LangGraph Agent with MCP

Project Overview

LangChain-MCP-Adapters is a toolkit provided by LangChain AI that enables AI agents to interact with external tools and data sources through the Model Context Protocol (MCP). This project provides a user-friendly interface for deploying ReAct agents that can access various data sources and APIs through MCP tools.

Features

• Streamlit Interface: User-friendly web interface for interacting with LangGraph ReAct Agent with MCP tools
• Tool Management: Add, remove, and configure MCP tools directly through the UI(supports Smithery JSON Format). This happens dynamically without restarting the application.
• Streaming Responses: See agent responses and tool calls in real-time
• Conversation History: Track and manage your conversation with the agent

MCP Architecture

MCP (Model Context Protocol) consists of three main components.

1. MCP Host: Programs that want to access data through MCP, such as Claude Desktop, IDEs, or LangChain/LangGraph.

2. MCP Client: Protocol clients that maintain 1:1 connections with servers, acting as intermediaries between hosts and servers.

3. MCP Server: Lightweight programs that expose specific functionalities through the standardized model context protocol, serving as key data sources.

Installation

1. Clone this repository

git clone https://github.com/yourusername/langgraph-mcp-agents.git
cd langgraph-mcp-agents

2. Create a virtual environment and install dependencies using uv

uv venv
uv pip install -r requirements.txt
source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

3. Create a .env file with your API keys(from .env.example)

ANTHROPIC_API_KEY=your_anthropic_api_key
OPENAI_API_KEY=your_openai_api_key(optional)
TAVILY_API_KEY=your_tavily_api_key(optional)
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=your_langsmith_api_key
LANGSMITH_PROJECT=your_langsmith_project

Usage

1. Start the Streamlit application.

streamlit run app.py

2. The application will launch in your browser, displaying the main interface.

3. Use the sidebar to add and configure MCP tools

You may visit to Smithery to find useful MCP servers.

First, select the tool you want to use.

Press COPY button JSON Configurations on the right side.

Paste copied JSON string to the Tool JSON section.

Press Add Tool button to the "Registered Tools List" section.

Finally, "Apply" button to apply changes to initialize the agent with the new tools.

4. Check the status of the agent.

5. Ask questions in the chat interface to interact with the ReAct agent that utilizes the configured MCP tools.

Hands-on Tutorial

For developers who want to dive deeper into how MCP integration works with LangGraph, we've provided a comprehensive Jupyter notebook tutorial:

• link: MCP-HandsOn-ENG.ipynb

This hands-on tutorial covers:

1. MCP Client Setup - Learn how to configure and initialize MultiServerMCPClient for connecting to MCP servers
2. Local MCP Server Integration - Connect to a locally running MCP server via SSE and Stdio methods
3. RAG Integration - Use MCP to access a retriever tool for document search functionality
4. Mixed Transport Methods - Combine different transport protocols (SSE and Stdio) in a single agent
5. LangChain Tools + MCP - Integrate native LangChain tools alongside MCP tools

The tutorial walks through practical examples with step-by-step explanations to help you understand how to build and integrate MCP tools into your LangGraph agents.

License

MIT License

References

• https://github.com/langchain-ai/langchain-mcp-adapters