AAIDC Module 2 Project: Multi-Agent System for AI Project Analysis

🧠 Project Overview

This project implements a multi-agent system as part of the Agentic AI Developer Certification (AAIDC) – Module 2. The system analyzes GitHub repositories of AI/ML projects and provides actionable suggestions to improve their presentation, discoverability, and completeness.

The system uses CrewAI as the orchestration framework and includes 3 specialized agents with distinct roles:

1. Researcher Agent – Searches the web for similar projects
2. Writer Agent – Generates improvement suggestions
3. Reviewer Agent – Validates suggestions against repo content

All agents use free, open-source LLMs (Groq + Llama3) and no paid APIs, making it beginner-friendly and cost-free.

🛠️ Technical Implementation

Stack Used

• Orchestration: CrewAI (simple, powerful, beginner-friendly)
• LLM: Groq API with llama3-8b-8192 (free tier available)
• Tools:
  • Tavily Search Tool (free web search)
  • GitHub Repo Reader (built-in, no API key needed)
  • RAG Retriever (local README analysis)
• Agent Roles:
  • Researcher → Finds similar projects
  • Writer → Suggests improvements
  • Reviewer → Fact-checks suggestions

Pipeline Flow

1. User provides a GitHub repo URL
2. Researcher agent searches for similar projects using Tavily
3. Writer agent generates suggestions based on findings
4. Reviewer agent validates suggestions against actual repo content
5. Final report is returned to user

✅ Key Features

• ✅ 3+ agents with distinct roles
• ✅ Multi-agent collaboration via CrewAI
• ✅ Tool integration (web search, repo reader, RAG)
• ✅ Free LLM (Groq) — no OpenAI API key needed
• ✅ Clean, documented code with setup instructions

▶️ Sample Output

When given repo: https://github.com/R786P/P-art

Researcher Agent Output:

"Found 3 similar projects: [Project A], [Project B], [Project C]"

Writer Agent Output:

"Suggestion: Add a 'How to Run' section to your README."

Reviewer Agent Output:

"Confirmed: Your README does not have a 'How to Run' section."

Final Report:

"Your project can be improved by adding a 'How to Run' section. Similar projects include this section, and users often look for it."

📁 GitHub Repository

🔗 Repo Link: https://github.com/R786P/module2-multi-agent

The repository includes:

• main.py – CrewAI setup with 3 agents
• agents/ – Agent definitions
• tools/ – Custom tools (Tavily, GitHub reader)
• requirements.txt – Dependencies
• README.md – Setup and usage guide

🎯 Alignment with AAIDC Module 2 Requirements

🙏 Conclusion

This project demonstrates how multi-agent systems can solve complex problems through collaboration and tool use. I’m excited to build more advanced agents with memory, planning, and human-in-the-loop features in upcoming modules!

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Submitted for AAIDC Module 2 Review Cycle – December 2025