What does this project demonstrate?
It demonstrates how to integrate MCP servers as tools in a LangGraph agent, using math and weather as example servers.
@hirokiyn
基本信息
LangGraph agent tutorial adapted to use MCP servers.
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It is a minimal, functional example of an agent powered by LangGraph where tools are implemented using MCP (Model Context Protocol) servers instead of traditional LangChain tools. It is based on the official LangGraph tutorial and is intended as a skeleton for building custom LangGraph agents with MCP tools.
Clone the repository, install dependencies using Poetry, and set the Anthropic API key in the .env file. Start the MCP servers (python src/mcp_servers/math_server.py and python src/mcp_servers/weather_server.py), then run poetry run main to launch the agent in interactive mode.
It demonstrates how to integrate MCP servers as tools in a LangGraph agent, using math and weather as example servers.
Clone the repository, install dependencies with Poetry, set your Anthropic API key in `.env`, start the MCP servers, and run `poetry run main` for interactive mode.
Yes, the LLM configuration can be changed in `src/common.py`.
The MCP servers are configured in `src/main.py`; you can modify the configuration to add or remove servers, or to change the transport mechanism.
The example tools are a math server and a weather server, each providing basic functions like arithmetic and weather data retrieval.
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