Coderag

🚀 CodeRAG - Graph-Powered Code Analysis

Transform your codebase into an intelligent knowledge graph for AI-powered insights

CodeRAG is a revolutionary tool that constructs a comprehensive graph database of your code structure using Neo4J. By mapping classes, methods, relationships, and dependencies, it empowers AI assistants to gain a deeper understanding of your codebase and offer more accurate, context-aware assistance.

🚀 Quick Start

Get up and running in 5 minutes:

1. Clone and Install

   git clone https://github.com/JonnoC/CodeRAG.git
   cd CodeRAG
   npm install
   

2. Setup Neo4J Database (see our detailed guide for help)

   # Using Docker (easiest)
   docker run --name neo4j-coderag -p 7474:7474 -p 7687:7687 -d \
     --env NEO4J_AUTH=neo4j/your_password neo4j:5.12
   

3. Configure Environment

   cp .env.example .env
   # Edit .env with your Neo4J credentials
   

4. Scan Your First Project

   npm run build
   npm run scan /path/to/your/project
   

5. Connect to Your AI Assistant

   Add to your AI tool's MCP configuration:

   {
     "mcpServers": {
       "coderag": {
         "command": "node",
         "args": ["/path/to/CodeRAG/build/index.js"]
       }
     }
   }
   

📖 Read the Complete User Guide for detailed setup instructions, AI tool integrations, and advanced usage.

✨ Features

What CodeRAG Does

• 🔍 Smart Code Scanning - Automatically analyzes your codebase and builds a detailed graph of all classes, methods, interfaces, and their relationships
• 📊 Quality Insights - Calculates industry-standard metrics (CK metrics, package coupling, architectural patterns) to identify code smells and improvement opportunities
• 🤖 AI Integration - Connects seamlessly with AI coding assistants through the Model Context Protocol (MCP), giving them deep understanding of your code structure
• 🏗️ Architecture Analysis - Visualizes inheritance hierarchies, dependency chains, and architectural patterns to help you understand complex codebases

Perfect For

• Code Reviews - Get AI assistance that understands your entire codebase context
• Onboarding - Help new team members quickly understand large, complex projects
• Refactoring - Identify tightly coupled code, circular dependencies, and architectural issues
• Documentation - Generate insights about code relationships and design patterns
• Legacy Analysis - Map and understand inherited codebases with complex structures

Key Features

• 🔧 Automated Scanning - Parses TypeScript, JavaScript, Java, and Python projects
• 🎯 Smart Analysis - Identifies classes, methods, interfaces, inheritance, and dependencies
• 📈 Quality Metrics - CK metrics, package coupling, architectural issue detection
• 🤖 AI-Ready - Integrates with Claude Code, Windsurf, Cursor, VS Code Continue, and more
• 💡 Guided Prompts - Interactive workflows for code analysis and exploration
• 🔄 Dual Modes - STDIO for direct AI integration, HTTP for web-based tools

📦 Installation

Supported Languages

• TypeScript & JavaScript
• Java
• Python
• C# (coming soon)

💻 Usage Examples

🕵️ Code Investigation

"Show me all the classes that call the authenticate method"

Use find_classes_calling_method with method_name="authenticate"

🏗️ Architecture Review

"What are the architectural issues in this codebase?"

Use find_architectural_issues to detect circular dependencies, god classes, and high coupling

📊 Quality Assessment

"How complex is my UserService class?"

Use calculate_ck_metrics for class_id="com.example.UserService"

🔍 Dependency Analysis

"What does this class depend on and what depends on it?"

Use the find_dependencies prompt for interactive guidance

📚 Documentation

📚 Complete User Guide - Detailed setup, integrations, and workflows

📄 License

MIT - see LICENSE for details.

🔧 Technical Details

Common Commands

# Quick project scan
npm run scan /path/to/project

# Start for AI assistant integration  
npm start

# Run quality analysis
npm run scan /path/to/project -- --analyze

# Start web server for HTTP access
npm start -- --sse --port 3000

Contributing

Contributions welcome! Please read our contributing guidelines and submit pull requests to help improve CodeRAG.