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.

deer-flow/backend/tests/test_create_deerflow_agent_live.py

@@ -0,0 +1,106 @@
+"""Live integration tests for create_deerflow_agent.
+
+Verifies the factory produces a working LangGraph agent that can actually
+process messages end-to-end with a real LLM.
+
+Tests marked ``requires_llm`` are skipped in CI or when OPENAI_API_KEY is unset.
+"""
+
+import os
+import uuid
+
+import pytest
+from langchain_core.tools import tool
+
+requires_llm = pytest.mark.skipif(
+    os.getenv("CI", "").lower() in ("true", "1") or not os.getenv("OPENAI_API_KEY"),
+    reason="Requires LLM API key — skipped in CI or when OPENAI_API_KEY is unset",
+)
+
+
+def _make_model():
+    """Create a real chat model from environment variables."""
+    from langchain_openai import ChatOpenAI
+
+    return ChatOpenAI(
+        model=os.getenv("E2E_MODEL_ID", "ep-20251211175242-llcmh"),
+        base_url=os.getenv("E2E_BASE_URL", "https://ark-cn-beijing.bytedance.net/api/v3"),
+        api_key=os.getenv("OPENAI_API_KEY", ""),
+        max_tokens=256,
+        temperature=0,
+    )
+
+
+# ---------------------------------------------------------------------------
+# 1. Minimal creation — model only, no features
+# ---------------------------------------------------------------------------
+@requires_llm
+def test_minimal_agent_responds():
+    """create_deerflow_agent(model) produces a graph that returns a response."""
+    from deerflow.agents.factory import create_deerflow_agent
+
+    model = _make_model()
+    graph = create_deerflow_agent(model, features=None, middleware=[])
+
+    result = graph.invoke(
+        {"messages": [("user", "Say exactly: pong")]},
+        config={"configurable": {"thread_id": str(uuid.uuid4())}},
+    )
+
+    messages = result.get("messages", [])
+    assert len(messages) >= 2
+    last_msg = messages[-1]
+    assert hasattr(last_msg, "content")
+    assert len(last_msg.content) > 0
+
+
+# ---------------------------------------------------------------------------
+# 2. With custom tool — verifies tool injection and execution
+# ---------------------------------------------------------------------------
+@requires_llm
+def test_agent_with_custom_tool():
+    """Agent can invoke a user-provided tool and return the result."""
+    from deerflow.agents.factory import create_deerflow_agent
+
+    @tool
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    model = _make_model()
+    graph = create_deerflow_agent(model, tools=[add], middleware=[])
+
+    result = graph.invoke(
+        {"messages": [("user", "Use the add tool to compute 3 + 7. Return only the result.")]},
+        config={"configurable": {"thread_id": str(uuid.uuid4())}},
+    )
+
+    messages = result.get("messages", [])
+    # Should have: user msg, AI tool_call, tool result, AI final
+    assert len(messages) >= 3
+    last_content = messages[-1].content
+    assert "10" in last_content
+
+
+# ---------------------------------------------------------------------------
+# 3. RuntimeFeatures mode — middleware chain runs without errors
+# ---------------------------------------------------------------------------
+@requires_llm
+def test_features_mode_middleware_chain():
+    """RuntimeFeatures assembles a working middleware chain that executes."""
+    from deerflow.agents.factory import create_deerflow_agent
+    from deerflow.agents.features import RuntimeFeatures
+
+    model = _make_model()
+    feat = RuntimeFeatures(sandbox=False, auto_title=False, memory=False)
+    graph = create_deerflow_agent(model, features=feat)
+
+    result = graph.invoke(
+        {"messages": [("user", "What is 2+2?")]},
+        config={"configurable": {"thread_id": str(uuid.uuid4())}},
+    )
+
+    messages = result.get("messages", [])
+    assert len(messages) >= 2
+    last_content = messages[-1].content
+    assert len(last_content) > 0