Content
# LangGraph ReAct Agent with MCP
[](https://github.com/langchain-ai/react-agent/actions/workflows/unit-tests.yml)
[](https://github.com/langchain-ai/react-agent/actions/workflows/integration-tests.yml)
[](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/react-agent)
This template showcases a [ReAct agent](https://arxiv.org/abs/2210.03629) implemented using [LangGraph](https://github.com/langchain-ai/langgraph) and the [Model Context Protocol (MCP)](https://modelcontextprotocol.io). The agent uses MCP servers to provide tools and capabilities through a unified gateway.
## Architecture
The system consists of three main components:
1. **MCP Gateway Server**: A server that:
- Manages multiple MCP server processes
- Provides a unified API for accessing tools
- Handles communication with MCP servers
- Exposes tools through a simple HTTP interface
2. **MCP Servers**: Individual servers that provide specific capabilities:
- Filesystem Server: File operations (read, write, list, search)
- Memory Server: Knowledge graph operations (entities, relations, queries)
- Additional servers can be added for more capabilities
3. **ReAct Agent**: The agent that:
- Connects to the MCP gateway
- Discovers available tools
- Uses tools to accomplish tasks
- Combines capabilities from multiple servers
## Getting Started
### 1. Install Dependencies
```bash
# Install the agent package
pip install -e .
# Install the gateway package
cd gateway
pip install -e .
cd ..
```
### 2. Configure MCP Servers
The gateway server is configured through `gateway/config.json`. By default, it starts two MCP servers:
```json
{
"mcp": {
"servers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/directory"
]
},
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}
}
```
You can add more servers from the [official MCP servers repository](https://github.com/modelcontextprotocol/servers).
### 3. Start the Gateway Server
```bash
cd gateway
python -m mcp_gateway.server
```
The server will start on port 8808 by default.
### 4. Configure the Agent
The agent's connection to the gateway is configured in `langgraph.json`:
```json
{
"dependencies": ["."],
"graphs": {
"agent": "./src/react_agent/graph.py:graph"
},
"env": ".env",
"mcp": {
"gateway_url": "http://localhost:8808"
}
}
```
### 5. Use the Agent
Open your app in LangGraph! Install guide [here](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/#launch-langgraph-server).
This will open a new browser window with the agent running. The agent will automatically:
1. Connect to the local gateway server
2. Discover available tools
3. Make tools available for use in conversations
## Available Tools
The agent has access to tools from both MCP servers:
### Filesystem Tools
- `read_file`: Read file contents
- `write_file`: Create or update files
- `list_directory`: List directory contents
- `search_files`: Find files matching patterns
- And more...
### Memory Tools
- `create_entities`: Add entities to knowledge graph
- `create_relations`: Link entities together
- `search_nodes`: Query the knowledge graph
- And more...
## Development
### Adding New MCP Servers
1. Find a server in the [MCP servers repository](https://github.com/modelcontextprotocol/servers)
2. Add its configuration to `gateway/config.json`
3. The agent will automatically discover its tools
### Customizing the Agent
- Modify the system prompt in `src/react_agent/prompts.py`
- Update the agent's reasoning in `src/react_agent/graph.py`
- Add new capabilities by including more MCP servers
## Documentation
- [LangGraph Documentation](https://github.com/langchain-ai/langgraph)
- [Model Context Protocol](https://modelcontextprotocol.io)
- [MCP Servers Repository](https://github.com/modelcontextprotocol/servers)
## License
This project is licensed under the MIT License - see the LICENSE file for details.
