Content
# LangGraph Agents + MCP
[](README.md) [](README_KOR.md)
[](https://github.com/teddylee777/langgraph-mcp-agents)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/)
[](https://github.com/teddylee777/langgraph-mcp-agents)
## 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**: A user-friendly web interface for interacting with LangGraph `ReAct Agent` with MCP tools
- **Tool Management**: Add, remove, and configure MCP tools through the UI (Smithery JSON format supported). This is done dynamically without restarting the application
- **Streaming Responses**: View agent responses and tool calls in real-time
- **Conversation History**: Track and manage conversations with the agent
## MCP Architecture
The Model Context Protocol (MCP) consists of three main components:
1. **MCP Host**: Programs seeking to access data through MCP, such as Claude Desktop, IDEs, or LangChain/LangGraph.
2. **MCP Client**: A protocol client that maintains a 1:1 connection with the server, acting as an intermediary between the host and server.
3. **MCP Server**: A lightweight program that exposes specific functionalities through a standardized model context protocol, serving as the primary data source.
## Quick Start with Docker
You can easily run this project using Docker without setting up a local Python environment.
### Requirements (Docker Desktop)
Install Docker Desktop from the link below:
- [Install Docker Desktop](https://www.docker.com/products/docker-desktop/)
### Run with Docker Compose
1. Navigate to the `dockers` directory
```bash
cd dockers
```
2. Create a `.env` file with your API keys in the project root directory.
```bash
cp .env.example .env
```
Enter your obtained API keys in the `.env` file.
(Note) Not all API keys are required. Only enter the ones you need.
- `ANTHROPIC_API_KEY`: If you enter an Anthropic API key, you can use "claude-3-7-sonnet-latest", "claude-3-5-sonnet-latest", "claude-3-haiku-latest" models.
- `OPENAI_API_KEY`: If you enter an OpenAI API key, you can use "gpt-4o", "gpt-4o-mini" models.
- `LANGSMITH_API_KEY`: If you enter a LangSmith API key, you can use LangSmith tracing.
```bash
ANTHROPIC_API_KEY=your_anthropic_api_key
OPENAI_API_KEY=your_openai_api_key
LANGSMITH_API_KEY=your_langsmith_api_key
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_PROJECT=LangGraph-MCP-Agents
```
When using the login feature, set `USE_LOGIN` to `true` and enter `USER_ID` and `USER_PASSWORD`.
```bash
USE_LOGIN=true
USER_ID=admin
USER_PASSWORD=admin123
```
If you don't want to use the login feature, set `USE_LOGIN` to `false`.
```bash
USE_LOGIN=false
```
3. Select the Docker Compose file that matches your system architecture.
**AMD64/x86_64 Architecture (Intel/AMD Processors)**
```bash
# Run container
docker compose -f docker-compose.yaml up -d
```
**ARM64 Architecture (Apple Silicon M1/M2/M3/M4)**
```bash
# Run container
docker compose -f docker-compose-mac.yaml up -d
```
4. Access the application in your browser at http://localhost:8585
(Note)
- If you need to modify ports or other settings, edit the docker-compose.yaml file before building.
## Install Directly from Source Code
1. Clone this repository
```bash
git clone https://github.com/teddynote-lab/langgraph-mcp-agents.git
cd langgraph-mcp-agents
```
2. Create a virtual environment and install dependencies using uv
```bash
uv venv
uv pip install -r requirements.txt
source .venv/bin/activate # For Windows: .venv\Scripts\activate
```
3. Create a `.env` file with your API keys (copy from `.env.example`)
```bash
cp .env.example .env
```
Enter your obtained API keys in the `.env` file.
(Note) Not all API keys are required. Only enter the ones you need.
- `ANTHROPIC_API_KEY`: If you enter an Anthropic API key, you can use "claude-3-7-sonnet-latest", "claude-3-5-sonnet-latest", "claude-3-haiku-latest" models.
- `OPENAI_API_KEY`: If you enter an OpenAI API key, you can use "gpt-4o", "gpt-4o-mini" models.
- `LANGSMITH_API_KEY`: If you enter a LangSmith API key, you can use LangSmith tracing.
```bash
ANTHROPIC_API_KEY=your_anthropic_api_key
OPENAI_API_KEY=your_openai_api_key
LANGSMITH_API_KEY=your_langsmith_api_key
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_PROJECT=LangGraph-MCP-Agents
```
4. (New) Use the login/logout feature
When using the login feature, set `USE_LOGIN` to `true` and enter `USER_ID` and `USER_PASSWORD`.
```bash
USE_LOGIN=true
USER_ID=admin
USER_PASSWORD=admin123
```
If you don't want to use the login feature, set `USE_LOGIN` to `false`.
```bash
USE_LOGIN=false
```
## Usage
1. Start the Streamlit application.
```bash
streamlit run app.py
```
2. The application will run in the browser and display the main interface.
3. Use the sidebar to add and configure MCP tools
Visit [Smithery](https://smithery.ai/) to find useful MCP servers.
First, select the tool you want to use.
Click the COPY button in the JSON configuration on the right.
Paste the copied JSON string in the `Tool JSON` section.
<img src="./assets/add-tools.png" alt="tool json" style="width: auto; height: auto;">
Click the `Add Tool` button to add it to the "Registered Tools List" section.
Finally, click the "Apply" button to apply the changes to initialize the agent with the new tools.
<img src="./assets/apply-tool-configuration.png" alt="tool json" style="width: auto; height: auto;">
4. Check the agent's status.
5. Interact with the ReAct agent that utilizes the configured MCP tools by asking questions in the chat interface.
## Hands-on Tutorial
For developers who want to learn more deeply about how MCP and LangGraph integration works, we provide a comprehensive Jupyter notebook tutorial:
- Link: [MCP-HandsOn-KOR.ipynb](./MCP-HandsOn-KOR.ipynb)
This hands-on tutorial covers:
1. **MCP Client Setup** - Learn how to configure and initialize the MultiServerMCPClient to connect to MCP servers
2. **Local MCP Server Integration** - Connect to locally running MCP servers via SSE and Stdio methods
3. **RAG Integration** - Access retriever tools using MCP for document retrieval capabilities
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
This tutorial provides practical examples with step-by-step explanations that help you understand how to build and integrate MCP tools into LangGraph agents.
## License
MIT License
## References
- https://github.com/langchain-ai/langchain-mcp-adapters
