mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 06:12:14 +08:00
1171598b2f
Add comprehensive MCP integration using langchain-mcp-adapters to enable pluggable external tools from MCP servers. Features: - MCP server configuration via mcp_config.json - Automatic lazy initialization for seamless use in both FastAPI and LangGraph Studio - Support for multiple MCP servers (filesystem, postgres, github, brave-search, etc.) - Environment variable resolution in configuration - Tool caching mechanism for optimal performance - Complete documentation and setup guide Implementation: - Add src/mcp module with client, tools, and cache components - Integrate MCP config loading in AppConfig - Update tool system to include MCP tools automatically - Add eager initialization in FastAPI lifespan handler - Add lazy initialization fallback for LangGraph Studio Dependencies: - Add langchain-mcp-adapters>=0.1.0 Documentation: - Add MCP_SETUP.md with comprehensive setup guide - Update CLAUDE.md with MCP system architecture - Update config.example.yaml with MCP configuration notes - Update README.md with MCP setup instructions Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
84 lines
2.1 KiB
Python
84 lines
2.1 KiB
Python
#!/usr/bin/env python
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"""
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Debug script for lead_agent.
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Run this file directly in VS Code with breakpoints.
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Usage:
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1. Set breakpoints in agent.py or other files
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2. Press F5 or use "Run and Debug" panel
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3. Input messages in the terminal to interact with the agent
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"""
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import asyncio
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import os
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import sys
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# Ensure we can import from src
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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# Load environment variables
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from dotenv import load_dotenv
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from langchain_core.messages import HumanMessage
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from src.agents import make_lead_agent
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load_dotenv()
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async def main():
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# Initialize MCP tools at startup
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try:
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from src.mcp import initialize_mcp_tools
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await initialize_mcp_tools()
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except Exception as e:
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print(f"Warning: Failed to initialize MCP tools: {e}")
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# Create agent with default config
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config = {
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"configurable": {
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"thread_id": "debug-thread-001",
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"thinking_enabled": True,
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# Uncomment to use a specific model
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"model_name": "deepseek-v3.2",
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}
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}
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agent = make_lead_agent(config)
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print("=" * 50)
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print("Lead Agent Debug Mode")
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print("Type 'quit' or 'exit' to stop")
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print("=" * 50)
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while True:
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try:
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user_input = input("\nYou: ").strip()
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if not user_input:
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continue
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if user_input.lower() in ("quit", "exit"):
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print("Goodbye!")
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break
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# Invoke the agent
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state = {"messages": [HumanMessage(content=user_input)]}
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result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
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# Print the response
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if result.get("messages"):
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last_message = result["messages"][-1]
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print(f"\nAgent: {last_message.content}")
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except KeyboardInterrupt:
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print("\nInterrupted. Goodbye!")
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break
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except Exception as e:
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print(f"\nError: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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asyncio.run(main())
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