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🚀 FastAPI + FastMCP + LangChain Wikipedia Agent

An AI-powered Wikipedia research agent demonstrating how FastAPI, FastMCP, LangChain, and LangGraph can work together to build a modular tool-using AI application.

The project exposes Wikipedia capabilities through an MCP (Model Context Protocol) server and allows a LangChain/LangGraph agent to automatically discover and use those tools.

🏗️ Architecture

                    ┌──────────────────────┐
                    │       User           │
                    │   Natural Language   │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │       FastAPI        │
                    │    Application API   │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │   LangChain Agent    │
                    │      LangGraph       │
                    └──────────┬───────────┘
                               │
                         MCP Protocol
                               │
                               ▼
                    ┌──────────────────────┐
                    │      FastMCP         │
                    │     MCP Server       │
                    └──────────┬───────────┘
                               │
                    ┌──────────┴───────────┐
                    ▼                      ▼
             ┌──────────────┐      ┌──────────────┐
             │   Wikipedia  │      │   Wikipedia  │
             │    Search    │      │     Page     │
             └──────────────┘      └──────────────┘

Related MCP server: MCP Wikipedia Server

✨ Features

  • 🤖 LLM-powered Wikipedia research

  • 🔌 Model Context Protocol (MCP) integration

  • ⚡ FastAPI application layer

  • 🧠 LangChain agent integration

  • 🔄 LangGraph ReAct agent

  • 🔎 Wikipedia search tool

  • 📄 Wikipedia page retrieval tool

  • 🔗 MCP tool discovery

  • 📡 stdio-based MCP communication

  • 🧩 Modular architecture that can easily support additional tools

🛠️ Tech Stack

Technology

Purpose

Python

Core programming language

FastAPI

API/application layer

FastMCP

MCP server and tool implementation

LangChain

LLM and tool integration

LangGraph

Agent workflow

Requests

Wikipedia API requests

Wikipedia REST API

External knowledge source

🔧 MCP Tools

The FastMCP server exposes two tools.

search_wikipedia

Searches Wikipedia for a given topic.

@mcp.tool
def search_wikipedia(query: str):
    ...

Example:

Search Wikipedia for Albert Einstein

The agent can automatically decide to call:

search_wikipedia("Albert Einstein")

get_wikipedia_page

Retrieves the content of a specific Wikipedia page.

@mcp.tool
def get_wikipedia_page(title: str):
    ...

Example:

Get the Wikipedia page for Artificial Intelligence

The agent can call:

get_wikipedia_page("Artificial Intelligence")

🔄 How MCP Works in This Project

The MCP server runs using:

mcp.run(transport="stdio")

The LangChain client connects to the MCP server:

client = MultiServerMCPClient(
    {
        "wikipedia": {
            "command": "python",
            "args": ["server.py"],
            "transport": "stdio",
        }
    }
)

The client then discovers the available MCP tools:

tools = await client.get_tools()

These tools are passed to the LangGraph agent:

agent = create_react_agent(
    model,
    tools
)

The LLM can then decide which tool to use based on the user's request.

⚙️ Installation

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/langchain-mcp-wikipedia.git
cd langchain-mcp-wikipedia

2. Create a virtual environment

Windows:

python -m venv venv
venv\Scripts\activate

Linux/macOS:

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

🔐 Environment Variables

Create a .env file:

OPENAI_API_KEY=your_openai_api_key

🧠 Why MCP?

Traditional tool integration often tightly couples an LLM application with individual APIs.

MCP provides a standardized way to expose capabilities as tools.

LLM
 │
 ▼
LangChain / LangGraph
 │
 ▼
MCP Client
 │
 ▼
MCP Server
 │
 ├── Wikipedia
 ├── Search
 ├── Database
 ├── APIs
 └── Custom Tools

This separation makes tools reusable across different AI applications and agents.

📌 Key Concepts Demonstrated

This project is useful for learning:

  • Model Context Protocol (MCP)

  • FastMCP

  • MCP servers

  • MCP clients

  • stdio transport

  • LangChain tool integration

  • LangGraph agents

  • ReAct agents

  • FastAPI

  • External API integration

  • LLM tool calling

  • Modular AI agent architecture

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B
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–Maintainers
–Response time
–Release cycle
–Releases (12mo)
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