langchain-mcp

# LangChain Agent with MCP Servers A LangChain agent using MCP Adapters for tool integration with Model Context Protocol (MCP) servers. ## Overview This project demonstrates how to build a LangChain agent that uses the Model Context Protocol (MCP) to interact with various services: - **Tavily Search**: Web search and news search capabilities - **Weather**: Mock weather information retrieval - **Math**: Mathematical expression evaluation The agent uses LangGraph's ReAct agent pattern to dynamically select and use these tools based on user queries. ## Features - **Graceful Shutdown**: All MCP servers implement proper signal handling for clean termination - **Subprocess Management**: The main agent tracks and manages all MCP server subprocesses - **Error Handling**: Robust error handling throughout the application - **Modular Design**: Easy to extend with additional MCP servers ## Graceful Shutdown Mechanism This project implements a comprehensive graceful shutdown system: 1. **Signal Handling**: Captures SIGINT and SIGTERM signals to initiate graceful shutdown 2. **Process Tracking**: The main agent maintains a registry of all child processes 3. **Cleanup Process**: Ensures all subprocesses are properly terminated on exit 4. **Shutdown Flags**: Each MCP server has a shutdown flag to prevent new operations when shutdown is initiated 5. **Async Cooperation**: Uses asyncio to allow operations in progress to complete when possible ## Installation ```bash # Clone the repository git clone https://github.com/yourusername/langchain-mcp.git cd langchain-mcp # Create a virtual environment python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate # Install dependencies pip install -e . ``` ## Configuration Create a `.env` file in the project root with the following variables: ``` OPENAI_API_KEY=your_openai_api_key TAVILY_API_KEY=your_tavily_api_key ``` ## Usage Run the agent from the command line: ```bash python src/agent.py ``` The agent will prompt for your query and then process it using the appropriate tools. ## Development To add a new MCP server: 1. Create a new file in `src/mcpserver/` 2. Implement the server with proper signal handling 3. Update `src/mcpserver/__init__.py` to expose the new server 4. Add the server configuration to `src/agent.py` ## License MIT