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Langchain-MCP
Agente Langchain con servidores MCP: usando adaptadores MCP de Langchain para la integración de herramientas.
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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:
- Signal Handling: Captures SIGINT and SIGTERM signals to initiate graceful shutdown
- Process Tracking: The main agent maintains a registry of all child processes
- Cleanup Process: Ensures all subprocesses are properly terminated on exit
- Shutdown Flags: Each MCP server has a shutdown flag to prevent new operations when shutdown is initiated
- Async Cooperation: Uses asyncio to allow operations in progress to complete when possible
Installation
# 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:
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:
- Create a new file in
src/mcpserver/
- Implement the server with proper signal handling
- Update
src/mcpserver/__init__.py
to expose the new server - Add the server configuration to
src/agent.py
License
MIT
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Reviews

user_4EuXQgMW
As an avid user, I must say MCP_Server_Spotify by Hashim9184 is phenomenal. The integration with Spotify is seamless, making music streaming delightful. The server's performance and reliability are top-notch, ensuring an uninterrupted musical experience. Highly recommend checking it out: https://mcp.so/server/MCP_Server_Spotify/Hashim9184.