||||
r"|/mnt/user-data/uploads/"
|
||||
r"|<uploaded_files>"
|
||||
r")[^.!?]*[.!?]?\s*",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
|
||||
|
||||
def _strip_upload_mentions_from_memory(memory_data: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Remove sentences about file uploads from all memory summaries and facts.
|
||||
|
||||
Uploaded files are session-scoped; persisting upload events in long-term
|
||||
memory causes the agent to search for non-existent files in future sessions.
|
||||
"""
|
||||
# Scrub summaries in user/history sections
|
||||
for section in ("user", "history"):
|
||||
section_data = memory_data.get(section, {})
|
||||
for _key, val in section_data.items():
|
||||
if isinstance(val, dict) and "summary" in val:
|
||||
cleaned = _UPLOAD_SENTENCE_RE.sub("", val["summary"]).strip()
|
||||
cleaned = re.sub(r" +", " ", cleaned)
|
||||
val["summary"] = cleaned
|
||||
|
||||
# Also remove any facts that describe upload events
|
||||
facts = memory_data.get("facts", [])
|
||||
if facts:
|
||||
memory_data["facts"] = [f for f in facts if not _UPLOAD_SENTENCE_RE.search(f.get("content", ""))]
|
||||
|
||||
return memory_data
|
||||
|
||||
|
||||
def _fact_content_key(content: Any) -> str | None:
|
||||
if not isinstance(content, str):
|
||||
return None
|
||||
stripped = content.strip()
|
||||
if not stripped:
|
||||
return None
|
||||
return stripped.casefold()
|
||||
|
||||
|
||||
class MemoryUpdater:
|
||||
"""Updates memory using LLM based on conversation context."""
|
||||
|
||||
def __init__(self, model_name: str | None = None):
|
||||
"""Initialize the memory updater.
|
||||
|
||||
Args:
|
||||
model_name: Optional model name to use. If None, uses config or default.
|
||||
"""
|
||||
self._model_name = model_name
|
||||
|
||||
def _get_model(self):
|
||||
"""Get the model for memory updates."""
|
||||
config = get_memory_config()
|
||||
model_name = self._model_name or config.model_name
|
||||
return create_chat_model(name=model_name, thinking_enabled=False)
|
||||
|
||||
def update_memory(
|
||||
self,
|
||||
messages: list[Any],
|
||||
thread_id: str | None = None,
|
||||
agent_name: str | None = None,
|
||||
correction_detected: bool = False,
|
||||
reinforcement_detected: bool = False,
|
||||
) -> bool:
|
||||
"""Update memory based on conversation messages.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages.
|
||||
thread_id: Optional thread ID for tracking source.
|
||||
agent_name: If provided, updates per-agent memory. If None, updates global memory.
|
||||
correction_detected: Whether recent turns include an explicit correction signal.
|
||||
reinforcement_detected: Whether recent turns include a positive reinforcement signal.
|
||||
|
||||
Returns:
|
||||
True if update was successful, False otherwise.
|
||||
"""
|
||||
config = get_memory_config()
|
||||
if not config.enabled:
|
||||
return False
|
||||
|
||||
if not messages:
|
||||
return False
|
||||
|
||||
try:
|
||||
# Get current memory
|
||||
current_memory = get_memory_data(agent_name)
|
||||
|
||||
# Format conversation for prompt
|
||||
conversation_text = format_conversation_for_update(messages)
|
||||
|
||||
if not conversation_text.strip():
|
||||
return False
|
||||
|
||||
# Build prompt
|
||||
correction_hint = ""
|
||||
if correction_detected:
|
||||
correction_hint = (
|
||||
"IMPORTANT: Explicit correction signals were detected in this conversation. "
|
||||
"Pay special attention to what the agent got wrong, what the user corrected, "
|
||||
"and record the correct approach as a fact with category "
|
||||
'"correction" and confidence >= 0.95 when appropriate.'
|
||||
)
|
||||
if reinforcement_detected:
|
||||
reinforcement_hint = (
|
||||
"IMPORTANT: Positive reinforcement signals were detected in this conversation. "
|
||||
"The user explicitly confirmed the agent's approach was correct or helpful. "
|
||||
"Record the confirmed approach, style, or preference as a fact with category "
|
||||
'"preference" or "behavior" and confidence >= 0.9 when appropriate.'
|
||||
)
|
||||
correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
|
||||
|
||||
prompt = MEMORY_UPDATE_PROMPT.format(
|
||||
current_memory=json.dumps(current_memory, indent=2),
|
||||
conversation=conversation_text,
|
||||
correction_hint=correction_hint,
|
||||
)
|
||||
|
||||
# Call LLM
|
||||
model = self._get_model()
|
||||
response = model.invoke(prompt)
|
||||
response_text = _extract_text(response.content).strip()
|
||||
|
||||
# Parse response
|
||||
# Remove markdown code blocks if present
|
||||
if response_text.startswith("```"):
|
||||
lines = response_text.split("\n")
|
||||
response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
|
||||
|
||||
update_data = json.loads(response_text)
|
||||
|
||||
# Apply updates
|
||||
updated_memory = self._apply_updates(current_memory, update_data, thread_id)
|
||||
|
||||
# Strip file-upload mentions from all summaries before saving.
|
||||
# Uploaded files are session-scoped and won't exist in future sessions,
|
||||
# so recording upload events in long-term memory causes the agent to
|
||||
# try (and fail) to locate those files in subsequent conversations.
|
||||
updated_memory = _strip_upload_mentions_from_memory(updated_memory)
|
||||
|
||||
# Save
|
||||
return get_memory_storage().save(updated_memory, agent_name)
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning("Failed to parse LLM response for memory update: %s", e)
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.exception("Memory update failed: %s", e)
|
||||
return False
|
||||
|
||||
def _apply_updates(
|
||||
self,
|
||||
current_memory: dict[str, Any],
|
||||
update_data: dict[str, Any],
|
||||
thread_id: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Apply LLM-generated updates to memory.
|
||||
|
||||
Args:
|
||||
current_memory: Current memory data.
|
||||
update_data: Updates from LLM.
|
||||
thread_id: Optional thread ID for tracking.
|
||||
|
||||
Returns:
|
||||
Updated memory data.
|
||||
"""
|
||||
config = get_memory_config()
|
||||
now = utc_now_iso_z()
|
||||
|
||||
# Update user sections
|
||||
user_updates = update_data.get("user", {})
|
||||
for section in ["workContext", "personalContext", "topOfMind"]:
|
||||
section_data = user_updates.get(section, {})
|
||||
if section_data.get("shouldUpdate") and section_data.get("summary"):
|
||||
current_memory["user"][section] = {
|
||||
"summary": section_data["summary"],
|
||||
"updatedAt": now,
|
||||
}
|
||||
|
||||
# Update history sections
|
||||
history_updates = update_data.get("history", {})
|
||||
for section in ["recentMonths", "earlierContext", "longTermBackground"]:
|
||||
section_data = history_updates.get(section, {})
|
||||
if section_data.get("shouldUpdate") and section_data.get("summary"):
|
||||
current_memory["history"][section] = {
|
||||
"summary": section_data["summary"],
|
||||
"updatedAt": now,
|
||||
}
|
||||
|
||||
# Remove facts
|
||||
facts_to_remove = set(update_data.get("factsToRemove", []))
|
||||
if facts_to_remove:
|
||||
current_memory["facts"] = [f for f in current_memory.get("facts", []) if f.get("id") not in facts_to_remove]
|
||||
|
||||
# Add new facts
|
||||
existing_fact_keys = {fact_key for fact_key in (_fact_content_key(fact.get("content")) for fact in current_memory.get("facts", [])) if fact_key is not None}
|
||||
new_facts = update_data.get("newFacts", [])
|
||||
for fact in new_facts:
|
||||
confidence = fact.get("confidence", 0.5)
|
||||
if confidence >= config.fact_confidence_threshold:
|
||||
raw_content = fact.get("content", "")
|
||||
if not isinstance(raw_content, str):
|
||||
continue
|
||||
normalized_content = raw_content.strip()
|
||||
fact_key = _fact_content_key(normalized_content)
|
||||
if fact_key is not None and fact_key in existing_fact_keys:
|
||||
continue
|
||||
|
||||
fact_entry = {
|
||||
"id": f"fact_{uuid.uuid4().hex[:8]}",
|
||||
"content": normalized_content,
|
||||
"category": fact.get("category", "context"),
|
||||
"confidence": confidence,
|
||||
"createdAt": now,
|
||||
"source": thread_id or "unknown",
|
||||
}
|
||||
source_error = fact.get("sourceError")
|
||||
if isinstance(source_error, str):
|
||||
normalized_source_error = source_error.strip()
|
||||
if normalized_source_error:
|
||||
fact_entry["sourceError"] = normalized_source_error
|
||||
current_memory["facts"].append(fact_entry)
|
||||
if fact_key is not None:
|
||||
existing_fact_keys.add(fact_key)
|
||||
|
||||
# Enforce max facts limit
|
||||
if len(current_memory["facts"]) > config.max_facts:
|
||||
# Sort by confidence and keep top ones
|
||||
current_memory["facts"] = sorted(
|
||||
current_memory["facts"],
|
||||
key=lambda f: f.get("confidence", 0),
|
||||
reverse=True,
|
||||
)[: config.max_facts]
|
||||
|
||||
return current_memory
|
||||
|
||||
|
||||
def update_memory_from_conversation(
|
||||
messages: list[Any],
|
||||
thread_id: str | None = None,
|
||||
agent_name: str | None = None,
|
||||
correction_detected: bool = False,
|
||||
reinforcement_detected: bool = False,
|
||||
) -> bool:
|
||||
"""Convenience function to update memory from a conversation.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages.
|
||||
thread_id: Optional thread ID.
|
||||
agent_name: If provided, updates per-agent memory. If None, updates global memory.
|
||||
correction_detected: Whether recent turns include an explicit correction signal.
|
||||
reinforcement_detected: Whether recent turns include a positive reinforcement signal.
|
||||
|
||||
Returns:
|
||||
True if successful, False otherwise.
|
||||
"""
|
||||
updater = MemoryUpdater()
|
||||
return updater.update_memory(messages, thread_id, agent_name, correction_detected, reinforcement_detected)
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,191 @@
|
||||
"""Middleware for intercepting clarification requests and presenting them to the user."""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langgraph.graph import END
|
||||
from langgraph.prebuilt.tool_node import ToolCallRequest
|
||||
from langgraph.types import Command
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ClarificationMiddlewareState(AgentState):
|
||||
"""Compatible with the `ThreadState` schema."""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
|
||||
"""Intercepts clarification tool calls and interrupts execution to present questions to the user.
|
||||
|
||||
When the model calls the `ask_clarification` tool, this middleware:
|
||||
1. Intercepts the tool call before execution
|
||||
2. Extracts the clarification question and metadata
|
||||
3. Formats a user-friendly message
|
||||
4. Returns a Command that interrupts execution and presents the question
|
||||
5. Waits for user response before continuing
|
||||
|
||||
This replaces the tool-based approach where clarification continued the conversation flow.
|
||||
"""
|
||||
|
||||
state_schema = ClarificationMiddlewareState
|
||||
|
||||
def _is_chinese(self, text: str) -> bool:
|
||||
"""Check if text contains Chinese characters.
|
||||
|
||||
Args:
|
||||
text: Text to check
|
||||
|
||||
Returns:
|
||||
True if text contains Chinese characters
|
||||
"""
|
||||
return any("\u4e00" <= char <= "\u9fff" for char in text)
|
||||
|
||||
def _format_clarification_message(self, args: dict) -> str:
|
||||
"""Format the clarification arguments into a user-friendly message.
|
||||
|
||||
Args:
|
||||
args: The tool call arguments containing clarification details
|
||||
|
||||
Returns:
|
||||
Formatted message string
|
||||
"""
|
||||
question = args.get("question", "")
|
||||
clarification_type = args.get("clarification_type", "missing_info")
|
||||
context = args.get("context")
|
||||
options = args.get("options", [])
|
||||
|
||||
# Some models (e.g. Qwen3-Max) serialize array parameters as JSON strings
|
||||
# instead of native arrays. Deserialize and normalize so `options`
|
||||
# is always a list for the rendering logic below.
|
||||
if isinstance(options, str):
|
||||
try:
|
||||
options = json.loads(options)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
options = [options]
|
||||
|
||||
if options is None:
|
||||
options = []
|
||||
elif not isinstance(options, list):
|
||||
options = [options]
|
||||
|
||||
# Type-specific icons
|
||||
type_icons = {
|
||||
"missing_info": "❓",
|
||||
"ambiguous_requirement": "🤔",
|
||||
"approach_choice": "🔀",
|
||||
"risk_confirmation": "⚠️",
|
||||
"suggestion": "💡",
|
||||
}
|
||||
|
||||
icon = type_icons.get(clarification_type, "❓")
|
||||
|
||||
# Build the message naturally
|
||||
message_parts = []
|
||||
|
||||
# Add icon and question together for a more natural flow
|
||||
if context:
|
||||
# If there's context, present it first as background
|
||||
message_parts.append(f"{icon} {context}")
|
||||
message_parts.append(f"\n{question}")
|
||||
else:
|
||||
# Just the question with icon
|
||||
message_parts.append(f"{icon} {question}")
|
||||
|
||||
# Add options in a cleaner format
|
||||
if options and len(options) > 0:
|
||||
message_parts.append("") # blank line for spacing
|
||||
for i, option in enumerate(options, 1):
|
||||
message_parts.append(f" {i}. {option}")
|
||||
|
||||
return "\n".join(message_parts)
|
||||
|
||||
def _handle_clarification(self, request: ToolCallRequest) -> Command:
|
||||
"""Handle clarification request and return command to interrupt execution.
|
||||
|
||||
Args:
|
||||
request: Tool call request
|
||||
|
||||
Returns:
|
||||
Command that interrupts execution with the formatted clarification message
|
||||
"""
|
||||
# Extract clarification arguments
|
||||
args = request.tool_call.get("args", {})
|
||||
question = args.get("question", "")
|
||||
|
||||
logger.info("Intercepted clarification request")
|
||||
logger.debug("Clarification question: %s", question)
|
||||
|
||||
# Format the clarification message
|
||||
formatted_message = self._format_clarification_message(args)
|
||||
|
||||
# Get the tool call ID
|
||||
tool_call_id = request.tool_call.get("id", "")
|
||||
|
||||
# Create a ToolMessage with the formatted question
|
||||
# This will be added to the message history
|
||||
tool_message = ToolMessage(
|
||||
content=formatted_message,
|
||||
tool_call_id=tool_call_id,
|
||||
name="ask_clarification",
|
||||
)
|
||||
|
||||
# Return a Command that:
|
||||
# 1. Adds the formatted tool message
|
||||
# 2. Interrupts execution by going to __end__
|
||||
# Note: We don't add an extra AIMessage here - the frontend will detect
|
||||
# and display ask_clarification tool messages directly
|
||||
return Command(
|
||||
update={"messages": [tool_message]},
|
||||
goto=END,
|
||||
)
|
||||
|
||||
@override
|
||||
def wrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], ToolMessage | Command],
|
||||
) -> ToolMessage | Command:
|
||||
"""Intercept ask_clarification tool calls and interrupt execution (sync version).
|
||||
|
||||
Args:
|
||||
request: Tool call request
|
||||
handler: Original tool execution handler
|
||||
|
||||
Returns:
|
||||
Command that interrupts execution with the formatted clarification message
|
||||
"""
|
||||
# Check if this is an ask_clarification tool call
|
||||
if request.tool_call.get("name") != "ask_clarification":
|
||||
# Not a clarification call, execute normally
|
||||
return handler(request)
|
||||
|
||||
return self._handle_clarification(request)
|
||||
|
||||
@override
|
||||
async def awrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], ToolMessage | Command],
|
||||
) -> ToolMessage | Command:
|
||||
"""Intercept ask_clarification tool calls and interrupt execution (async version).
|
||||
|
||||
Args:
|
||||
request: Tool call request
|
||||
handler: Original tool execution handler (async)
|
||||
|
||||
Returns:
|
||||
Command that interrupts execution with the formatted clarification message
|
||||
"""
|
||||
# Check if this is an ask_clarification tool call
|
||||
if request.tool_call.get("name") != "ask_clarification":
|
||||
# Not a clarification call, execute normally
|
||||
return await handler(request)
|
||||
|
||||
return self._handle_clarification(request)
|
||||
@@ -0,0 +1,110 @@
|
||||
"""Middleware to fix dangling tool calls in message history.
|
||||
|
||||
A dangling tool call occurs when an AIMessage contains tool_calls but there are
|
||||
no corresponding ToolMessages in the history (e.g., due to user interruption or
|
||||
request cancellation). This causes LLM errors due to incomplete message format.
|
||||
|
||||
This middleware intercepts the model call to detect and patch such gaps by
|
||||
inserting synthetic ToolMessages with an error indicator immediately after the
|
||||
AIMessage that made the tool calls, ensuring correct message ordering.
|
||||
|
||||
Note: Uses wrap_model_call instead of before_model to ensure patches are inserted
|
||||
at the correct positions (immediately after each dangling AIMessage), not appended
|
||||
to the end of the message list as before_model + add_messages reducer would do.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
|
||||
from langchain_core.messages import ToolMessage
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Inserts placeholder ToolMessages for dangling tool calls before model invocation.
|
||||
|
||||
Scans the message history for AIMessages whose tool_calls lack corresponding
|
||||
ToolMessages, and injects synthetic error responses immediately after the
|
||||
offending AIMessage so the LLM receives a well-formed conversation.
|
||||
"""
|
||||
|
||||
def _build_patched_messages(self, messages: list) -> list | None:
|
||||
"""Return a new message list with patches inserted at the correct positions.
|
||||
|
||||
For each AIMessage with dangling tool_calls (no corresponding ToolMessage),
|
||||
a synthetic ToolMessage is inserted immediately after that AIMessage.
|
||||
Returns None if no patches are needed.
|
||||
"""
|
||||
# Collect IDs of all existing ToolMessages
|
||||
existing_tool_msg_ids: set[str] = set()
|
||||
for msg in messages:
|
||||
if isinstance(msg, ToolMessage):
|
||||
existing_tool_msg_ids.add(msg.tool_call_id)
|
||||
|
||||
# Check if any patching is needed
|
||||
needs_patch = False
|
||||
for msg in messages:
|
||||
if getattr(msg, "type", None) != "ai":
|
||||
continue
|
||||
for tc in getattr(msg, "tool_calls", None) or []:
|
||||
tc_id = tc.get("id")
|
||||
if tc_id and tc_id not in existing_tool_msg_ids:
|
||||
needs_patch = True
|
||||
break
|
||||
if needs_patch:
|
||||
break
|
||||
|
||||
if not needs_patch:
|
||||
return None
|
||||
|
||||
# Build new list with patches inserted right after each dangling AIMessage
|
||||
patched: list = []
|
||||
patched_ids: set[str] = set()
|
||||
patch_count = 0
|
||||
for msg in messages:
|
||||
patched.append(msg)
|
||||
if getattr(msg, "type", None) != "ai":
|
||||
continue
|
||||
for tc in getattr(msg, "tool_calls", None) or []:
|
||||
tc_id = tc.get("id")
|
||||
if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
|
||||
patched.append(
|
||||
ToolMessage(
|
||||
content="[Tool call was interrupted and did not return a result.]",
|
||||
tool_call_id=tc_id,
|
||||
name=tc.get("name", "unknown"),
|
||||
status="error",
|
||||
)
|
||||
)
|
||||
patched_ids.add(tc_id)
|
||||
patch_count += 1
|
||||
|
||||
logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
|
||||
return patched
|
||||
|
||||
@override
|
||||
def wrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], ModelResponse],
|
||||
) -> ModelCallResult:
|
||||
patched = self._build_patched_messages(request.messages)
|
||||
if patched is not None:
|
||||
request = request.override(messages=patched)
|
||||
return handler(request)
|
||||
|
||||
@override
|
||||
async def awrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
|
||||
) -> ModelCallResult:
|
||||
patched = self._build_patched_messages(request.messages)
|
||||
if patched is not None:
|
||||
request = request.override(messages=patched)
|
||||
return await handler(request)
|
||||
@@ -0,0 +1,60 @@
|
||||
"""Middleware to filter deferred tool schemas from model binding.
|
||||
|
||||
When tool_search is enabled, MCP tools are registered in the DeferredToolRegistry
|
||||
and passed to ToolNode for execution, but their schemas should NOT be sent to the
|
||||
LLM via bind_tools (that's the whole point of deferral — saving context tokens).
|
||||
|
||||
This middleware intercepts wrap_model_call and removes deferred tools from
|
||||
request.tools so that model.bind_tools only receives active tool schemas.
|
||||
The agent discovers deferred tools at runtime via the tool_search tool.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Remove deferred tools from request.tools before model binding.
|
||||
|
||||
ToolNode still holds all tools (including deferred) for execution routing,
|
||||
but the LLM only sees active tool schemas — deferred tools are discoverable
|
||||
via tool_search at runtime.
|
||||
"""
|
||||
|
||||
def _filter_tools(self, request: ModelRequest) -> ModelRequest:
|
||||
from deerflow.tools.builtins.tool_search import get_deferred_registry
|
||||
|
||||
registry = get_deferred_registry()
|
||||
if not registry:
|
||||
return request
|
||||
|
||||
deferred_names = {e.name for e in registry.entries}
|
||||
active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
|
||||
|
||||
if len(active_tools) < len(request.tools):
|
||||
logger.debug(f"Filtered {len(request.tools) - len(active_tools)} deferred tool schema(s) from model binding")
|
||||
|
||||
return request.override(tools=active_tools)
|
||||
|
||||
@override
|
||||
def wrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], ModelResponse],
|
||||
) -> ModelCallResult:
|
||||
return handler(self._filter_tools(request))
|
||||
|
||||
@override
|
||||
async def awrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
|
||||
) -> ModelCallResult:
|
||||
return await handler(self._filter_tools(request))
|
||||
@@ -0,0 +1,275 @@
|
||||
"""LLM error handling middleware with retry/backoff and user-facing fallbacks."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
from collections.abc import Awaitable, Callable
|
||||
from email.utils import parsedate_to_datetime
|
||||
from typing import Any, override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain.agents.middleware.types import (
|
||||
ModelCallResult,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
)
|
||||
from langchain_core.messages import AIMessage
|
||||
from langgraph.errors import GraphBubbleUp
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_RETRIABLE_STATUS_CODES = {408, 409, 425, 429, 500, 502, 503, 504}
|
||||
_BUSY_PATTERNS = (
|
||||
"server busy",
|
||||
"temporarily unavailable",
|
||||
"try again later",
|
||||
"please retry",
|
||||
"please try again",
|
||||
"overloaded",
|
||||
"high demand",
|
||||
"rate limit",
|
||||
"负载较高",
|
||||
"服务繁忙",
|
||||
"稍后重试",
|
||||
"请稍后重试",
|
||||
)
|
||||
_QUOTA_PATTERNS = (
|
||||
"insufficient_quota",
|
||||
"quota",
|
||||
"billing",
|
||||
"credit",
|
||||
"payment",
|
||||
"余额不足",
|
||||
"超出限额",
|
||||
"额度不足",
|
||||
"欠费",
|
||||
)
|
||||
_AUTH_PATTERNS = (
|
||||
"authentication",
|
||||
"unauthorized",
|
||||
"invalid api key",
|
||||
"invalid_api_key",
|
||||
"permission",
|
||||
"forbidden",
|
||||
"access denied",
|
||||
"无权",
|
||||
"未授权",
|
||||
)
|
||||
|
||||
|
||||
class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Retry transient LLM errors and surface graceful assistant messages."""
|
||||
|
||||
retry_max_attempts: int = 3
|
||||
retry_base_delay_ms: int = 1000
|
||||
retry_cap_delay_ms: int = 8000
|
||||
|
||||
def _classify_error(self, exc: BaseException) -> tuple[bool, str]:
|
||||
detail = _extract_error_detail(exc)
|
||||
lowered = detail.lower()
|
||||
error_code = _extract_error_code(exc)
|
||||
status_code = _extract_status_code(exc)
|
||||
|
||||
if _matches_any(lowered, _QUOTA_PATTERNS) or _matches_any(str(error_code).lower(), _QUOTA_PATTERNS):
|
||||
return False, "quota"
|
||||
if _matches_any(lowered, _AUTH_PATTERNS):
|
||||
return False, "auth"
|
||||
|
||||
exc_name = exc.__class__.__name__
|
||||
if exc_name in {
|
||||
"APITimeoutError",
|
||||
"APIConnectionError",
|
||||
"InternalServerError",
|
||||
}:
|
||||
return True, "transient"
|
||||
if status_code in _RETRIABLE_STATUS_CODES:
|
||||
return True, "transient"
|
||||
if _matches_any(lowered, _BUSY_PATTERNS):
|
||||
return True, "busy"
|
||||
|
||||
return False, "generic"
|
||||
|
||||
def _build_retry_delay_ms(self, attempt: int, exc: BaseException) -> int:
|
||||
retry_after = _extract_retry_after_ms(exc)
|
||||
if retry_after is not None:
|
||||
return retry_after
|
||||
backoff = self.retry_base_delay_ms * (2 ** max(0, attempt - 1))
|
||||
return min(backoff, self.retry_cap_delay_ms)
|
||||
|
||||
def _build_retry_message(self, attempt: int, wait_ms: int, reason: str) -> str:
|
||||
seconds = max(1, round(wait_ms / 1000))
|
||||
reason_text = "provider is busy" if reason == "busy" else "provider request failed temporarily"
|
||||
return f"LLM request retry {attempt}/{self.retry_max_attempts}: {reason_text}. Retrying in {seconds}s."
|
||||
|
||||
def _build_user_message(self, exc: BaseException, reason: str) -> str:
|
||||
detail = _extract_error_detail(exc)
|
||||
if reason == "quota":
|
||||
return "The configured LLM provider rejected the request because the account is out of quota, billing is unavailable, or usage is restricted. Please fix the provider account and try again."
|
||||
if reason == "auth":
|
||||
return "The configured LLM provider rejected the request because authentication or access is invalid. Please check the provider credentials and try again."
|
||||
if reason in {"busy", "transient"}:
|
||||
return "The configured LLM provider is temporarily unavailable after multiple retries. Please wait a moment and continue the conversation."
|
||||
return f"LLM request failed: {detail}"
|
||||
|
||||
def _emit_retry_event(self, attempt: int, wait_ms: int, reason: str) -> None:
|
||||
try:
|
||||
from langgraph.config import get_stream_writer
|
||||
|
||||
writer = get_stream_writer()
|
||||
writer(
|
||||
{
|
||||
"type": "llm_retry",
|
||||
"attempt": attempt,
|
||||
"max_attempts": self.retry_max_attempts,
|
||||
"wait_ms": wait_ms,
|
||||
"reason": reason,
|
||||
"message": self._build_retry_message(attempt, wait_ms, reason),
|
||||
}
|
||||
)
|
||||
except Exception:
|
||||
logger.debug("Failed to emit llm_retry event", exc_info=True)
|
||||
|
||||
@override
|
||||
def wrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], ModelResponse],
|
||||
) -> ModelCallResult:
|
||||
attempt = 1
|
||||
while True:
|
||||
try:
|
||||
return handler(request)
|
||||
except GraphBubbleUp:
|
||||
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
|
||||
raise
|
||||
except Exception as exc:
|
||||
retriable, reason = self._classify_error(exc)
|
||||
if retriable and attempt < self.retry_max_attempts:
|
||||
wait_ms = self._build_retry_delay_ms(attempt, exc)
|
||||
logger.warning(
|
||||
"Transient LLM error on attempt %d/%d; retrying in %dms: %s",
|
||||
attempt,
|
||||
self.retry_max_attempts,
|
||||
wait_ms,
|
||||
_extract_error_detail(exc),
|
||||
)
|
||||
self._emit_retry_event(attempt, wait_ms, reason)
|
||||
time.sleep(wait_ms / 1000)
|
||||
attempt += 1
|
||||
continue
|
||||
logger.warning(
|
||||
"LLM call failed after %d attempt(s): %s",
|
||||
attempt,
|
||||
_extract_error_detail(exc),
|
||||
exc_info=exc,
|
||||
)
|
||||
return AIMessage(content=self._build_user_message(exc, reason))
|
||||
|
||||
@override
|
||||
async def awrap_model_call(
|
||||
self,
|
||||
request: ModelRequest,
|
||||
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
|
||||
) -> ModelCallResult:
|
||||
attempt = 1
|
||||
while True:
|
||||
try:
|
||||
return await handler(request)
|
||||
except GraphBubbleUp:
|
||||
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
|
||||
raise
|
||||
except Exception as exc:
|
||||
retriable, reason = self._classify_error(exc)
|
||||
if retriable and attempt < self.retry_max_attempts:
|
||||
wait_ms = self._build_retry_delay_ms(attempt, exc)
|
||||
logger.warning(
|
||||
"Transient LLM error on attempt %d/%d; retrying in %dms: %s",
|
||||
attempt,
|
||||
self.retry_max_attempts,
|
||||
wait_ms,
|
||||
_extract_error_detail(exc),
|
||||
)
|
||||
self._emit_retry_event(attempt, wait_ms, reason)
|
||||
await asyncio.sleep(wait_ms / 1000)
|
||||
attempt += 1
|
||||
continue
|
||||
logger.warning(
|
||||
"LLM call failed after %d attempt(s): %s",
|
||||
attempt,
|
||||
_extract_error_detail(exc),
|
||||
exc_info=exc,
|
||||
)
|
||||
return AIMessage(content=self._build_user_message(exc, reason))
|
||||
|
||||
|
||||
def _matches_any(detail: str, patterns: tuple[str, ...]) -> bool:
|
||||
return any(pattern in detail for pattern in patterns)
|
||||
|
||||
|
||||
def _extract_error_code(exc: BaseException) -> Any:
|
||||
for attr in ("code", "error_code"):
|
||||
value = getattr(exc, attr, None)
|
||||
if value not in (None, ""):
|
||||
return value
|
||||
|
||||
body = getattr(exc, "body", None)
|
||||
if isinstance(body, dict):
|
||||
error = body.get("error")
|
||||
if isinstance(error, dict):
|
||||
for key in ("code", "type"):
|
||||
value = error.get(key)
|
||||
if value not in (None, ""):
|
||||
return value
|
||||
return None
|
||||
|
||||
|
||||
def _extract_status_code(exc: BaseException) -> int | None:
|
||||
for attr in ("status_code", "status"):
|
||||
value = getattr(exc, attr, None)
|
||||
if isinstance(value, int):
|
||||
return value
|
||||
response = getattr(exc, "response", None)
|
||||
status = getattr(response, "status_code", None)
|
||||
return status if isinstance(status, int) else None
|
||||
|
||||
|
||||
def _extract_retry_after_ms(exc: BaseException) -> int | None:
|
||||
response = getattr(exc, "response", None)
|
||||
headers = getattr(response, "headers", None)
|
||||
if headers is None:
|
||||
return None
|
||||
|
||||
raw = None
|
||||
header_name = ""
|
||||
for key in ("retry-after-ms", "Retry-After-Ms", "retry-after", "Retry-After"):
|
||||
header_name = key
|
||||
if hasattr(headers, "get"):
|
||||
raw = headers.get(key)
|
||||
if raw:
|
||||
break
|
||||
if not raw:
|
||||
return None
|
||||
|
||||
try:
|
||||
multiplier = 1 if "ms" in header_name.lower() else 1000
|
||||
return max(0, int(float(raw) * multiplier))
|
||||
except (TypeError, ValueError):
|
||||
try:
|
||||
target = parsedate_to_datetime(str(raw))
|
||||
delta = target.timestamp() - time.time()
|
||||
return max(0, int(delta * 1000))
|
||||
except (TypeError, ValueError, OverflowError):
|
||||
return None
|
||||
|
||||
|
||||
def _extract_error_detail(exc: BaseException) -> str:
|
||||
detail = str(exc).strip()
|
||||
if detail:
|
||||
return detail
|
||||
message = getattr(exc, "message", None)
|
||||
if isinstance(message, str) and message.strip():
|
||||
return message.strip()
|
||||
return exc.__class__.__name__
|
||||
@@ -0,0 +1,372 @@
|
||||
"""Middleware to detect and break repetitive tool call loops.
|
||||
|
||||
P0 safety: prevents the agent from calling the same tool with the same
|
||||
arguments indefinitely until the recursion limit kills the run.
|
||||
|
||||
Detection strategy:
|
||||
1. After each model response, hash the tool calls (name + args).
|
||||
2. Track recent hashes in a sliding window.
|
||||
3. If the same hash appears >= warn_threshold times, inject a
|
||||
"you are repeating yourself — wrap up" system message (once per hash).
|
||||
4. If it appears >= hard_limit times, strip all tool_calls from the
|
||||
response so the agent is forced to produce a final text answer.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
from collections import OrderedDict, defaultdict
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Defaults — can be overridden via constructor
|
||||
_DEFAULT_WARN_THRESHOLD = 3 # inject warning after 3 identical calls
|
||||
_DEFAULT_HARD_LIMIT = 5 # force-stop after 5 identical calls
|
||||
_DEFAULT_WINDOW_SIZE = 20 # track last N tool calls
|
||||
_DEFAULT_MAX_TRACKED_THREADS = 100 # LRU eviction limit
|
||||
_DEFAULT_TOOL_FREQ_WARN = 30 # warn after 30 calls to the same tool type
|
||||
_DEFAULT_TOOL_FREQ_HARD_LIMIT = 50 # force-stop after 50 calls to the same tool type
|
||||
|
||||
|
||||
def _normalize_tool_call_args(raw_args: object) -> tuple[dict, str | None]:
|
||||
"""Normalize tool call args to a dict plus an optional fallback key.
|
||||
|
||||
Some providers serialize ``args`` as a JSON string instead of a dict.
|
||||
We defensively parse those cases so loop detection does not crash while
|
||||
still preserving a stable fallback key for non-dict payloads.
|
||||
"""
|
||||
if isinstance(raw_args, dict):
|
||||
return raw_args, None
|
||||
|
||||
if isinstance(raw_args, str):
|
||||
try:
|
||||
parsed = json.loads(raw_args)
|
||||
except (TypeError, ValueError, json.JSONDecodeError):
|
||||
return {}, raw_args
|
||||
|
||||
if isinstance(parsed, dict):
|
||||
return parsed, None
|
||||
return {}, json.dumps(parsed, sort_keys=True, default=str)
|
||||
|
||||
if raw_args is None:
|
||||
return {}, None
|
||||
|
||||
return {}, json.dumps(raw_args, sort_keys=True, default=str)
|
||||
|
||||
|
||||
def _stable_tool_key(name: str, args: dict, fallback_key: str | None) -> str:
|
||||
"""Derive a stable key from salient args without overfitting to noise."""
|
||||
if name == "read_file" and fallback_key is None:
|
||||
path = args.get("path") or ""
|
||||
start_line = args.get("start_line")
|
||||
end_line = args.get("end_line")
|
||||
|
||||
bucket_size = 200
|
||||
try:
|
||||
start_line = int(start_line) if start_line is not None else 1
|
||||
except (TypeError, ValueError):
|
||||
start_line = 1
|
||||
try:
|
||||
end_line = int(end_line) if end_line is not None else start_line
|
||||
except (TypeError, ValueError):
|
||||
end_line = start_line
|
||||
|
||||
start_line, end_line = sorted((start_line, end_line))
|
||||
bucket_start = max(start_line, 1)
|
||||
bucket_end = max(end_line, 1)
|
||||
bucket_start = (bucket_start - 1) // bucket_size
|
||||
bucket_end = (bucket_end - 1) // bucket_size
|
||||
return f"{path}:{bucket_start}-{bucket_end}"
|
||||
|
||||
# write_file / str_replace are content-sensitive: same path may be updated
|
||||
# with different payloads during iteration. Using only salient fields (path)
|
||||
# can collapse distinct calls, so we hash full args to reduce false positives.
|
||||
if name in {"write_file", "str_replace"}:
|
||||
if fallback_key is not None:
|
||||
return fallback_key
|
||||
return json.dumps(args, sort_keys=True, default=str)
|
||||
|
||||
salient_fields = ("path", "url", "query", "command", "pattern", "glob", "cmd")
|
||||
stable_args = {field: args[field] for field in salient_fields if args.get(field) is not None}
|
||||
if stable_args:
|
||||
return json.dumps(stable_args, sort_keys=True, default=str)
|
||||
|
||||
if fallback_key is not None:
|
||||
return fallback_key
|
||||
|
||||
return json.dumps(args, sort_keys=True, default=str)
|
||||
|
||||
|
||||
def _hash_tool_calls(tool_calls: list[dict]) -> str:
|
||||
"""Deterministic hash of a set of tool calls (name + stable key).
|
||||
|
||||
This is intended to be order-independent: the same multiset of tool calls
|
||||
should always produce the same hash, regardless of their input order.
|
||||
"""
|
||||
# Normalize each tool call to a stable (name, key) structure.
|
||||
normalized: list[str] = []
|
||||
for tc in tool_calls:
|
||||
name = tc.get("name", "")
|
||||
args, fallback_key = _normalize_tool_call_args(tc.get("args", {}))
|
||||
key = _stable_tool_key(name, args, fallback_key)
|
||||
|
||||
normalized.append(f"{name}:{key}")
|
||||
|
||||
# Sort so permutations of the same multiset of calls yield the same ordering.
|
||||
normalized.sort()
|
||||
blob = json.dumps(normalized, sort_keys=True, default=str)
|
||||
return hashlib.md5(blob.encode()).hexdigest()[:12]
|
||||
|
||||
|
||||
_WARNING_MSG = "[LOOP DETECTED] You are repeating the same tool calls. Stop calling tools and produce your final answer now. If you cannot complete the task, summarize what you accomplished so far."
|
||||
|
||||
_TOOL_FREQ_WARNING_MSG = (
|
||||
"[LOOP DETECTED] You have called {tool_name} {count} times without producing a final answer. Stop calling tools and produce your final answer now. If you cannot complete the task, summarize what you accomplished so far."
|
||||
)
|
||||
|
||||
_HARD_STOP_MSG = "[FORCED STOP] Repeated tool calls exceeded the safety limit. Producing final answer with results collected so far."
|
||||
|
||||
_TOOL_FREQ_HARD_STOP_MSG = "[FORCED STOP] Tool {tool_name} called {count} times — exceeded the per-tool safety limit. Producing final answer with results collected so far."
|
||||
|
||||
|
||||
class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Detects and breaks repetitive tool call loops.
|
||||
|
||||
Args:
|
||||
warn_threshold: Number of identical tool call sets before injecting
|
||||
a warning message. Default: 3.
|
||||
hard_limit: Number of identical tool call sets before stripping
|
||||
tool_calls entirely. Default: 5.
|
||||
window_size: Size of the sliding window for tracking calls.
|
||||
Default: 20.
|
||||
max_tracked_threads: Maximum number of threads to track before
|
||||
evicting the least recently used. Default: 100.
|
||||
tool_freq_warn: Number of calls to the same tool *type* (regardless
|
||||
of arguments) before injecting a frequency warning. Catches
|
||||
cross-file read loops that hash-based detection misses.
|
||||
Default: 30.
|
||||
tool_freq_hard_limit: Number of calls to the same tool type before
|
||||
forcing a stop. Default: 50.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
warn_threshold: int = _DEFAULT_WARN_THRESHOLD,
|
||||
hard_limit: int = _DEFAULT_HARD_LIMIT,
|
||||
window_size: int = _DEFAULT_WINDOW_SIZE,
|
||||
max_tracked_threads: int = _DEFAULT_MAX_TRACKED_THREADS,
|
||||
tool_freq_warn: int = _DEFAULT_TOOL_FREQ_WARN,
|
||||
tool_freq_hard_limit: int = _DEFAULT_TOOL_FREQ_HARD_LIMIT,
|
||||
):
|
||||
super().__init__()
|
||||
self.warn_threshold = warn_threshold
|
||||
self.hard_limit = hard_limit
|
||||
self.window_size = window_size
|
||||
self.max_tracked_threads = max_tracked_threads
|
||||
self.tool_freq_warn = tool_freq_warn
|
||||
self.tool_freq_hard_limit = tool_freq_hard_limit
|
||||
self._lock = threading.Lock()
|
||||
# Per-thread tracking using OrderedDict for LRU eviction
|
||||
self._history: OrderedDict[str, list[str]] = OrderedDict()
|
||||
self._warned: dict[str, set[str]] = defaultdict(set)
|
||||
# Per-thread, per-tool-type cumulative call counts
|
||||
self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
|
||||
self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)
|
||||
|
||||
def _get_thread_id(self, runtime: Runtime) -> str:
|
||||
"""Extract thread_id from runtime context for per-thread tracking."""
|
||||
thread_id = runtime.context.get("thread_id") if runtime.context else None
|
||||
if thread_id:
|
||||
return thread_id
|
||||
return "default"
|
||||
|
||||
def _evict_if_needed(self) -> None:
|
||||
"""Evict least recently used threads if over the limit.
|
||||
|
||||
Must be called while holding self._lock.
|
||||
"""
|
||||
while len(self._history) > self.max_tracked_threads:
|
||||
evicted_id, _ = self._history.popitem(last=False)
|
||||
self._warned.pop(evicted_id, None)
|
||||
self._tool_freq.pop(evicted_id, None)
|
||||
self._tool_freq_warned.pop(evicted_id, None)
|
||||
logger.debug("Evicted loop tracking for thread %s (LRU)", evicted_id)
|
||||
|
||||
def _track_and_check(self, state: AgentState, runtime: Runtime) -> tuple[str | None, bool]:
|
||||
"""Track tool calls and check for loops.
|
||||
|
||||
Two detection layers:
|
||||
1. **Hash-based** (existing): catches identical tool call sets.
|
||||
2. **Frequency-based** (new): catches the same *tool type* being
|
||||
called many times with varying arguments (e.g. ``read_file``
|
||||
on 40 different files).
|
||||
|
||||
Returns:
|
||||
(warning_message_or_none, should_hard_stop)
|
||||
"""
|
||||
messages = state.get("messages", [])
|
||||
if not messages:
|
||||
return None, False
|
||||
|
||||
last_msg = messages[-1]
|
||||
if getattr(last_msg, "type", None) != "ai":
|
||||
return None, False
|
||||
|
||||
tool_calls = getattr(last_msg, "tool_calls", None)
|
||||
if not tool_calls:
|
||||
return None, False
|
||||
|
||||
thread_id = self._get_thread_id(runtime)
|
||||
call_hash = _hash_tool_calls(tool_calls)
|
||||
|
||||
with self._lock:
|
||||
# Touch / create entry (move to end for LRU)
|
||||
if thread_id in self._history:
|
||||
self._history.move_to_end(thread_id)
|
||||
else:
|
||||
self._history[thread_id] = []
|
||||
self._evict_if_needed()
|
||||
|
||||
history = self._history[thread_id]
|
||||
history.append(call_hash)
|
||||
if len(history) > self.window_size:
|
||||
history[:] = history[-self.window_size :]
|
||||
|
||||
count = history.count(call_hash)
|
||||
tool_names = [tc.get("name", "?") for tc in tool_calls]
|
||||
|
||||
# --- Layer 1: hash-based (identical call sets) ---
|
||||
if count >= self.hard_limit:
|
||||
logger.error(
|
||||
"Loop hard limit reached — forcing stop",
|
||||
extra={
|
||||
"thread_id": thread_id,
|
||||
"call_hash": call_hash,
|
||||
"count": count,
|
||||
"tools": tool_names,
|
||||
},
|
||||
)
|
||||
return _HARD_STOP_MSG, True
|
||||
|
||||
if count >= self.warn_threshold:
|
||||
warned = self._warned[thread_id]
|
||||
if call_hash not in warned:
|
||||
warned.add(call_hash)
|
||||
logger.warning(
|
||||
"Repetitive tool calls detected — injecting warning",
|
||||
extra={
|
||||
"thread_id": thread_id,
|
||||
"call_hash": call_hash,
|
||||
"count": count,
|
||||
"tools": tool_names,
|
||||
},
|
||||
)
|
||||
return _WARNING_MSG, False
|
||||
|
||||
# --- Layer 2: per-tool-type frequency ---
|
||||
freq = self._tool_freq[thread_id]
|
||||
for tc in tool_calls:
|
||||
name = tc.get("name", "")
|
||||
if not name:
|
||||
continue
|
||||
freq[name] += 1
|
||||
tc_count = freq[name]
|
||||
|
||||
if tc_count >= self.tool_freq_hard_limit:
|
||||
logger.error(
|
||||
"Tool frequency hard limit reached — forcing stop",
|
||||
extra={
|
||||
"thread_id": thread_id,
|
||||
"tool_name": name,
|
||||
"count": tc_count,
|
||||
},
|
||||
)
|
||||
return _TOOL_FREQ_HARD_STOP_MSG.format(tool_name=name, count=tc_count), True
|
||||
|
||||
if tc_count >= self.tool_freq_warn:
|
||||
warned = self._tool_freq_warned[thread_id]
|
||||
if name not in warned:
|
||||
warned.add(name)
|
||||
logger.warning(
|
||||
"Tool frequency warning — too many calls to same tool type",
|
||||
extra={
|
||||
"thread_id": thread_id,
|
||||
"tool_name": name,
|
||||
"count": tc_count,
|
||||
},
|
||||
)
|
||||
return _TOOL_FREQ_WARNING_MSG.format(tool_name=name, count=tc_count), False
|
||||
|
||||
return None, False
|
||||
|
||||
@staticmethod
|
||||
def _append_text(content: str | list | None, text: str) -> str | list:
|
||||
"""Append *text* to AIMessage content, handling str, list, and None.
|
||||
|
||||
When content is a list of content blocks (e.g. Anthropic thinking mode),
|
||||
we append a new ``{"type": "text", ...}`` block instead of concatenating
|
||||
a string to a list, which would raise ``TypeError``.
|
||||
"""
|
||||
if content is None:
|
||||
return text
|
||||
if isinstance(content, list):
|
||||
return [*content, {"type": "text", "text": f"\n\n{text}"}]
|
||||
if isinstance(content, str):
|
||||
return content + f"\n\n{text}"
|
||||
# Fallback: coerce unexpected types to str to avoid TypeError
|
||||
return str(content) + f"\n\n{text}"
|
||||
|
||||
def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
warning, hard_stop = self._track_and_check(state, runtime)
|
||||
|
||||
if hard_stop:
|
||||
# Strip tool_calls from the last AIMessage to force text output
|
||||
messages = state.get("messages", [])
|
||||
last_msg = messages[-1]
|
||||
stripped_msg = last_msg.model_copy(
|
||||
update={
|
||||
"tool_calls": [],
|
||||
"content": self._append_text(last_msg.content, warning),
|
||||
}
|
||||
)
|
||||
return {"messages": [stripped_msg]}
|
||||
|
||||
if warning:
|
||||
# Inject as HumanMessage instead of SystemMessage to avoid
|
||||
# Anthropic's "multiple non-consecutive system messages" error.
|
||||
# Anthropic models require system messages only at the start of
|
||||
# the conversation; injecting one mid-conversation crashes
|
||||
# langchain_anthropic's _format_messages(). HumanMessage works
|
||||
# with all providers. See #1299.
|
||||
return {"messages": [HumanMessage(content=warning)]}
|
||||
|
||||
return None
|
||||
|
||||
@override
|
||||
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._apply(state, runtime)
|
||||
|
||||
@override
|
||||
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._apply(state, runtime)
|
||||
|
||||
def reset(self, thread_id: str | None = None) -> None:
|
||||
"""Clear tracking state. If thread_id given, clear only that thread."""
|
||||
with self._lock:
|
||||
if thread_id:
|
||||
self._history.pop(thread_id, None)
|
||||
self._warned.pop(thread_id, None)
|
||||
self._tool_freq.pop(thread_id, None)
|
||||
self._tool_freq_warned.pop(thread_id, None)
|
||||
else:
|
||||
self._history.clear()
|
||||
self._warned.clear()
|
||||
self._tool_freq.clear()
|
||||
self._tool_freq_warned.clear()
|
||||
@@ -0,0 +1,248 @@
|
||||
"""Middleware for memory mechanism."""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from typing import Any, override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langgraph.config import get_config
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
from deerflow.agents.memory.queue import get_memory_queue
|
||||
from deerflow.config.memory_config import get_memory_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
|
||||
_CORRECTION_PATTERNS = (
|
||||
re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
|
||||
re.compile(r"\byou misunderstood\b", re.IGNORECASE),
|
||||
re.compile(r"\btry again\b", re.IGNORECASE),
|
||||
re.compile(r"\bredo\b", re.IGNORECASE),
|
||||
re.compile(r"不对"),
|
||||
re.compile(r"你理解错了"),
|
||||
re.compile(r"你理解有误"),
|
||||
re.compile(r"重试"),
|
||||
re.compile(r"重新来"),
|
||||
re.compile(r"换一种"),
|
||||
re.compile(r"改用"),
|
||||
)
|
||||
|
||||
_REINFORCEMENT_PATTERNS = (
|
||||
re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
|
||||
re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
|
||||
re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
|
||||
re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
|
||||
re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
|
||||
re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
|
||||
re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
|
||||
re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
|
||||
re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
|
||||
re.compile(r"完全正确(?:[。！？!?.]|$)"),
|
||||
re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
|
||||
re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
|
||||
re.compile(r"继续保持(?:[。！？!?.]|$)"),
|
||||
)
|
||||
|
||||
|
||||
class MemoryMiddlewareState(AgentState):
|
||||
"""Compatible with the `ThreadState` schema."""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def _extract_message_text(message: Any) -> str:
|
||||
"""Extract plain text from message content for filtering and signal detection."""
|
||||
content = getattr(message, "content", "")
|
||||
if isinstance(content, list):
|
||||
text_parts: list[str] = []
|
||||
for part in content:
|
||||
if isinstance(part, str):
|
||||
text_parts.append(part)
|
||||
elif isinstance(part, dict):
|
||||
text_val = part.get("text")
|
||||
if isinstance(text_val, str):
|
||||
text_parts.append(text_val)
|
||||
return " ".join(text_parts)
|
||||
return str(content)
|
||||
|
||||
|
||||
def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
|
||||
"""Filter messages to keep only user inputs and final assistant responses.
|
||||
|
||||
This filters out:
|
||||
- Tool messages (intermediate tool call results)
|
||||
- AI messages with tool_calls (intermediate steps, not final responses)
|
||||
- The <uploaded_files> block injected by UploadsMiddleware into human messages
|
||||
(file paths are session-scoped and must not persist in long-term memory).
|
||||
The user's actual question is preserved; only turns whose content is entirely
|
||||
the upload block (nothing remains after stripping) are dropped along with
|
||||
their paired assistant response.
|
||||
|
||||
Only keeps:
|
||||
- Human messages (with the ephemeral upload block removed)
|
||||
- AI messages without tool_calls (final assistant responses), unless the
|
||||
paired human turn was upload-only and had no real user text.
|
||||
|
||||
Args:
|
||||
messages: List of all conversation messages.
|
||||
|
||||
Returns:
|
||||
Filtered list containing only user inputs and final assistant responses.
|
||||
"""
|
||||
filtered = []
|
||||
skip_next_ai = False
|
||||
for msg in messages:
|
||||
msg_type = getattr(msg, "type", None)
|
||||
|
||||
if msg_type == "human":
|
||||
content_str = _extract_message_text(msg)
|
||||
if "<uploaded_files>" in content_str:
|
||||
# Strip the ephemeral upload block; keep the user's real question.
|
||||
stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
|
||||
if not stripped:
|
||||
# Nothing left — the entire turn was upload bookkeeping;
|
||||
# skip it and the paired assistant response.
|
||||
skip_next_ai = True
|
||||
continue
|
||||
# Rebuild the message with cleaned content so the user's question
|
||||
# is still available for memory summarisation.
|
||||
from copy import copy
|
||||
|
||||
clean_msg = copy(msg)
|
||||
clean_msg.content = stripped
|
||||
filtered.append(clean_msg)
|
||||
skip_next_ai = False
|
||||
else:
|
||||
filtered.append(msg)
|
||||
skip_next_ai = False
|
||||
elif msg_type == "ai":
|
||||
tool_calls = getattr(msg, "tool_calls", None)
|
||||
if not tool_calls:
|
||||
if skip_next_ai:
|
||||
skip_next_ai = False
|
||||
continue
|
||||
filtered.append(msg)
|
||||
# Skip tool messages and AI messages with tool_calls
|
||||
|
||||
return filtered
|
||||
|
||||
|
||||
def detect_correction(messages: list[Any]) -> bool:
|
||||
"""Detect explicit user corrections in recent conversation turns.
|
||||
|
||||
The queue keeps only one pending context per thread, so callers pass the
|
||||
latest filtered message list. Checking only recent user turns keeps signal
|
||||
detection conservative while avoiding stale corrections from long histories.
|
||||
"""
|
||||
recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
|
||||
|
||||
for msg in recent_user_msgs:
|
||||
content = _extract_message_text(msg).strip()
|
||||
if not content:
|
||||
continue
|
||||
if any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def detect_reinforcement(messages: list[Any]) -> bool:
|
||||
"""Detect explicit positive reinforcement signals in recent conversation turns.
|
||||
|
||||
Complements detect_correction() by identifying when the user confirms the
|
||||
agent's approach was correct. This allows the memory system to record what
|
||||
worked well, not just what went wrong.
|
||||
|
||||
The queue keeps only one pending context per thread, so callers pass the
|
||||
latest filtered message list. Checking only recent user turns keeps signal
|
||||
detection conservative while avoiding stale signals from long histories.
|
||||
"""
|
||||
recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
|
||||
|
||||
for msg in recent_user_msgs:
|
||||
content = _extract_message_text(msg).strip()
|
||||
if not content:
|
||||
continue
|
||||
if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
|
||||
"""Middleware that queues conversation for memory update after agent execution.
|
||||
|
||||
This middleware:
|
||||
1. After each agent execution, queues the conversation for memory update
|
||||
2. Only includes user inputs and final assistant responses (ignores tool calls)
|
||||
3. The queue uses debouncing to batch multiple updates together
|
||||
4. Memory is updated asynchronously via LLM summarization
|
||||
"""
|
||||
|
||||
state_schema = MemoryMiddlewareState
|
||||
|
||||
def __init__(self, agent_name: str | None = None):
|
||||
"""Initialize the MemoryMiddleware.
|
||||
|
||||
Args:
|
||||
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
|
||||
"""
|
||||
super().__init__()
|
||||
self._agent_name = agent_name
|
||||
|
||||
@override
|
||||
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
"""Queue conversation for memory update after agent completes.
|
||||
|
||||
Args:
|
||||
state: The current agent state.
|
||||
runtime: The runtime context.
|
||||
|
||||
Returns:
|
||||
None (no state changes needed from this middleware).
|
||||
"""
|
||||
config = get_memory_config()
|
||||
if not config.enabled:
|
||||
return None
|
||||
|
||||
# Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
|
||||
thread_id = runtime.context.get("thread_id") if runtime.context else None
|
||||
if thread_id is None:
|
||||
config_data = get_config()
|
||||
thread_id = config_data.get("configurable", {}).get("thread_id")
|
||||
if not thread_id:
|
||||
logger.debug("No thread_id in context, skipping memory update")
|
||||
return None
|
||||
|
||||
# Get messages from state
|
||||
messages = state.get("messages", [])
|
||||
if not messages:
|
||||
logger.debug("No messages in state, skipping memory update")
|
||||
return None
|
||||
|
||||
# Filter to only keep user inputs and final assistant responses
|
||||
filtered_messages = _filter_messages_for_memory(messages)
|
||||
|
||||
# Only queue if there's meaningful conversation
|
||||
# At minimum need one user message and one assistant response
|
||||
user_messages = [m for m in filtered_messages if getattr(m, "type", None) == "human"]
|
||||
assistant_messages = [m for m in filtered_messages if getattr(m, "type", None) == "ai"]
|
||||
|
||||
if not user_messages or not assistant_messages:
|
||||
return None
|
||||
|
||||
# Queue the filtered conversation for memory update
|
||||
correction_detected = detect_correction(filtered_messages)
|
||||
reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
|
||||
queue = get_memory_queue()
|
||||
queue.add(
|
||||
thread_id=thread_id,
|
||||
messages=filtered_messages,
|
||||
agent_name=self._agent_name,
|
||||
correction_detected=correction_detected,
|
||||
reinforcement_detected=reinforcement_detected,
|
||||
)
|
||||
|
||||
return None
|
||||
@@ -0,0 +1,363 @@
|
||||
"""SandboxAuditMiddleware - bash command security auditing."""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import shlex
|
||||
from collections.abc import Awaitable, Callable
|
||||
from datetime import UTC, datetime
|
||||
from typing import override
|
||||
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langgraph.prebuilt.tool_node import ToolCallRequest
|
||||
from langgraph.types import Command
|
||||
|
||||
from deerflow.agents.thread_state import ThreadState
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Command classification rules
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# Each pattern is compiled once at import time.
|
||||
_HIGH_RISK_PATTERNS: list[re.Pattern[str]] = [
|
||||
# --- original rules (retained) ---
|
||||
re.compile(r"rm\s+-[^\s]*r[^\s]*\s+(/\*?|~/?\*?|/home\b|/root\b)\s*$"),
|
||||
re.compile(r"dd\s+if="),
|
||||
re.compile(r"mkfs"),
|
||||
re.compile(r"cat\s+/etc/shadow"),
|
||||
re.compile(r">+\s*/etc/"),
|
||||
# --- pipe to sh/bash (generalised, replaces old curl|sh rule) ---
|
||||
re.compile(r"\|\s*(ba)?sh\b"),
|
||||
# --- command substitution (targeted – only dangerous executables) ---
|
||||
re.compile(r"[`$]\(?\s*(curl|wget|bash|sh|python|ruby|perl|base64)"),
|
||||
# --- base64 decode piped to execution ---
|
||||
re.compile(r"base64\s+.*-d.*\|"),
|
||||
# --- overwrite system binaries ---
|
||||
re.compile(r">+\s*(/usr/bin/|/bin/|/sbin/)"),
|
||||
# --- overwrite shell startup files ---
|
||||
re.compile(r">+\s*~/?\.(bashrc|profile|zshrc|bash_profile)"),
|
||||
# --- process environment leakage ---
|
||||
re.compile(r"/proc/[^/]+/environ"),
|
||||
# --- dynamic linker hijack (one-step escalation) ---
|
||||
re.compile(r"\b(LD_PRELOAD|LD_LIBRARY_PATH)\s*="),
|
||||
# --- bash built-in networking (bypasses tool allowlists) ---
|
||||
re.compile(r"/dev/tcp/"),
|
||||
# --- fork bomb ---
|
||||
re.compile(r"\S+\(\)\s*\{[^}]*\|\s*\S+\s*&"), # :(){ :|:& };:
|
||||
re.compile(r"while\s+true.*&\s*done"), # while true; do bash & done
|
||||
]
|
||||
|
||||
_MEDIUM_RISK_PATTERNS: list[re.Pattern[str]] = [
|
||||
re.compile(r"chmod\s+777"),
|
||||
re.compile(r"pip3?\s+install"),
|
||||
re.compile(r"apt(-get)?\s+install"),
|
||||
# sudo/su: no-op under Docker root; warn so LLM is aware
|
||||
re.compile(r"\b(sudo|su)\b"),
|
||||
# PATH modification: long attack chain, warn rather than block
|
||||
re.compile(r"\bPATH\s*="),
|
||||
]
|
||||
|
||||
|
||||
def _split_compound_command(command: str) -> list[str]:
|
||||
"""Split a compound command into sub-commands (quote-aware).
|
||||
|
||||
Scans the raw command string so unquoted shell control operators are
|
||||
recognised even when they are not surrounded by whitespace
|
||||
(e.g. ``safe;rm -rf /`` or ``rm -rf /&&echo ok``). Operators inside
|
||||
quotes are ignored. If the command ends with an unclosed quote or a
|
||||
dangling escape, return the whole command unchanged (fail-closed —
|
||||
safer to classify the unsplit string than silently drop parts).
|
||||
"""
|
||||
parts: list[str] = []
|
||||
current: list[str] = []
|
||||
in_single_quote = False
|
||||
in_double_quote = False
|
||||
escaping = False
|
||||
index = 0
|
||||
|
||||
while index < len(command):
|
||||
char = command[index]
|
||||
|
||||
if escaping:
|
||||
current.append(char)
|
||||
escaping = False
|
||||
index += 1
|
||||
continue
|
||||
|
||||
if char == "\\" and not in_single_quote:
|
||||
current.append(char)
|
||||
escaping = True
|
||||
index += 1
|
||||
continue
|
||||
|
||||
if char == "'" and not in_double_quote:
|
||||
in_single_quote = not in_single_quote
|
||||
current.append(char)
|
||||
index += 1
|
||||
continue
|
||||
|
||||
if char == '"' and not in_single_quote:
|
||||
in_double_quote = not in_double_quote
|
||||
current.append(char)
|
||||
index += 1
|
||||
continue
|
||||
|
||||
if not in_single_quote and not in_double_quote:
|
||||
if command.startswith("&&", index) or command.startswith("||", index):
|
||||
part = "".join(current).strip()
|
||||
if part:
|
||||
parts.append(part)
|
||||
current = []
|
||||
index += 2
|
||||
continue
|
||||
if char == ";":
|
||||
part = "".join(current).strip()
|
||||
if part:
|
||||
parts.append(part)
|
||||
current = []
|
||||
index += 1
|
||||
continue
|
||||
|
||||
current.append(char)
|
||||
index += 1
|
||||
|
||||
# Unclosed quote or dangling escape → fail-closed, return whole command
|
||||
if in_single_quote or in_double_quote or escaping:
|
||||
return [command]
|
||||
|
||||
part = "".join(current).strip()
|
||||
if part:
|
||||
parts.append(part)
|
||||
return parts if parts else [command]
|
||||
|
||||
|
||||
def _classify_single_command(command: str) -> str:
|
||||
"""Classify a single (non-compound) command. Return 'block', 'warn', or 'pass'."""
|
||||
normalized = " ".join(command.split())
|
||||
|
||||
for pattern in _HIGH_RISK_PATTERNS:
|
||||
if pattern.search(normalized):
|
||||
return "block"
|
||||
|
||||
# Also try shlex-parsed tokens for high-risk detection
|
||||
try:
|
||||
tokens = shlex.split(command)
|
||||
joined = " ".join(tokens)
|
||||
for pattern in _HIGH_RISK_PATTERNS:
|
||||
if pattern.search(joined):
|
||||
return "block"
|
||||
except ValueError:
|
||||
# shlex.split fails on unclosed quotes — treat as suspicious
|
||||
return "block"
|
||||
|
||||
for pattern in _MEDIUM_RISK_PATTERNS:
|
||||
if pattern.search(normalized):
|
||||
return "warn"
|
||||
|
||||
return "pass"
|
||||
|
||||
|
||||
def _classify_command(command: str) -> str:
|
||||
"""Return 'block', 'warn', or 'pass'.
|
||||
|
||||
Strategy:
|
||||
1. First scan the *whole* raw command against high-risk patterns. This
|
||||
catches structural attacks like ``while true; do bash & done`` or
|
||||
``:(){ :|:& };:`` that span multiple shell statements — splitting them
|
||||
on ``;`` would destroy the pattern context.
|
||||
2. Then split compound commands (e.g. ``cmd1 && cmd2 ; cmd3``) and
|
||||
classify each sub-command independently. The most severe verdict wins.
|
||||
"""
|
||||
# Pass 1: whole-command high-risk scan (catches multi-statement patterns)
|
||||
normalized = " ".join(command.split())
|
||||
for pattern in _HIGH_RISK_PATTERNS:
|
||||
if pattern.search(normalized):
|
||||
return "block"
|
||||
|
||||
# Pass 2: per-sub-command classification
|
||||
sub_commands = _split_compound_command(command)
|
||||
worst = "pass"
|
||||
for sub in sub_commands:
|
||||
verdict = _classify_single_command(sub)
|
||||
if verdict == "block":
|
||||
return "block" # short-circuit: can't get worse
|
||||
if verdict == "warn":
|
||||
worst = "warn"
|
||||
return worst
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Middleware
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class SandboxAuditMiddleware(AgentMiddleware[ThreadState]):
|
||||
"""Bash command security auditing middleware.
|
||||
|
||||
For every ``bash`` tool call:
|
||||
1. **Command classification**: regex + shlex analysis grades commands as
|
||||
high-risk (block), medium-risk (warn), or safe (pass).
|
||||
2. **Audit log**: every bash call is recorded as a structured JSON entry
|
||||
via the standard logger (visible in langgraph.log).
|
||||
|
||||
High-risk commands (e.g. ``rm -rf /``, ``curl url | bash``) are blocked:
|
||||
the handler is not called and an error ``ToolMessage`` is returned so the
|
||||
agent loop can continue gracefully.
|
||||
|
||||
Medium-risk commands (e.g. ``pip install``, ``chmod 777``) are executed
|
||||
normally; a warning is appended to the tool result so the LLM is aware.
|
||||
"""
|
||||
|
||||
state_schema = ThreadState
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _get_thread_id(self, request: ToolCallRequest) -> str | None:
|
||||
runtime = request.runtime # ToolRuntime; may be None-like in tests
|
||||
if runtime is None:
|
||||
return None
|
||||
ctx = getattr(runtime, "context", None) or {}
|
||||
thread_id = ctx.get("thread_id") if isinstance(ctx, dict) else None
|
||||
if thread_id is None:
|
||||
cfg = getattr(runtime, "config", None) or {}
|
||||
thread_id = cfg.get("configurable", {}).get("thread_id")
|
||||
return thread_id
|
||||
|
||||
_AUDIT_COMMAND_LIMIT = 200
|
||||
|
||||
def _write_audit(self, thread_id: str | None, command: str, verdict: str, *, truncate: bool = False) -> None:
|
||||
audited_command = command
|
||||
if truncate and len(command) > self._AUDIT_COMMAND_LIMIT:
|
||||
audited_command = f"{command[: self._AUDIT_COMMAND_LIMIT]}... ({len(command)} chars)"
|
||||
record = {
|
||||
"timestamp": datetime.now(UTC).isoformat(),
|
||||
"thread_id": thread_id or "unknown",
|
||||
"command": audited_command,
|
||||
"verdict": verdict,
|
||||
}
|
||||
logger.info("[SandboxAudit] %s", json.dumps(record, ensure_ascii=False))
|
||||
|
||||
def _build_block_message(self, request: ToolCallRequest, reason: str) -> ToolMessage:
|
||||
tool_call_id = str(request.tool_call.get("id") or "missing_id")
|
||||
return ToolMessage(
|
||||
content=f"Command blocked: {reason}. Please use a safer alternative approach.",
|
||||
tool_call_id=tool_call_id,
|
||||
name="bash",
|
||||
status="error",
|
||||
)
|
||||
|
||||
def _append_warn_to_result(self, result: ToolMessage | Command, command: str) -> ToolMessage | Command:
|
||||
"""Append a warning note to the tool result for medium-risk commands."""
|
||||
if not isinstance(result, ToolMessage):
|
||||
return result
|
||||
warning = f"\n\n⚠️ Warning: `{command}` is a medium-risk command that may modify the runtime environment."
|
||||
if isinstance(result.content, list):
|
||||
new_content = list(result.content) + [{"type": "text", "text": warning}]
|
||||
else:
|
||||
new_content = str(result.content) + warning
|
||||
return ToolMessage(
|
||||
content=new_content,
|
||||
tool_call_id=result.tool_call_id,
|
||||
name=result.name,
|
||||
status=result.status,
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Input sanitisation
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
# Normal bash commands rarely exceed a few hundred characters. 10 000 is
|
||||
# well above any legitimate use case yet a tiny fraction of Linux ARG_MAX.
|
||||
# Anything longer is almost certainly a payload injection or base64-encoded
|
||||
# attack string.
|
||||
_MAX_COMMAND_LENGTH = 10_000
|
||||
|
||||
def _validate_input(self, command: str) -> str | None:
|
||||
"""Return ``None`` if *command* is acceptable, else a rejection reason."""
|
||||
if not command.strip():
|
||||
return "empty command"
|
||||
if len(command) > self._MAX_COMMAND_LENGTH:
|
||||
return "command too long"
|
||||
if "\x00" in command:
|
||||
return "null byte detected"
|
||||
return None
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Core logic (shared between sync and async paths)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _pre_process(self, request: ToolCallRequest) -> tuple[str, str | None, str, str | None]:
|
||||
"""
|
||||
Returns (command, thread_id, verdict, reject_reason).
|
||||
verdict is 'block', 'warn', or 'pass'.
|
||||
reject_reason is non-None only for input sanitisation rejections.
|
||||
"""
|
||||
args = request.tool_call.get("args", {})
|
||||
raw_command = args.get("command")
|
||||
command = raw_command if isinstance(raw_command, str) else ""
|
||||
thread_id = self._get_thread_id(request)
|
||||
|
||||
# ① input sanitisation — reject malformed input before regex analysis
|
||||
reject_reason = self._validate_input(command)
|
||||
if reject_reason:
|
||||
self._write_audit(thread_id, command, "block", truncate=True)
|
||||
logger.warning("[SandboxAudit] INVALID INPUT thread=%s reason=%s", thread_id, reject_reason)
|
||||
return command, thread_id, "block", reject_reason
|
||||
|
||||
# ② classify command
|
||||
verdict = _classify_command(command)
|
||||
|
||||
# ③ audit log
|
||||
self._write_audit(thread_id, command, verdict)
|
||||
|
||||
if verdict == "block":
|
||||
logger.warning("[SandboxAudit] BLOCKED thread=%s cmd=%r", thread_id, command)
|
||||
elif verdict == "warn":
|
||||
logger.warning("[SandboxAudit] WARN (medium-risk) thread=%s cmd=%r", thread_id, command)
|
||||
|
||||
return command, thread_id, verdict, None
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# wrap_tool_call hooks
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@override
|
||||
def wrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], ToolMessage | Command],
|
||||
) -> ToolMessage | Command:
|
||||
if request.tool_call.get("name") != "bash":
|
||||
return handler(request)
|
||||
|
||||
command, _, verdict, reject_reason = self._pre_process(request)
|
||||
if verdict == "block":
|
||||
reason = reject_reason or "security violation detected"
|
||||
return self._build_block_message(request, reason)
|
||||
result = handler(request)
|
||||
if verdict == "warn":
|
||||
result = self._append_warn_to_result(result, command)
|
||||
return result
|
||||
|
||||
@override
|
||||
async def awrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
|
||||
) -> ToolMessage | Command:
|
||||
if request.tool_call.get("name") != "bash":
|
||||
return await handler(request)
|
||||
|
||||
command, _, verdict, reject_reason = self._pre_process(request)
|
||||
if verdict == "block":
|
||||
reason = reject_reason or "security violation detected"
|
||||
return self._build_block_message(request, reason)
|
||||
result = await handler(request)
|
||||
if verdict == "warn":
|
||||
result = self._append_warn_to_result(result, command)
|
||||
return result
|
||||
@@ -0,0 +1,75 @@
|
||||
"""Middleware to enforce maximum concurrent subagent tool calls per model response."""
|
||||
|
||||
import logging
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
from deerflow.subagents.executor import MAX_CONCURRENT_SUBAGENTS
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Valid range for max_concurrent_subagents
|
||||
MIN_SUBAGENT_LIMIT = 2
|
||||
MAX_SUBAGENT_LIMIT = 4
|
||||
|
||||
|
||||
def _clamp_subagent_limit(value: int) -> int:
|
||||
"""Clamp subagent limit to valid range [2, 4]."""
|
||||
return max(MIN_SUBAGENT_LIMIT, min(MAX_SUBAGENT_LIMIT, value))
|
||||
|
||||
|
||||
class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Truncates excess 'task' tool calls from a single model response.
|
||||
|
||||
When an LLM generates more than max_concurrent parallel task tool calls
|
||||
in one response, this middleware keeps only the first max_concurrent and
|
||||
discards the rest. This is more reliable than prompt-based limits.
|
||||
|
||||
Args:
|
||||
max_concurrent: Maximum number of concurrent subagent calls allowed.
|
||||
Defaults to MAX_CONCURRENT_SUBAGENTS (3). Clamped to [2, 4].
|
||||
"""
|
||||
|
||||
def __init__(self, max_concurrent: int = MAX_CONCURRENT_SUBAGENTS):
|
||||
super().__init__()
|
||||
self.max_concurrent = _clamp_subagent_limit(max_concurrent)
|
||||
|
||||
def _truncate_task_calls(self, state: AgentState) -> dict | None:
|
||||
messages = state.get("messages", [])
|
||||
if not messages:
|
||||
return None
|
||||
|
||||
last_msg = messages[-1]
|
||||
if getattr(last_msg, "type", None) != "ai":
|
||||
return None
|
||||
|
||||
tool_calls = getattr(last_msg, "tool_calls", None)
|
||||
if not tool_calls:
|
||||
return None
|
||||
|
||||
# Count task tool calls
|
||||
task_indices = [i for i, tc in enumerate(tool_calls) if tc.get("name") == "task"]
|
||||
if len(task_indices) <= self.max_concurrent:
|
||||
return None
|
||||
|
||||
# Build set of indices to drop (excess task calls beyond the limit)
|
||||
indices_to_drop = set(task_indices[self.max_concurrent :])
|
||||
truncated_tool_calls = [tc for i, tc in enumerate(tool_calls) if i not in indices_to_drop]
|
||||
|
||||
dropped_count = len(indices_to_drop)
|
||||
logger.warning(f"Truncated {dropped_count} excess task tool call(s) from model response (limit: {self.max_concurrent})")
|
||||
|
||||
# Replace the AIMessage with truncated tool_calls (same id triggers replacement)
|
||||
updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
|
||||
return {"messages": [updated_msg]}
|
||||
|
||||
@override
|
||||
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._truncate_task_calls(state)
|
||||
|
||||
@override
|
||||
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._truncate_task_calls(state)
|
||||
@@ -0,0 +1,99 @@
|
||||
import logging
|
||||
from typing import NotRequired, override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langgraph.config import get_config
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
from deerflow.agents.thread_state import ThreadDataState
|
||||
from deerflow.config.paths import Paths, get_paths
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ThreadDataMiddlewareState(AgentState):
|
||||
"""Compatible with the `ThreadState` schema."""
|
||||
|
||||
thread_data: NotRequired[ThreadDataState | None]
|
||||
|
||||
|
||||
class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
|
||||
"""Create thread data directories for each thread execution.
|
||||
|
||||
Creates the following directory structure:
|
||||
- {base_dir}/threads/{thread_id}/user-data/workspace
|
||||
- {base_dir}/threads/{thread_id}/user-data/uploads
|
||||
- {base_dir}/threads/{thread_id}/user-data/outputs
|
||||
|
||||
Lifecycle Management:
|
||||
- With lazy_init=True (default): Only compute paths, directories created on-demand
|
||||
- With lazy_init=False: Eagerly create directories in before_agent()
|
||||
"""
|
||||
|
||||
state_schema = ThreadDataMiddlewareState
|
||||
|
||||
def __init__(self, base_dir: str | None = None, lazy_init: bool = True):
|
||||
"""Initialize the middleware.
|
||||
|
||||
Args:
|
||||
base_dir: Base directory for thread data. Defaults to Paths resolution.
|
||||
lazy_init: If True, defer directory creation until needed.
|
||||
If False, create directories eagerly in before_agent().
|
||||
Default is True for optimal performance.
|
||||
"""
|
||||
super().__init__()
|
||||
self._paths = Paths(base_dir) if base_dir else get_paths()
|
||||
self._lazy_init = lazy_init
|
||||
|
||||
def _get_thread_paths(self, thread_id: str) -> dict[str, str]:
|
||||
"""Get the paths for a thread's data directories.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
|
||||
Returns:
|
||||
Dictionary with workspace_path, uploads_path, and outputs_path.
|
||||
"""
|
||||
return {
|
||||
"workspace_path": str(self._paths.sandbox_work_dir(thread_id)),
|
||||
"uploads_path": str(self._paths.sandbox_uploads_dir(thread_id)),
|
||||
"outputs_path": str(self._paths.sandbox_outputs_dir(thread_id)),
|
||||
}
|
||||
|
||||
def _create_thread_directories(self, thread_id: str) -> dict[str, str]:
|
||||
"""Create the thread data directories.
|
||||
|
||||
Args:
|
||||
thread_id: The thread ID.
|
||||
|
||||
Returns:
|
||||
Dictionary with the created directory paths.
|
||||
"""
|
||||
self._paths.ensure_thread_dirs(thread_id)
|
||||
return self._get_thread_paths(thread_id)
|
||||
|
||||
@override
|
||||
def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
context = runtime.context or {}
|
||||
thread_id = context.get("thread_id")
|
||||
if thread_id is None:
|
||||
config = get_config()
|
||||
thread_id = config.get("configurable", {}).get("thread_id")
|
||||
|
||||
if thread_id is None:
|
||||
raise ValueError("Thread ID is required in runtime context or config.configurable")
|
||||
|
||||
if self._lazy_init:
|
||||
# Lazy initialization: only compute paths, don't create directories
|
||||
paths = self._get_thread_paths(thread_id)
|
||||
else:
|
||||
# Eager initialization: create directories immediately
|
||||
paths = self._create_thread_directories(thread_id)
|
||||
logger.debug("Created thread data directories for thread %s", thread_id)
|
||||
|
||||
return {
|
||||
"thread_data": {
|
||||
**paths,
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,138 @@
|
||||
"""Middleware for automatic thread title generation."""
|
||||
|
||||
import logging
|
||||
from typing import NotRequired, override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
from deerflow.config.title_config import get_title_config
|
||||
from deerflow.models import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TitleMiddlewareState(AgentState):
|
||||
"""Compatible with the `ThreadState` schema."""
|
||||
|
||||
title: NotRequired[str | None]
|
||||
|
||||
|
||||
class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
|
||||
"""Automatically generate a title for the thread after the first user message."""
|
||||
|
||||
state_schema = TitleMiddlewareState
|
||||
|
||||
def _normalize_content(self, content: object) -> str:
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
|
||||
if isinstance(content, list):
|
||||
parts = [self._normalize_content(item) for item in content]
|
||||
return "\n".join(part for part in parts if part)
|
||||
|
||||
if isinstance(content, dict):
|
||||
text_value = content.get("text")
|
||||
if isinstance(text_value, str):
|
||||
return text_value
|
||||
|
||||
nested_content = content.get("content")
|
||||
if nested_content is not None:
|
||||
return self._normalize_content(nested_content)
|
||||
|
||||
return ""
|
||||
|
||||
def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
|
||||
"""Check if we should generate a title for this thread."""
|
||||
config = get_title_config()
|
||||
if not config.enabled:
|
||||
return False
|
||||
|
||||
# Check if thread already has a title in state
|
||||
if state.get("title"):
|
||||
return False
|
||||
|
||||
# Check if this is the first turn (has at least one user message and one assistant response)
|
||||
messages = state.get("messages", [])
|
||||
if len(messages) < 2:
|
||||
return False
|
||||
|
||||
# Count user and assistant messages
|
||||
user_messages = [m for m in messages if m.type == "human"]
|
||||
assistant_messages = [m for m in messages if m.type == "ai"]
|
||||
|
||||
# Generate title after first complete exchange
|
||||
return len(user_messages) == 1 and len(assistant_messages) >= 1
|
||||
|
||||
def _build_title_prompt(self, state: TitleMiddlewareState) -> tuple[str, str]:
|
||||
"""Extract user/assistant messages and build the title prompt.
|
||||
|
||||
Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
|
||||
"""
|
||||
config = get_title_config()
|
||||
messages = state.get("messages", [])
|
||||
|
||||
user_msg_content = next((m.content for m in messages if m.type == "human"), "")
|
||||
assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")
|
||||
|
||||
user_msg = self._normalize_content(user_msg_content)
|
||||
assistant_msg = self._normalize_content(assistant_msg_content)
|
||||
|
||||
prompt = config.prompt_template.format(
|
||||
max_words=config.max_words,
|
||||
user_msg=user_msg[:500],
|
||||
assistant_msg=assistant_msg[:500],
|
||||
)
|
||||
return prompt, user_msg
|
||||
|
||||
def _parse_title(self, content: object) -> str:
|
||||
"""Normalize model output into a clean title string."""
|
||||
config = get_title_config()
|
||||
title_content = self._normalize_content(content)
|
||||
title = title_content.strip().strip('"').strip("'")
|
||||
return title[: config.max_chars] if len(title) > config.max_chars else title
|
||||
|
||||
def _fallback_title(self, user_msg: str) -> str:
|
||||
config = get_title_config()
|
||||
fallback_chars = min(config.max_chars, 50)
|
||||
if len(user_msg) > fallback_chars:
|
||||
return user_msg[:fallback_chars].rstrip() + "..."
|
||||
return user_msg if user_msg else "New Conversation"
|
||||
|
||||
def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
|
||||
"""Generate a local fallback title without blocking on an LLM call."""
|
||||
if not self._should_generate_title(state):
|
||||
return None
|
||||
|
||||
_, user_msg = self._build_title_prompt(state)
|
||||
return {"title": self._fallback_title(user_msg)}
|
||||
|
||||
async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
|
||||
"""Generate a title asynchronously and fall back locally on failure."""
|
||||
if not self._should_generate_title(state):
|
||||
return None
|
||||
|
||||
config = get_title_config()
|
||||
prompt, user_msg = self._build_title_prompt(state)
|
||||
|
||||
try:
|
||||
if config.model_name:
|
||||
model = create_chat_model(name=config.model_name, thinking_enabled=False)
|
||||
else:
|
||||
model = create_chat_model(thinking_enabled=False)
|
||||
response = await model.ainvoke(prompt)
|
||||
title = self._parse_title(response.content)
|
||||
if title:
|
||||
return {"title": title}
|
||||
except Exception:
|
||||
logger.debug("Failed to generate async title; falling back to local title", exc_info=True)
|
||||
return {"title": self._fallback_title(user_msg)}
|
||||
|
||||
@override
|
||||
def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
return self._generate_title_result(state)
|
||||
|
||||
@override
|
||||
async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
return await self._agenerate_title_result(state)
|
||||
@@ -0,0 +1,100 @@
|
||||
"""Middleware that extends TodoListMiddleware with context-loss detection.
|
||||
|
||||
When the message history is truncated (e.g., by SummarizationMiddleware), the
|
||||
original `write_todos` tool call and its ToolMessage can be scrolled out of the
|
||||
active context window. This middleware detects that situation and injects a
|
||||
reminder message so the model still knows about the outstanding todo list.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, override
|
||||
|
||||
from langchain.agents.middleware import TodoListMiddleware
|
||||
from langchain.agents.middleware.todo import PlanningState, Todo
|
||||
from langchain_core.messages import AIMessage, HumanMessage
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
|
||||
def _todos_in_messages(messages: list[Any]) -> bool:
|
||||
"""Return True if any AIMessage in *messages* contains a write_todos tool call."""
|
||||
for msg in messages:
|
||||
if isinstance(msg, AIMessage) and msg.tool_calls:
|
||||
for tc in msg.tool_calls:
|
||||
if tc.get("name") == "write_todos":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _reminder_in_messages(messages: list[Any]) -> bool:
|
||||
"""Return True if a todo_reminder HumanMessage is already present in *messages*."""
|
||||
for msg in messages:
|
||||
if isinstance(msg, HumanMessage) and getattr(msg, "name", None) == "todo_reminder":
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _format_todos(todos: list[Todo]) -> str:
|
||||
"""Format a list of Todo items into a human-readable string."""
|
||||
lines: list[str] = []
|
||||
for todo in todos:
|
||||
status = todo.get("status", "pending")
|
||||
content = todo.get("content", "")
|
||||
lines.append(f"- [{status}] {content}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
class TodoMiddleware(TodoListMiddleware):
|
||||
"""Extends TodoListMiddleware with `write_todos` context-loss detection.
|
||||
|
||||
When the original `write_todos` tool call has been truncated from the message
|
||||
history (e.g., after summarization), the model loses awareness of the current
|
||||
todo list. This middleware detects that gap in `before_model` / `abefore_model`
|
||||
and injects a reminder message so the model can continue tracking progress.
|
||||
"""
|
||||
|
||||
@override
|
||||
def before_model(
|
||||
self,
|
||||
state: PlanningState,
|
||||
runtime: Runtime, # noqa: ARG002
|
||||
) -> dict[str, Any] | None:
|
||||
"""Inject a todo-list reminder when write_todos has left the context window."""
|
||||
todos: list[Todo] = state.get("todos") or [] # type: ignore[assignment]
|
||||
if not todos:
|
||||
return None
|
||||
|
||||
messages = state.get("messages") or []
|
||||
if _todos_in_messages(messages):
|
||||
# write_todos is still visible in context — nothing to do.
|
||||
return None
|
||||
|
||||
if _reminder_in_messages(messages):
|
||||
# A reminder was already injected and hasn't been truncated yet.
|
||||
return None
|
||||
|
||||
# The todo list exists in state but the original write_todos call is gone.
|
||||
# Inject a reminder as a HumanMessage so the model stays aware.
|
||||
formatted = _format_todos(todos)
|
||||
reminder = HumanMessage(
|
||||
name="todo_reminder",
|
||||
content=(
|
||||
"<system_reminder>\n"
|
||||
"Your todo list from earlier is no longer visible in the current context window, "
|
||||
"but it is still active. Here is the current state:\n\n"
|
||||
f"{formatted}\n\n"
|
||||
"Continue tracking and updating this todo list as you work. "
|
||||
"Call `write_todos` whenever the status of any item changes.\n"
|
||||
"</system_reminder>"
|
||||
),
|
||||
)
|
||||
return {"messages": [reminder]}
|
||||
|
||||
@override
|
||||
async def abefore_model(
|
||||
self,
|
||||
state: PlanningState,
|
||||
runtime: Runtime,
|
||||
) -> dict[str, Any] | None:
|
||||
"""Async version of before_model."""
|
||||
return self.before_model(state, runtime)
|
||||
@@ -0,0 +1,37 @@
|
||||
"""Middleware for logging LLM token usage."""
|
||||
|
||||
import logging
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class TokenUsageMiddleware(AgentMiddleware):
|
||||
"""Logs token usage from model response usage_metadata."""
|
||||
|
||||
@override
|
||||
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._log_usage(state)
|
||||
|
||||
@override
|
||||
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._log_usage(state)
|
||||
|
||||
def _log_usage(self, state: AgentState) -> None:
|
||||
messages = state.get("messages", [])
|
||||
if not messages:
|
||||
return None
|
||||
last = messages[-1]
|
||||
usage = getattr(last, "usage_metadata", None)
|
||||
if usage:
|
||||
logger.info(
|
||||
"LLM token usage: input=%s output=%s total=%s",
|
||||
usage.get("input_tokens", "?"),
|
||||
usage.get("output_tokens", "?"),
|
||||
usage.get("total_tokens", "?"),
|
||||
)
|
||||
return None
|
||||
@@ -0,0 +1,143 @@
|
||||
"""Tool error handling middleware and shared runtime middleware builders."""
|
||||
|
||||
import logging
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langgraph.errors import GraphBubbleUp
|
||||
from langgraph.prebuilt.tool_node import ToolCallRequest
|
||||
from langgraph.types import Command
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_MISSING_TOOL_CALL_ID = "missing_tool_call_id"
|
||||
|
||||
|
||||
class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
|
||||
"""Convert tool exceptions into error ToolMessages so the run can continue."""
|
||||
|
||||
def _build_error_message(self, request: ToolCallRequest, exc: Exception) -> ToolMessage:
|
||||
tool_name = str(request.tool_call.get("name") or "unknown_tool")
|
||||
tool_call_id = str(request.tool_call.get("id") or _MISSING_TOOL_CALL_ID)
|
||||
detail = str(exc).strip() or exc.__class__.__name__
|
||||
if len(detail) > 500:
|
||||
detail = detail[:497] + "..."
|
||||
|
||||
content = f"Error: Tool '{tool_name}' failed with {exc.__class__.__name__}: {detail}. Continue with available context, or choose an alternative tool."
|
||||
return ToolMessage(
|
||||
content=content,
|
||||
tool_call_id=tool_call_id,
|
||||
name=tool_name,
|
||||
status="error",
|
||||
)
|
||||
|
||||
@override
|
||||
def wrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], ToolMessage | Command],
|
||||
) -> ToolMessage | Command:
|
||||
try:
|
||||
return handler(request)
|
||||
except GraphBubbleUp:
|
||||
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
|
||||
raise
|
||||
except Exception as exc:
|
||||
logger.exception("Tool execution failed (sync): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
|
||||
return self._build_error_message(request, exc)
|
||||
|
||||
@override
|
||||
async def awrap_tool_call(
|
||||
self,
|
||||
request: ToolCallRequest,
|
||||
handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
|
||||
) -> ToolMessage | Command:
|
||||
try:
|
||||
return await handler(request)
|
||||
except GraphBubbleUp:
|
||||
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
|
||||
raise
|
||||
except Exception as exc:
|
||||
logger.exception("Tool execution failed (async): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
|
||||
return self._build_error_message(request, exc)
|
||||
|
||||
|
||||
def _build_runtime_middlewares(
|
||||
*,
|
||||
include_uploads: bool,
|
||||
include_dangling_tool_call_patch: bool,
|
||||
lazy_init: bool = True,
|
||||
) -> list[AgentMiddleware]:
|
||||
"""Build shared base middlewares for agent execution."""
|
||||
from deerflow.agents.middlewares.llm_error_handling_middleware import LLMErrorHandlingMiddleware
|
||||
from deerflow.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
|
||||
from deerflow.sandbox.middleware import SandboxMiddleware
|
||||
|
||||
middlewares: list[AgentMiddleware] = [
|
||||
ThreadDataMiddleware(lazy_init=lazy_init),
|
||||
SandboxMiddleware(lazy_init=lazy_init),
|
||||
]
|
||||
|
||||
if include_uploads:
|
||||
from deerflow.agents.middlewares.uploads_middleware import UploadsMiddleware
|
||||
|
||||
middlewares.insert(1, UploadsMiddleware())
|
||||
|
||||
if include_dangling_tool_call_patch:
|
||||
from deerflow.agents.middlewares.dangling_tool_call_middleware import DanglingToolCallMiddleware
|
||||
|
||||
middlewares.append(DanglingToolCallMiddleware())
|
||||
|
||||
middlewares.append(LLMErrorHandlingMiddleware())
|
||||
|
||||
# Guardrail middleware (if configured)
|
||||
from deerflow.config.guardrails_config import get_guardrails_config
|
||||
|
||||
guardrails_config = get_guardrails_config()
|
||||
if guardrails_config.enabled and guardrails_config.provider:
|
||||
import inspect
|
||||
|
||||
from deerflow.guardrails.middleware import GuardrailMiddleware
|
||||
from deerflow.reflection import resolve_variable
|
||||
|
||||
provider_cls = resolve_variable(guardrails_config.provider.use)
|
||||
provider_kwargs = dict(guardrails_config.provider.config) if guardrails_config.provider.config else {}
|
||||
# Pass framework hint if the provider accepts it (e.g. for config discovery).
|
||||
# Built-in providers like AllowlistProvider don't need it, so only inject
|
||||
# when the constructor accepts 'framework' or '**kwargs'.
|
||||
if "framework" not in provider_kwargs:
|
||||
try:
|
||||
sig = inspect.signature(provider_cls.__init__)
|
||||
if "framework" in sig.parameters or any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
|
||||
provider_kwargs["framework"] = "deerflow"
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
provider = provider_cls(**provider_kwargs)
|
||||
middlewares.append(GuardrailMiddleware(provider, fail_closed=guardrails_config.fail_closed, passport=guardrails_config.passport))
|
||||
|
||||
from deerflow.agents.middlewares.sandbox_audit_middleware import SandboxAuditMiddleware
|
||||
|
||||
middlewares.append(SandboxAuditMiddleware())
|
||||
middlewares.append(ToolErrorHandlingMiddleware())
|
||||
return middlewares
|
||||
|
||||
|
||||
def build_lead_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
|
||||
"""Middlewares shared by lead agent runtime before lead-only middlewares."""
|
||||
return _build_runtime_middlewares(
|
||||
include_uploads=True,
|
||||
include_dangling_tool_call_patch=True,
|
||||
lazy_init=lazy_init,
|
||||
)
|
||||
|
||||
|
||||
def build_subagent_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
|
||||
"""Middlewares shared by subagent runtime before subagent-only middlewares."""
|
||||
return _build_runtime_middlewares(
|
||||
include_uploads=False,
|
||||
include_dangling_tool_call_patch=True,
|
||||
lazy_init=lazy_init,
|
||||
)
|
||||
@@ -0,0 +1,293 @@
|
||||
"""Middleware to inject uploaded files information into agent context."""
|
||||
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import NotRequired, override
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import AgentMiddleware
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
from deerflow.config.paths import Paths, get_paths
|
||||
from deerflow.utils.file_conversion import extract_outline
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
_OUTLINE_PREVIEW_LINES = 5
|
||||
|
||||
|
||||
def _extract_outline_for_file(file_path: Path) -> tuple[list[dict], list[str]]:
|
||||
"""Return the document outline and fallback preview for *file_path*.
|
||||
|
||||
Looks for a sibling ``<stem>.md`` file produced by the upload conversion
|
||||
pipeline.
|
||||
|
||||
Returns:
|
||||
(outline, preview) where:
|
||||
- outline: list of ``{title, line}`` dicts (plus optional sentinel).
|
||||
Empty when no headings are found or no .md exists.
|
||||
- preview: first few non-empty lines of the .md, used as a content
|
||||
anchor when outline is empty so the agent has some context.
|
||||
Empty when outline is non-empty (no fallback needed).
|
||||
"""
|
||||
md_path = file_path.with_suffix(".md")
|
||||
if not md_path.is_file():
|
||||
return [], []
|
||||
|
||||
outline = extract_outline(md_path)
|
||||
if outline:
|
||||
logger.debug("Extracted %d outline entries from %s", len(outline), file_path.name)
|
||||
return outline, []
|
||||
|
||||
# outline is empty — read the first few non-empty lines as a content preview
|
||||
preview: list[str] = []
|
||||
try:
|
||||
with md_path.open(encoding="utf-8") as f:
|
||||
for line in f:
|
||||
stripped = line.strip()
|
||||
if stripped:
|
||||
preview.append(stripped)
|
||||
if len(preview) >= _OUTLINE_PREVIEW_LINES:
|
||||
break
|
||||
except Exception:
|
||||
logger.debug("Failed to read preview lines from %s", md_path, exc_info=True)
|
||||
return [], preview
|
||||
|
||||
|
||||
class UploadsMiddlewareState(AgentState):
|
||||
"""State schema for uploads middleware."""
|
||||
|
||||
uploaded_files: NotRequired[list[dict] | None]
|
||||
|
||||
|
||||
class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
|
||||
"""Middleware to inject uploaded files information into the agent context.
|
||||
|
||||
Reads file metadata from the current message's additional_kwargs.files
|
||||
(set by the frontend after upload) and prepends an <uploaded_files> block
|
||||
to the last human message so the model knows which files are available.
|
||||
"""
|
||||
|
||||
state_schema = UploadsMiddlewareState
|
||||
|
||||
def __init__(self, base_dir: str | None = None):
|
||||
"""Initialize the middleware.
|
||||
|
||||
Args:
|
||||
base_dir: Base directory for thread data. Defaults to Paths resolution.
|
||||
"""
|
||||
super().__init__()
|
||||
self._paths = Paths(base_dir) if base_dir else get_paths()
|
||||
|
||||
def _format_file_entry(self, file: dict, lines: list[str]) -> None:
|
||||
"""Append a single file entry (name, size, path, optional outline) to lines."""
|
||||
size_kb = file["size"] / 1024
|
||||
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
|
||||
lines.append(f"- {file['filename']} ({size_str})")
|
||||
lines.append(f" Path: {file['path']}")
|
||||
outline = file.get("outline") or []
|
||||
if outline:
|
||||
truncated = outline[-1].get("truncated", False)
|
||||
visible = [e for e in outline if not e.get("truncated")]
|
||||
lines.append(" Document outline (use `read_file` with line ranges to read sections):")
|
||||
for entry in visible:
|
||||
lines.append(f" L{entry['line']}: {entry['title']}")
|
||||
if truncated:
|
||||
lines.append(f" ... (showing first {len(visible)} headings; use `read_file` to explore further)")
|
||||
else:
|
||||
preview = file.get("outline_preview") or []
|
||||
if preview:
|
||||
lines.append(" No structural headings detected. Document begins with:")
|
||||
for text in preview:
|
||||
lines.append(f" > {text}")
|
||||
lines.append(" Use `grep` to search for keywords (e.g. `grep(pattern='keyword', path='/mnt/user-data/uploads/')`).")
|
||||
lines.append("")
|
||||
|
||||
def _create_files_message(self, new_files: list[dict], historical_files: list[dict]) -> str:
|
||||
"""Create a formatted message listing uploaded files.
|
||||
|
||||
Args:
|
||||
new_files: Files uploaded in the current message.
|
||||
historical_files: Files uploaded in previous messages.
|
||||
Each file dict may contain an optional ``outline`` key — a list of
|
||||
``{title, line}`` dicts extracted from the converted Markdown file.
|
||||
|
||||
Returns:
|
||||
Formatted string inside <uploaded_files> tags.
|
||||
"""
|
||||
lines = ["<uploaded_files>"]
|
||||
|
||||
lines.append("The following files were uploaded in this message:")
|
||||
lines.append("")
|
||||
if new_files:
|
||||
for file in new_files:
|
||||
self._format_file_entry(file, lines)
|
||||
else:
|
||||
lines.append("(empty)")
|
||||
lines.append("")
|
||||
|
||||
if historical_files:
|
||||
lines.append("The following files were uploaded in previous messages and are still available:")
|
||||
lines.append("")
|
||||
for file in historical_files:
|
||||
self._format_file_entry(file, lines)
|
||||
|
||||
lines.append("To work with these files:")
|
||||
lines.append("- Read from the file first — use the outline line numbers and `read_file` to locate relevant sections.")
|
||||
lines.append("- Use `grep` to search for keywords when you are not sure which section to look at")
|
||||
lines.append(" (e.g. `grep(pattern='revenue', path='/mnt/user-data/uploads/')`).")
|
||||
lines.append("- Use `glob` to find files by name pattern")
|
||||
lines.append(" (e.g. `glob(pattern='**/*.md', path='/mnt/user-data/uploads/')`).")
|
||||
lines.append("- Only fall back to web search if the file content is clearly insufficient to answer the question.")
|
||||
lines.append("</uploaded_files>")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
def _files_from_kwargs(self, message: HumanMessage, uploads_dir: Path | None = None) -> list[dict] | None:
|
||||
"""Extract file info from message additional_kwargs.files.
|
||||
|
||||
The frontend sends uploaded file metadata in additional_kwargs.files
|
||||
after a successful upload. Each entry has: filename, size (bytes),
|
||||
path (virtual path), status.
|
||||
|
||||
Args:
|
||||
message: The human message to inspect.
|
||||
uploads_dir: Physical uploads directory used to verify file existence.
|
||||
When provided, entries whose files no longer exist are skipped.
|
||||
|
||||
Returns:
|
||||
List of file dicts with virtual paths, or None if the field is absent or empty.
|
||||
"""
|
||||
kwargs_files = (message.additional_kwargs or {}).get("files")
|
||||
if not isinstance(kwargs_files, list) or not kwargs_files:
|
||||
return None
|
||||
|
||||
files = []
|
||||
for f in kwargs_files:
|
||||
if not isinstance(f, dict):
|
||||
continue
|
||||
filename = f.get("filename") or ""
|
||||
if not filename or Path(filename).name != filename:
|
||||
continue
|
||||
if uploads_dir is not None and not (uploads_dir / filename).is_file():
|
||||
continue
|
||||
files.append(
|
||||
{
|
||||
"filename": filename,
|
||||
"size": int(f.get("size") or 0),
|
||||
"path": f"/mnt/user-data/uploads/{filename}",
|
||||
"extension": Path(filename).suffix,
|
||||
}
|
||||
)
|
||||
return files if files else None
|
||||
|
||||
@override
|
||||
def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
|
||||
"""Inject uploaded files information before agent execution.
|
||||
|
||||
New files come from the current message's additional_kwargs.files.
|
||||
Historical files are scanned from the thread's uploads directory,
|
||||
excluding the new ones.
|
||||
|
||||
Prepends <uploaded_files> context to the last human message content.
|
||||
The original additional_kwargs (including files metadata) is preserved
|
||||
on the updated message so the frontend can read it from the stream.
|
||||
|
||||
Args:
|
||||
state: Current agent state.
|
||||
runtime: Runtime context containing thread_id.
|
||||
|
||||
Returns:
|
||||
State updates including uploaded files list.
|
||||
"""
|
||||
messages = list(state.get("messages", []))
|
||||
if not messages:
|
||||
return None
|
||||
|
||||
last_message_index = len(messages) - 1
|
||||
last_message = messages[last_message_index]
|
||||
|
||||
if not isinstance(last_message, HumanMessage):
|
||||
return None
|
||||
|
||||
# Resolve uploads directory for existence checks
|
||||
thread_id = (runtime.context or {}).get("thread_id")
|
||||
if thread_id is None:
|
||||
try:
|
||||
from langgraph.config import get_config
|
||||
|
||||
thread_id = get_config().get("configurable", {}).get("thread_id")
|
||||
except RuntimeError:
|
||||
pass # get_config() raises outside a runnable context (e.g. unit tests)
|
||||
uploads_dir = self._paths.sandbox_uploads_dir(thread_id) if thread_id else None
|
||||
|
||||
# Get newly uploaded files from the current message's additional_kwargs.files
|
||||
new_files = self._files_from_kwargs(last_message, uploads_dir) or []
|
||||
|
||||
# Collect historical files from the uploads directory (all except the new ones)
|
||||
new_filenames = {f["filename"] for f in new_files}
|
||||
historical_files: list[dict] = []
|
||||
if uploads_dir and uploads_dir.exists():
|
||||
for file_path in sorted(uploads_dir.iterdir()):
|
||||
if file_path.is_file() and file_path.name not in new_filenames:
|
||||
stat = file_path.stat()
|
||||
outline, preview = _extract_outline_for_file(file_path)
|
||||
historical_files.append(
|
||||
{
|
||||
"filename": file_path.name,
|
||||
"size": stat.st_size,
|
||||
"path": f"/mnt/user-data/uploads/{file_path.name}",
|
||||
"extension": file_path.suffix,
|
||||
"outline": outline,
|
||||
"outline_preview": preview,
|
||||
}
|
||||
)
|
||||
|
||||
# Attach outlines to new files as well
|
||||
if uploads_dir:
|
||||
for file in new_files:
|
||||
phys_path = uploads_dir / file["filename"]
|
||||
outline, preview = _extract_outline_for_file(phys_path)
|
||||
file["outline"] = outline
|
||||
file["outline_preview"] = preview
|
||||
|
||||
if not new_files and not historical_files:
|
||||
return None
|
||||
|
||||
logger.debug(f"New files: {[f['filename'] for f in new_files]}, historical: {[f['filename'] for f in historical_files]}")
|
||||
|
||||
# Create files message and prepend to the last human message content
|
||||
files_message = self._create_files_message(new_files, historical_files)
|
||||
|
||||
# Extract original content - handle both string and list formats
|
||||
original_content = last_message.content
|
||||
if isinstance(original_content, str):
|
||||
# Simple case: string content, just prepend files message
|
||||
updated_content = f"{files_message}\n\n{original_content}"
|
||||
elif isinstance(original_content, list):
|
||||
# Complex case: list content (multimodal), preserve all blocks
|
||||
# Prepend files message as the first text block
|
||||
files_block = {"type": "text", "text": f"{files_message}\n\n"}
|
||||
# Keep all original blocks (including images)
|
||||
updated_content = [files_block, *original_content]
|
||||
else:
|
||||
# Other types, preserve as-is
|
||||
updated_content = original_content
|
||||
|
||||
# Create new message with combined content.
|
||||
# Preserve additional_kwargs (including files metadata) so the frontend
|
||||
# can read structured file info from the streamed message.
|
||||
updated_message = HumanMessage(
|
||||
content=updated_content,
|
||||
id=last_message.id,
|
||||
additional_kwargs=last_message.additional_kwargs,
|
||||
)
|
||||
|
||||
messages[last_message_index] = updated_message
|
||||
|
||||
return {
|
||||
"uploaded_files": new_files,
|
||||
"messages": messages,
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user