🚀 Code Quality Intelligence Agent

AI-powered code analysis platform that provides comprehensive quality assessment, security vulnerability detection, and intelligent insights for your codebase.

Watch the DemoCLI Video

Watch the WebApp Video

✨ Features

🎯 Core Capabilities

• 🔍 Multi-Language Analysis: Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, Ruby, PHP.
• 🤖 AI-Powered Insights: Advanced analysis using Google Gemini AI
• 💬 Interactive Q&A: Natural language queries about your codebase
• 📊 Rich Reports: Comprehensive dashboards with actionable recommendations
• 🔗 GitHub Integration: Direct repository analysis from GitHub URLs.

🛡️ Analysis Categories

• Security: Vulnerability detection, injection flaws, authentication issues
• Performance: Bottlenecks, inefficient algorithms, resource optimization
• Code Quality: Duplication, complexity, naming conventions
• Maintainability: Technical debt, documentation, test coverage
• Best Practices: Language-specific patterns and conventions

🔧 Advanced Technology

• AST Parsing: Deep structural code analysis
• RAG Engine: Vector-based intelligent responses using FAISS
• Severity Scoring: Automated issue prioritization
• Real-time Updates: Live analysis progress tracking

🚀 Quick Start

Using Docker (Recommended)

# Clone the repository
git clone <repository-url>
cd code-quality-intelligence-agent

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Start all services
docker-compose up -d

# Access the application
open http://localhost:3000

Manual Setup

# Run setup script
chmod +x scripts/setup.sh
./scripts/setup.sh

# Start development servers
make dev

🛠️ Installation

Prerequisites

• Python 3.11+
• Node.js 18+
• Docker & Docker Compose
• MongoDB 7.0+

Environment Configuration

# Required
GEMINI_API_KEY=your_gemini_api_key_here

# Optional but recommended  
GITHUB_TOKEN=your_github_token_here

Step-by-Step Setup

1. Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Frontend Setup

cd frontend
npm install

3. Database Setup

# Using Docker
docker run -d --name mongodb -p 27017:27017 mongo:7.0

# Or install MongoDB locally

4. Start Services

# Backend
cd backend && uvicorn app.main:app --reload

# Frontend  
cd frontend && npm start

💻 Usage

Web Interface

1. 📁 Upload/Connect Code

   • Upload zip/tar archives
   • Connect GitHub repositories
   • Select analysis options

2. ⚡ Run Analysis

   • Real-time progress tracking
   • Multi-language detection
   • Comprehensive scanning

3. 📊 Review Results

   • Quality scores and metrics
   • Issue categorization
   • Actionable recommendations

4. 💬 Ask Questions

   • Natural language queries
   • Context-aware responses
   • Source attribution

CLI Usage

# Analyze local directory
python -m app.cli analyze /path/to/code

# Analyze GitHub repository
python -m app.cli analyze https://github.com/user/repo

# Interactive Q&A
python -m app.cli qa --report-id abc123

# Export reports
python -m app.cli export abc123 --format json

# Health check
python -m app.cli health

API Usage

# Start analysis
curl -X POST http://localhost:8000/api/v1/analyze \
  -F "source_type=github" \
  -F "source_path=https://github.com/user/repo"

# Check status
curl http://localhost:8000/api/v1/analyze/{report_id}/status

# Ask questions
curl -X POST http://localhost:8000/api/v1/ask \
  -H "Content-Type: application/json" \
  -d '{"question": "What are the security issues?"}'

🏗️ Architecture

graph TB
    A[Frontend React] --> B[FastAPI Backend]
    B --> C[Analysis Engine]
    B --> D[RAG Engine]
    B --> E[MongoDB]
    B --> F[FAISS Vector DB]
    B --> G[Gemini AI]
    C --> H[AST Parser]
    C --> I[Pattern Matcher]
    C --> J[Severity Scorer]

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Key Components

• Analysis Engine: Core code processing and issue detection
• AST Parser: Deep structural analysis for supported languages
• RAG Engine: Vector-based retrieval for intelligent Q&A
• Severity Scorer: Automated risk assessment and prioritization

🧪 Testing

# Run all tests
make test

# Backend only
cd backend && pytest tests/ -v --cov=app

# Frontend only  
cd frontend && npm test

# Integration tests
pytest tests/integration/ -v -m integration

📚 API Documentation

Interactive API documentation available at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Key Endpoints

Method	Endpoint	Description
POST	/api/v1/analyze	Start code analysis
GET	/api/v1/analyze/{id}/status	Get analysis status
GET	/api/v1/report/{id}	Retrieve analysis report
POST	/api/v1/ask	Ask questions about code

🚢 Deployment

Production Deployment

# Build and deploy
./scripts/deploy.sh production

# Or using Docker Compose
docker-compose -f docker-compose.prod.yml up -d

Kubernetes

# Apply manifests
kubectl apply -f k8s/

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Install pre-commit hooks
pre-commit install

# Format code
make format

# Run lints
make lint

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Google Gemini AI for advanced code analysis
• FAISS for efficient vector similarity search
• FastAPI for the robust backend framework
• React & Material-UI for the modern frontend
• MongoDB for flexible data storage

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Built with ❤️ for developers who care about code quality

Need help? Check our documentation or open an issue