MCP Server for LangGraph Agent

FastMCP-based Model Context Protocol server for ChatGPT Enterprise integration with LangGraph agent.

🎉 Quick Start

The fastest way to get started:

./start.sh

This will:

Check if your LangGraph agent is running on port 2024
Start the MCP server on port 8000
Start the test UI on port 3005
Open http://localhost:3005 in your browser

Overview

This MCP server provides a standardized interface to interact with a LangGraph agent deployed on port 2024, compliant with ChatGPT Enterprise integration requirements.

✨ Latest Updates

January 2025 - LangGraph CLI Integration

✅ Refactored to use LangGraph CLI API architecture
✅ Updated to /runs, /runs/stream, /ok endpoints
✅ Changed message format to {"type": "human"}
✅ Added 3 new tools: health check, agent status, thread listing
✅ Fixed all async operations (no more blocking calls)
✅ Updated web UI to match new API structure

See REFACTORING_SUMMARY.md for detailed changes.

Features

✅ MCP Protocol 2025-06-18 compliant
✅ ChatGPT Enterprise compatible (SSE transport)
✅ OAuth 2.0 Authentication - Google OAuth and API key support
✅ FastMCP 2.13.0+ framework for production-ready deployment
✅ LangGraph CLI API integration
✅ 6 Tools: invoke_agent, stream_agent, check_system_health, check_agent_status, get_thread_state, list_threads
✅ 2 Resources: Agent health check and server info
✅ Prompts: Formatted agent queries
✅ Web Test UI: Interactive testing interface on port 3005
✅ Secure by Default: Optional authentication for production deployments

Installation

Install dependencies:

pip install -r requirements.txt

Configure environment (optional):

# Copy example configuration cp .env.example .env # Edit .env with your settings # For development without auth: OAUTH_ENABLED=false # For production with auth: OAUTH_ENABLED=true GOOGLE_CLIENT_ID=your-client-id GOOGLE_CLIENT_SECRET=your-client-secret API_KEYS=your-api-key-1,your-api-key-2

Generate credentials (if using OAuth):

python generate_credentials.py

See OAUTH_SETUP.md for detailed authentication setup.

Usage

Option 1: Quick Start Script (Recommended)

./start.sh

Option 2: Manual Start

Start MCP Server:

python src/agent_mcp/mcp_server.py

Start Test UI (optional):

cd web_ui && python server.py

Option 3: Using FastMCP CLI

python -m agent_mcp.mcp_server

Or using FastMCP CLI:

fastmcp run src/agent_mcp/mcp_server.py

For local development (STDIO):

fastmcp dev src/agent_mcp/mcp_server.py

Custom transport:

from agent_mcp.mcp_server import mcp # HTTP transport mcp.run(transport="http", host="0.0.0.0", port=8000, path="/mcp") # SSE transport (for ChatGPT Enterprise) mcp.run(transport="sse", host="0.0.0.0", port=8000)

Available Tools

1. `invoke_agent`

Execute a single invocation of the LangGraph agent.

{ "prompt": "What is the capital of France?", "thread_id": "optional-thread-id" }

2. `stream_agent`

Stream responses from the LangGraph agent.

{ "prompt": "Tell me a story", "thread_id": "optional-thread-id" }

3. `get_agent_state`

Retrieve the current state of a conversation thread.

{ "thread_id": "thread-id-to-query" }

Authentication

The MCP server supports three authentication methods for production deployments:

1. OAuth 2.0 (Google or Okta)

Enable user-based authentication with your preferred identity provider:

Google OAuth:

# .env configuration OAUTH_ENABLED=true OAUTH_PROVIDER=google GOOGLE_CLIENT_ID=your-client-id.apps.googleusercontent.com GOOGLE_CLIENT_SECRET=your-client-secret

Okta OAuth:

# .env configuration OAUTH_ENABLED=true OAUTH_PROVIDER=okta OKTA_DOMAIN=your-domain.okta.com OKTA_CLIENT_ID=your-okta-client-id OKTA_CLIENT_SECRET=your-okta-client-secret

OAuth Endpoints:

GET /auth/login - Initiate OAuth flow
GET /auth/callback - OAuth callback handler
GET /auth/logout - Logout
GET /auth/status - Check authentication status

Quick Start Guides:

2. API Key Authentication

Use API keys for service-to-service authentication:

# Generate API keys python generate_credentials.py # Add to .env API_KEYS=key1,key2,key3

Using API Keys:

# cURL curl -H "X-API-Key: your-api-key" http://localhost:8000/sse # Python headers = {"X-API-Key": "your-api-key"}

Testing Authentication

# Test OAuth setup python test_oauth.py # Or test manually curl http://localhost:8000/health # Public endpoint curl -H "X-API-Key: your-key" http://localhost:8000/sse # Protected

For detailed setup instructions, see

Resources

agent://health - Agent health check
agent://info - Agent capabilities and metadata

Prompts

agent_query_prompt - Format queries for the agent

ChatGPT Enterprise Integration

This server is designed for ChatGPT Enterprise integration with:

SSE Transport: Default transport for real-time streaming
MCP Protocol 2025-06-18: Latest stable protocol version
Proper Tool Schemas: Auto-generated from Python type hints
Context Support: Logging and progress reporting
Error Handling: Comprehensive error responses

ChatGPT Configuration

Add to your ChatGPT Enterprise MCP configuration:

{ "mcpServers": { "langgraph-agent": { "url": "http://your-server:8000/sse", "transport": "sse" } } }

Testing

Web UI Test Tool

We provide a beautiful web-based UI to test your MCP server and LangGraph agent:

# Start the test UI server cd web_ui python server.py

Then open http://localhost:3005 in your browser to:

Test MCP server connectivity
Test LangGraph agent connectivity
Invoke agent with custom prompts
Stream responses in real-time
View activity logs

See web_ui/README.md for details.

Unit Tests

Run tests:

pytest tests/test_mcp_server.py -v

Run all tests:

pytest

Development

Project Structure

agent-mcp-py/ ├── src/ │ └── agent_mcp/ │ ├── __init__.py │ └── mcp_server.py # FastMCP server implementation ├── tests/ │ ├── test_mcp_server.py # MCP server tests │ └── test_*.py # Other tests ├── requirements.txt # Production dependencies ├── requirements-dev.txt # Development dependencies └── README.md

Adding New Tools

from fastmcp import Context @mcp.tool() async def my_tool(param: str, ctx: Context = None) -> dict: """Tool description for ChatGPT.""" if ctx: await ctx.info(f"Processing: {param}") # Your logic here return {"result": "success"}

Adding Resources

@mcp.resource("custom://resource") async def my_resource() -> str: """Resource description.""" return "Resource content"

Architecture

┌─────────────────┐ │ ChatGPT │ │ Enterprise │ └────────┬──��─────┘ │ MCP/SSE │ ┌────────▼────────┐ │ FastMCP │ │ Server │ │ (Port 8000) │ └────────┬────────┘ │ HTTP │ ┌────────▼────────┐ │ LangGraph │ │ Agent │ │ (Port 2024) │ └─────────────────┘

Production Deployment

With Authentication

from fastmcp.server.auth.providers.google import GoogleProvider auth = GoogleProvider( client_id="your-client-id", client_secret="your-client-secret", base_url="https://your-domain.com" ) mcp = FastMCP( "LangGraph Agent Server", auth=auth )

Docker Deployment

FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY src/ ./src/ CMD ["python", "-m", "agent_mcp.mcp_server"]

License

MIT

LangGraph Agent MCP Server

MCP Server for LangGraph Agent

🎉 Quick Start

Overview

✨ Latest Updates

Features

Installation

Usage

Option 1: Quick Start Script (Recommended)

Option 2: Manual Start

Option 3: Using FastMCP CLI

Available Tools

1. `invoke_agent`

2. `stream_agent`

3. `get_agent_state`

Authentication

1. OAuth 2.0 (Google or Okta)

2. API Key Authentication

Testing Authentication

Resources

Prompts

ChatGPT Enterprise Integration

ChatGPT Configuration

Testing

Web UI Test Tool

Unit Tests

Development

Project Structure

Adding New Tools

Adding Resources

Architecture

Production Deployment

With Authentication

Docker Deployment

License

References

Related Resources

New MCP Servers

MCP directory API

MCP Server for LangGraph Agent

🎉 Quick Start

Overview

✨ Latest Updates

Features

Installation

Usage

Option 1: Quick Start Script (Recommended)

Option 2: Manual Start

Option 3: Using FastMCP CLI

Available Tools

1. invoke_agent

2. stream_agent

3. get_agent_state

Authentication

1. OAuth 2.0 (Google or Okta)

2. API Key Authentication

Testing Authentication

Resources

Prompts

ChatGPT Enterprise Integration

ChatGPT Configuration

Testing

Web UI Test Tool

Unit Tests

Development

Project Structure

Adding New Tools

Adding Resources

Architecture

Production Deployment

With Authentication

Docker Deployment

License

References

Related Resources

New MCP Servers

MCP directory API

1. `invoke_agent`

2. `stream_agent`

3. `get_agent_state`