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MCP + LangGraph Agent

MCP + LangGraph Agent

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github.com/hirokiynAI & Agents16Updated 81w ago
Repository

About MCP + LangGraph Agent

MCP + LangGraph Agent is a minimal tutorial example for building LangGraph agents that use MCP servers as tools. It is aimed at developers and includes math and weather server examples plus an interactive CLI.

Key features

  1. 01LangGraph state and message routing
  2. 02MCP servers as agent tools
  3. 03Example math and weather servers
  4. 04Interactive command-line interface
  5. 05Configurable server transport

Use cases

  1. 01Build a LangGraph agent with MCP tools
  2. 02Explore agent state and message routing
  3. 03Try tool-based math and weather queries

MCP support

The agent uses MCP servers as its tools. The example includes math and weather servers, configured in `src/main.py`.

#agent#mcp#langchain#langgraph

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP + LangGraph Agent

This is a minimal, functional example of an agent powered by LangGraph with tools implemented using MCP (Model Context Protocol) servers instead of traditional LangChain tools. It's based on the official LangGraph tutorial, adapted to demonstrate how to integrate MCP servers as tools.

🧠 Use this repo as a skeleton to quickly build your own LangGraph agent with MCP tools!

Features

flowchart TD
    __start__ --> chatbot
    chatbot -.-> tools
    chatbot -.-> __end__
    tools --> chatbot
  • Integrates LangGraph for managing agent state and message routing.
  • Uses MCP servers to provide access to tools.
  • Includes example MCP servers for math and weather.
  • Provides a command-line interface for interacting with the agent.

Installation

  1. Clone the repository:

    git clone <repository_url>

  2. Install the dependencies using Poetry:

    poetry install

Usage

  1. Set the Anthropic API key in the .env file. You may need to create this file if it doesn't exist. For example:

    ANTHROPIC_API_KEY=your_api_key

  2. Start the MCP servers:

    • Math server: python src/mcp_servers/math_server.py
    • Weather server: python src/mcp_servers/weather_server.py

  3. Run the agent:

    poetry run main

    This will start the agent in interactive mode. You can then enter prompts, and the agent will respond using the tools provided by the MCP servers.

Example

User: What's (3 + 5) x 12?
Assistant: The result of (3 + 5) × 12 = 96
User: What is the weather in New York?
Assistant: It's always sunny in New York.

MCP Server Configuration

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.

LLM Configuration

The LLM used by the agent can be changed in src/common.py.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.