🚀 Code Intel

AI-Powered Codebase Analysis Platform

Understand any codebase in seconds, not hours

Live Demo • Features • Quick Start • Documentation

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🎯 What is Code Intel?

Code Intel is an intelligent code analysis platform that combines static analysis, AST parsing, and LLM reasoning to help developers understand complex codebases, identify issues, and improve code quality—all in real-time.

Simply connect your GitHub repository and get:

• 🔍 Deep code analysis - Security vulnerabilities, performance bottlenecks, anti-patterns
• 🔄 Circular dependency detection - Using graph traversal algorithms
• 📊 Code duplication tracking - With exact file/line references
• 🤖 AI-powered insights - Context-aware recommendations from specialized agents
• ⚡ Real-time results - Stream analysis progress via WebSocket

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✨ Features

🔌 GitHub Integration

One-click repository scanning directly from GitHub URLs. No manual uploads needed.

🧠 Multi-Agent Analysis

Specialized AI agents analyze different aspects:

• Security Agent - Identifies vulnerabilities and security risks
• Performance Agent - Detects bottlenecks and optimization opportunities
• Architecture Agent - Reviews code structure and design patterns

🎨 AST-Based Code Understanding

Parse and analyze code structure using Abstract Syntax Trees to identify:

• Code patterns and anti-patterns
• Duplicate code blocks with exact references
• Structural issues and complexity hotspots

📈 RAG Pipeline

Vector embeddings (ChromaDB) combined with traditional static analysis for context-aware insights that understand your entire codebase.

🎯 Graph Analysis

Directed graph traversal to detect circular dependencies and visualize module relationships.

💾 Export Results

Download comprehensive analysis reports as JSON for further processing or sharing.

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🛠️ Tech Stack

Backend:

• FastAPI (Python) - High-performance async API
• LangChain - Multi-agent orchestration
• ChromaDB - Vector database for embeddings
• OpenAI GPT-4 - LLM reasoning

Frontend:

• React 18 - Modern UI framework
• WebSocket - Real-time progress updates
• Glassmorphism UI - Beautiful, responsive design

Infrastructure:

• PostgreSQL - Data persistence
• GitHub API - Repository integration
• Vercel - Deployment

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🚀 Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• OpenAI API key (Get one here)
• GitHub OAuth App (Setup guide)

1. Clone the Repository

git clone https://github.com/yourusername/code-intel.git
cd code-intel

2. Backend Setup

# Install Python dependencies
pip install -r requirements.txt

# Copy environment template
cp .env.example .env

# Edit .env and add your credentials
nano .env

Required environment variables:

# OpenAI API Key
OPENAI_API_KEY=sk-your-key-here

# GitHub OAuth (see setup guide below)
GITHUB_CLIENT_ID=your-client-id
GITHUB_CLIENT_SECRET=your-client-secret

# Server URLs (for local development)
BACKEND_URL=http://localhost:8000
FRONTEND_URL=http://localhost:3000

# Vector DB
VECTOR_DB_PATH=./chroma_db
EMBEDDING_MODEL=all-MiniLM-L6-v2

3. Frontend Setup

# Navigate to web directory
cd web

# Install dependencies
npm install

# Start development server
npm start

4. Start the Backend

# From project root
python api.py

5. Access the App

Open http://localhost:3000 in your browser 🎉

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🔐 GitHub OAuth Setup

To enable GitHub repository scanning, you need to create a GitHub OAuth App:

Step 1: Create OAuth App

1. Go to GitHub Developer Settings
2. Click “New OAuth App”
3. Fill in the details:

• Application name: Code Intel (Local Dev)
• Homepage URL: http://localhost:3000
• Authorization callback URL: http://localhost:8000/auth/github/callback

1. Click “Register application”

Step 2: Get Credentials

1. Copy your Client ID
2. Click “Generate a new client secret” and copy it
3. Add both to your .env file:

GITHUB_CLIENT_ID=your_client_id_here
GITHUB_CLIENT_SECRET=your_client_secret_here

Step 3: Restart Backend

# Kill the running server and restart
python api.py

✅ GitHub integration is now ready!

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📖 Usage

Analyze a Repository

1. Enter Repository URL

https://github.com/username/repository

Configure Analysis (optional)

• Branch: main (default)
• File patterns: *.py, *.js, *.ts (customize as needed)

Start Analysis

• Click “Analyze Repository”
• Watch real-time progress via WebSocket
• View results in interactive file explorer

Review Results

• Expandable file tree showing all issues
• Detailed descriptions with severity levels
• Code snippets with exact line references
• Export as JSON for further analysis

Example Repositories to Try

Repository	Language	Complexity
pallets/flask	Python	Medium
django/django	Python	High
fastapi/fastapi	Python	Medium
facebook/react	JavaScript	High

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🏗️ Architecture

┌──────────────────────────────────────────────────────────────┐
│                         Code Intel                            │
└──────────────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
│               │               │
┌────▼────┐    ┌────▼────┐    ┌────▼────┐
│  React  │    │ FastAPI │    │ GitHub  │
│   Web   │◄──►│   API   │◄──►│   API   │
│  (3000) │ WS │  (8000) │    │         │
└─────────┘    └────┬────┘    └─────────┘
│
┌──────────────┼──────────────┐
│              │              │
┌────▼────┐   ┌────▼────┐   ┌────▼────┐
│ OpenAI  │   │ ChromaDB│   │  AST    │
│   API   │   │ Vector  │   │ Parser  │
│  (GPT4) │   │   DB    │   │         │
└─────────┘   └─────────┘   └─────────┘

Analysis Pipeline

1. Repository Cloning - Clone GitHub repo to temp directory
2. File Discovery - Identify relevant files based on patterns
3. AST Parsing - Parse code structure and extract metadata
4. Vector Embedding - Generate embeddings and store in ChromaDB
5. Static Analysis - Run traditional linters and analyzers
6. Graph Analysis - Build dependency graph and detect cycles
7. LLM Reasoning - Multi-agent analysis with GPT-4
8. Results Synthesis - Combine insights and generate report

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🎨 API Endpoints

Analysis

• POST /github/analyze - Start repository analysis

{
"repo_url": "https://github.com/user/repo",
"branch": "main",
"file_patterns": ["*.py"]
}

Status & Results

• GET /github/status/{job_id} - Check analysis status
• GET /github/results/{job_id} - Get analysis results
• WS /ws/progress/{job_id} - Real-time progress updates

Authentication

• GET /auth/github - Initiate GitHub OAuth flow
• GET /auth/github/callback - OAuth callback handler

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🐛 Troubleshooting

Common Issues

“GitHub OAuth not working”

• Verify GITHUB_CLIENT_ID and GITHUB_CLIENT_SECRET are set correctly
• Check callback URL matches: http://localhost:8000/auth/github/callback
• Make sure backend is running on port 8000

“OpenAI API errors”

• Verify your OPENAI_API_KEY is valid
• Check you have API credits remaining
• Ensure you’re using GPT-4 compatible key

“WebSocket connection failed”

• Backend must be running on port 8000
• Check CORS settings in api.py
• Verify FRONTEND_URL is set to http://localhost:3000

“Analysis stuck at X%”

• Large repos take time (5-10 minutes for 1000+ files)
• Check backend logs for errors
• Ensure sufficient disk space for temp cloning

Getting Help

• 📧 Open an issue
• 💬 Check existing discussions
• 🐛 Submit bug reports with logs and repo details

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🤝 Contributing

Contributions are welcome! Here’s how you can help:

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

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📝 License

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

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🙏 Acknowledgments

• OpenAI for GPT-4 API
• LangChain for agent orchestration framework
• ChromaDB for vector database
• FastAPI for excellent API framework
• React team for the UI library

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