CodeRAG

CodeRAG - Graph-Powered Code Analysis

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

CodeRAG is a revolutionary tool that builds a comprehensive graph database of your code structure using Neo4J. By mapping classes, methods, relationships, and dependencies, it enables AI assistants to understand your codebase at a deeper level and provide more accurate, context-aware assistance.

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

Supported Languages

TypeScript & JavaScript
Java
Python
C# (coming soon)

Quick Start

Get up and running in 5 minutes:

Clone and Install
git clone https://github.com/JonnoC/CodeRAG.git cd CodeRAG npm install
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
Configure Environment
cp .env.example .env # Edit .env with your Neo4J credentials
Scan Your First Project
npm run build npm run scan /path/to/your/project
Connect to Your AI AssistantAdd 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.

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

Example Use Cases

🕵️ 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

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

Documentation

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

Contributing

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

License

MIT - see LICENSE for details.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

An MCP server that transforms codebases into knowledge graphs using Neo4J, enabling AI assistants to understand code structure, relationships, and metrics for more context-aware assistance.

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