mcpserver.so LogoMCPFly
Submit
LangChain Agent with MCP Servers logo

LangChain Agent with MCP Servers

by esakrissa

AI/AgentsLangChainAgentTavily SearchWeatherMathLangGraphReAct

A LangChain agent that utilizes Model Context Protocol (MCP) adapters for tool integration with various services. It demonstrates how to build an agent that dynamically selects and uses tools based on user queries.

View on GitHub

Last updated: N/A

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

# 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:

  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