🚀 Production-Ready Multi-Agent AI System for Intelligent Repository Analysis and Automation

🧠 Overview

This project represents the final capstone (Module 3) of the Agentic AI Developer Certification Program, building upon the foundation of Project 2: Multi-Agent System.
The system has been upgraded to a production-grade AI application, designed to analyze GitHub repositories, summarize project insights, and enhance documentation quality — all powered by a network of intelligent, collaborative AI agents.

---

🎯 Project Objectives

The goal of this project is to transform a functional prototype into a reliable, secure, and user-ready AI system by integrating:

• ✅ Comprehensive Testing Suite — Unit, integration, and end-to-end tests
• ✅ Safety & Guardrails — Input validation, output filtering, and error handling
• ✅ User Interface — A simple, intuitive Streamlit-based web UI
• ✅ Resilience & Monitoring — Retry logic, exception management, and logging
• ✅ Professional Documentation — Clear setup, usage, and maintenance guides

---

🧩 System Architecture

The system follows a modular multi-agent design:

multi-agent-system/
│
├── agents/
│ ├── analyzer_agent.py # Summarizes and interprets repository content
│ ├── improver_agent.py # Suggests improvements for documentation/code
│ ├── metadata_agent.py # Extracts key metadata (tech stack, dependencies)
│
├── tools/
│ ├── repo_reader.py # Reads repository structure and content
│ ├── web_search_tool.py # Fetches external information for enrichment
│
├── utils/
│ ├── guardrails.py # Input validation and safety filters
│ ├── logger.py # Logging and error tracing
│
├── ui/
│ ├── app.py # Streamlit-based web interface
│
├── tests/
│ ├── test_analyzer_agent.py
│ ├── test_metadata_agent.py
│ ├── test_improver_agent.py
│ ├── test_end_to_end.py
│
└── main.py # Orchestrates the end-to-end agent workflow.

Each agent interacts through clearly defined interfaces to ensure scalability, maintainability, and testability.

---

⚙️ Features

🧩 Multi-Agent Collaboration

• AnalyzerAgent: Generates structured summaries of repositories
• MetadataAgent: Extracts technologies and project purpose
• ImproverAgent: Suggests improvements to documentation clarity

🧰 Intelligent Tooling

• Repo Reader: Reads README and source files for context
• Web Search Tool: Performs DuckDuckGo searches for knowledge enrichment

🛡️ Safety & Guardrails

• Input validation for all agents
• Output sanitization to prevent unsafe content
• Graceful fallbacks when LLMs are unavailable

🧪 Testing & Reliability

• Unit, integration, and end-to-end tests with pytest
• DummyLLM support for deterministic test results

• 70% coverage on all core workflows

💻 User Interface

A clean Streamlit app allows users to:

• Enter a local repository path
• Run full analysis with one click
• View structured JSON summaries

---

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/yourusername/multi-agent-system.git
cd multi-agent-system
2️⃣ Install Dependencies:
pip install -r requirements.txt
3️⃣ Run Tests:
pytest -v
4️⃣ Launch the Streamlit App:
cd ui
streamlit run app.py

🧪 Example Output:
{
  "analysis": "This repository demonstrates an AI-powered system for repository analysis...",
  "metadata": {
    "tech_stack": ["Python", "LangChain", "Streamlit"],
    "purpose": "Automated documentation improvement"
  },
  "improvements": [
    "Add project architecture diagram",
    "Include contribution guidelines"
  ]
}