Project Overview
This system i built is an intelligent multi-agent system designed to analyze GitHub repositories and provide comprehensive, actionable recommendations to improve project presentation, documentation quality and discoverability on GitHub.
Problem Statement
Many developers and researchers create valuable AI/ML projects but face significant challenges:
Poor Discoverability: Projects are hard to find due to inadequate metadata and tags
Incomplete Documentation: Missing or unclear README files, setup instructions, and usage examples
Structural Issues: Lack of essential files (LICENSE, CONTRIBUTING.md, etc.)
Presentation Quality: Missing visual elements, badges, and professional formatting
User Experience: Unclear setup processes that prevent adoption
These issues prevent great projects from reaching their intended audience and limit their impact in the community.
Solution
A sophisticated 3-agent collaborative system powered by LangGraph that:
Automatically analyzes any GitHub repository
Identifies gaps in documentation, structure, and presentation
Generates prioritized recommendations (Critical, Recommended, Optional)
Provides specific examples and code snippets for improvements
Predicts impact of suggested changes
How It Works
User Input (GitHub URL)
Technical Architecture
Multi-Agent System Design
Agent 1: Repo Analyzer
Role: Data collection and initial analysis
Responsibilities:
Fetch GitHub repository metadata
Read and parse README content
Analyze directory structure
Identify present and missing elements
Tools Used: GitHub API, README fetcher, structure analyzer
Output: Comprehensive repository analysis
Agent 2: Content Improver
Role: Improvement suggestion generation
Responsibilities:
Review findings from Repo Analyzer
Generate prioritized suggestions
Provide specific examples
Recommend metadata enhancements
Tools Used: Keyword extractor, suggestion engine
Output: Categorized improvement recommendations
Agent 3: Review Coordinator**
Tool Integration (5 Custom Tools)
get_repo_info - GitHub API Tool
Fetches repository metadata (stars, forks, topics, license)
Returns structured information about repository status
fetch_readme - README Content Retriever
Retrieves README content from repositories
Handles multiple branches and naming conventions
analyze_repo_structure- Structure Analyzer
Examines file and folder organization
Identifies key files (LICENSE, tests, CI/CD)
Returns comprehensive structure analysis
generate_keywords- Keyword Extractor
Extracts relevant technology keywords from text
Suggests topics and tags for better SEO
suggest_improvements - Recommendation Engine
Generates categorized improvement suggestions
Prioritizes by impact (Critical/Recommended/Optional)
Orchestration Framework
LangGraph Implementation:
Sequential state graph with clear agent transitions
Shared state object for data passing between agents
Message-based communication protocol
Persistent state with MemorySaver
Comprehensive error handling at each node
🛠️ Technology Stack
| Component | Technology | Version |
| Language| Python | 3.8+ |
✨ Key Features
For Users
✅ Interactive CLI with beautiful terminal formatting
✅ Command-line mode for automation and scripting
✅ Markdown report generation with professional formatting
✅ Export functionality to save reports as files
✅ Batch processing capability for multiple repositories
For Developers
✅ Modular architecture - Easy to extend with new agents or tools
✅ Comprehensive error handling - Graceful failure recovery
✅ Type hints throughout - Better IDE support and code clarity
✅ Well-documented code- Docstrings and inline comments
✅ Test suite included - Verification of all components
Technical Excellence
✅ Production-ready code - Proper structure and organization
✅ State management - LangGraph StateGraph with persistence
✅ Tool composition- Multiple tools working together
✅ Agent coordination - Clear communication patterns
✅ Comprehensive documentation- Multiple doc files
Installation & Setup
Prerequisites
Python 3.8 or higher
OpenAI API key
GitHub Personal Access Token
Quick Start
bash
Clone repository
git clone https://github.com/saphaniox/publication-assistant-2
Create virtual environment
python -m venv venv
Install dependencies
pip install -r requirements.txt
cp .env.example .env
Edit .env and add your OPENAI_API_KEY
Run tests
python test_setup.py
Start using
python main.py
🧪Testing & Validation
Automated Testing
The project includes test_setup.py which verifies:
✅ All dependencies installed correctly
✅ Configuration is valid
✅ All agents instantiate properly
✅ Workflow compiles successfully
✅ All tools are available
Manual Testing
Tested with multiple real repositories:
LangChain/LangGraph
Microsoft/AutoGen
Various open-source ML projects
Personal projects with different structures
Results
All agents execute in proper sequence
Tools return accurate data
State passes correctly between agents
Final reports are comprehensive and actionable
Error handling works gracefully
🔮 Future Enhancements
Planned Features
Human-in-the-loop approval steps
Evaluation metrics and benchmarking
Support for GitLab and Bitbucket
Web-based UI (FastAPI + React)
Caching for faster repeated analyses
A/B testing of improvement strategies
Advanced Integrations
Model Context Protocol (MCP) support
Automated PR generation with suggestions
Integration with documentation sites
CI/CD pipeline integration
Slack/Discord notifications
Documentation
Available Documentation
README.md- Main project documentation with quick start
SETUP.md - Detailed setup instructions with troubleshooting
PROJECT_SUBMISSION.md - AAIDC requirements compliance details
ARCHITECTURE.md - System architecture with diagrams
This file (PUBLICATION.md) - Comprehensive project publication
Code Documentation
Comprehensive docstrings for all functions and classes
Type hints throughout the codebase
Inline comments explaining complex logic
Example code in docstrings
📧 Contact & Contribution
GitHub: @saphaniox https://github.com/saphaniox/publication-assistant-2