LangGraph FastAPI MCP Server & Agent Platform

A robust, enterprise-grade integration framework that combines LangGraph (agentic workflows) with FastAPI MCP Servers (Model Context Protocol). This architecture enables LLM-powered agents to communicate securely and dynamically with downstream microservices via Server-Sent Events (SSE) transport.

Technology Stack

• FastAPI: A modern, high-performance web framework for building APIs with Python 3.11+.
• FastAPI-MCP: An open-source library that exposes FastAPI endpoints as Model Context Protocol (MCP) tools.
• MCP (Model Context Protocol): An open standard that facilitates seamless interaction between LLMs and external data/tools.
• LangChain: An open-source framework for building applications powered by large language models.
• LangGraph: A framework for building stateful, multi-actor applications with LLMs, ideal for agentic loops.
• Gradio: An open-source library used to build the high-fidelity web chat interface.
• LangSmith: An observability platform for tracing, debugging, and monitoring LLM applications.
• uv: An extremely fast Python package manager and resolver.

📊 System Architecture

The diagram below details the integration between the chatbot agent UI, the LangGraph orchestration engine, the MCP client gateway, and the FastAPI service backend.

graph TD
    User([User / Operator]) <-->|Chat Interface| Gradio[Gradio Web UI]
    Gradio <-->|Interacts with| LangGraphAgent[LangGraph ReAct Agent]
    LangGraphAgent <-->|Invokes Tools via| MCPClient[MCP Multi-Server Client]
    MCPClient <-->|SSE Transport| FastAPIMCP[FastAPI MCP Server]
    FastAPIMCP <-->|Resolves Routes| APIRoutes[FastAPI Endpoints]
    APIRoutes <-->|CRUD Operations| SQLASession[SQLAlchemy AsyncSession]
    SQLASession <-->|Reads/Writes| SQLite[(SQLite Database)]

🚀 Getting Started

1. Installation

Ensure you have uv installed.

Clone the repository and install all dependencies:

git clone https://github.com/gilish-tech/ai-shopping-assistant-mcp-server.git
cd ai-shopping-assistant-mcp-server
uv sync

2. Environment Configuration

Create a .env file in the project root:

# OpenAI Configuration
OPENAI_API_KEY=your-openai-api-key-here

# Optional: LangSmith Tracing & Observability
LANGCHAIN_TRACING_V2=true
LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
LANGCHAIN_API_KEY=your-langsmith-api-key-here
LANGCHAIN_PROJECT=langgraph-fastapi-mcp-server

3. Start the FastAPI MCP Service

Launch the FastAPI server which auto-exposes its routes as MCP tools:

uv run uvicorn server.main:app --host 0.0.0.0 --port 8000 --reload

• Interactive Swagger Docs: http://localhost:8000/docs
• MCP Endpoint: http://localhost:8000/mcp

4. Start the Agent Client

Launch the Gradio chat interface to interact with the LangGraph agent:

uv run chatbot.py

• Chat Web UI: http://localhost:7860

🛠️ Production Readiness & Deployment

To move this system into a production environment, follow these best practices:

1. Database Migrations: Apply changes to the schema using Alembic:

   uv run alembic upgrade head
   

2. Production Web Server: Run the FastAPI application using uvicorn with multiple workers or behind a reverse proxy (e.g., Nginx).
3. Security and Auth: Implement auth middleware in FastAPI and pass tokens through the SSE connection headers for tool execution control.
4. Persistent Memory: Replace the default in-memory SQLite checkpointer in LangGraph with a persistent store (e.g., PostgreSQL Checkpointer) for durable chat histories.

📄 License

Distributed under the MIT License. See LICENSE for details.

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Maintained by gilbert (@gilish-tech).