Autonomous MCP GitHub Agent

An autonomous, Agentic AI application that manages your GitHub account through natural language.

Built using the cutting-edge Model Context Protocol (MCP), this project connects an ultra-fast LLM (LLaMA-3 via Groq) to the GitHub API. Instead of just chatting, the AI acts as an autonomous agent: it reasons, remembers conversation context, and executes complex tool calls (like creating repositories, managing files, and searching issues) directly on your GitHub account.

Demo (Note: Replace with your actual GIF path)

Key Features

Agentic Reasoning: The AI decides which tools to use and when to use them based on your natural language prompt.
Contextual Memory: The agent remembers previous steps in the conversation (e.g., "Create a repo called X... now add a file to that repo").
Model Context Protocol (MCP): Utilizes the official @modelcontextprotocol/server-github via local stdio to securely bridge the LLM and GitHub without exposing your token to the internet.
Advanced Prompt Engineering: Implements strict XML-based tool calling rules and edge-case handling (e.g., automatically creating .gitkeep files since Git doesn't support empty directories).
Interactive UI: A sleek, real-time chat interface built with Streamlit, featuring an action-tracking dropdown to show the AI's internal reasoning and tool execution steps.

Tech Stack

AI & LLM: LLaMA-3.3-70B-versatile via Groq API (using the OpenAI Python SDK)
Architecture: Agentic AI, Tool Calling, Model Context Protocol (MCP)
Backend: Python asyncio, Regex parsing
Frontend: Streamlit
Environment: Node.js (npx to run the MCP server)

Prerequisites

Before running this project, ensure you have the following installed on your machine:

Python 3.10+
Node.js & npm (Required to run the MCP server dynamically via npx)
A Groq API Key (Free at console.groq.com)
A GitHub Personal Access Token (PAT) with repo (Read & Write) permissions.

Installation & Setup

1. Clone the repository

git clone https://github.com/BouchraBenGhazala/Autonomous MCP Github Agent.git
cd your-repo-name

2. Create and activate a virtual environment (Recommended)

python -m venv env
# On Windows:
env\Scripts\activate
# On macOS/Linux:
source env/bin/activate

3. Install Python dependencies

pip install -r requirements.txt

4. Configure Environment Variables Create a .env file in the root directory of the project and add your API keys:

GITHUB_PERSONAL_ACCESS_TOKEN=ghp_YOUR_GITHUB_TOKEN_HERE
GROQ_API_KEY=gsk_YOUR_GROQ_API_KEY_HERE

(Make sure to add .env to your .gitignore file so you don't accidentally publish your secrets!)

Usage

Start the Streamlit application by running the following command in your terminal:

streamlit run app.py

This will open the web interface in your default browser.

Example Prompts to try:

"What are my latest repositories?"
"Create a new private repository called 'mcp-test-project'."
"Add a new file named README.md to the 'mcp-test-project' repository saying 'Hello World'."
"Search for the 3 latest issues in the langchain-ai/langchain repository."

How it works (Under the hood)

User Input: You ask the agent a question via the Streamlit UI.
System Prompt & Schema: The Python client fetches the available tools from the local Node.js MCP Server and passes them to LLaMA-3 as a JSON schema.
LLM Reasoning: The model analyzes the request and generates an XML-formatted <tool_call> block containing the exact tool name and required arguments.
Execution: Python parses the XML, triggers the local MCP server via asynchronous pipes (stdio), and the server securely communicates with the GitHub API.
Analysis & Response: The raw JSON result from GitHub is fed back into the LLM, which translates it into a clean, human-readable response displayed in the chat.

MCP Servers