PUBLICATION : GitHub Project Analyzer: Multi-Agent System for README Enhancement
GitHub repositories often suffer from inconsistent documentation quality. Developers focus on code but neglect README files, leaving users confused about installation, usage, and project purpose. This gap reduces project adoption and creates unnecessary friction for contributors.
I designed a multi-agent system that automatically analyzes GitHub repositories and provides actionable suggestions to improve documentation quality. The system leverages three specialized AI agents working in concert: a Researcher for gathering context, a Writer for generating suggestions, and a Reviewer for validation.
The implementation begins with the Researcher agent, which uses the Tavily Search API to find similar projects and best practices for the given repository. Unlike generic search systems, this agent specifically queries for documentation standards relevant to the repository's domain and technology stack.
The Writer agent then processes this research to generate concrete, actionable suggestions. For instance, if the repository lacks installation instructions, the Writer might suggest: "Add a 'How to Install' section with pip commands." The agent's prompts are carefully crafted to produce Hindi-language output that's both professional and accessible.
The Reviewer agent validates each suggestion against the actual repository content. This critical step prevents generic or inaccurate recommendations by ensuring every suggestion is grounded in the repository's reality.
I tested the system on 15 diverse GitHub repositories spanning different domains (web development, data science, machine learning). The results show:
The system demonstrates particular strength in identifying missing documentation elements like license files, installation instructions, and usage examples.
Robustness was a key design consideration. The system includes multiple fallback mechanisms:
This layered approach ensures the system remains functional even under adverse conditions.
While the system operates autonomously, I've incorporated basic human oversight capabilities. After generating suggestions, the system can prompt users for approval before implementing changes. This hybrid approach combines AI efficiency with human judgment, ensuring suggestions align with project maintainers' intentions.
The project is actively maintained with the following support structure:
Building this multi-agent system taught me that agent specialization is crucial for complex tasks. The Researcher-Writers-Reviewer pattern proved highly effective for documentation analysis, with each agent bringing unique capabilities to the workflow. Additionally, the importance of validation cannot be overstated ā without the Reviewer agent, suggestion quality dropped significantly.
This project is released under the MIT License, allowing free use, modification, and distribution while maintaining proper attribution to the original author.
This project implements a multi-agent system as part of the Agentic AI Developer Certification (AAIDC) ā 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:
All agents use free, open-source LLMs (Groq + Llama3) and no paid APIs, making it beginner-friendly and cost-free.
git clone https://github.com/R786P/aaidc-module2.gitpip install -r requirements.txt.env filepython multi_agent_system.pyUser ā š Researcher ā āļø Writer ā ā Reviewer ā Final Report
llama3-8b-8192 (free tier available)When given repo: https://github.com/R786P/aaidc-module2.git
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."
š Repo Link: https://github.com/R786P/module2-multi-agent
The repository includes:
main.py ā CrewAI setup with 3 agentsagents/ ā Agent definitionstools/ ā Custom tools (Tavily, GitHub reader)requirements.txt ā DependenciesREADME.md ā Setup and usage guide| Requirement | Status |
|---|---|
| Multi-Agent System (3+ agents) | ā |
| Tool Integration (3+ tools) | ā |
| Orchestration Framework (CrewAI) | ā |
| Clear communication between agents | ā |
| No external paid services | ā |
git clone https://github.com/R786P/aaidc-module2.git cd aaidc-module2 pip install -r requirements.txt python multi_agent_system.py ## š Conclusion This project demonstrates how multi-agent systems can solve complex problems through collaboration and tool use. Iām excited to build more advanced agents with memory, planning, and human-in-the-loop features in upcoming modules! *Built with ā¤ļø by Rahul Bunker ā Future Agentic AI Developer* --- *Submitted for AAIDC Module 2 Review Cycle ā December 2025*