CodeRAG
Analyzes codebases and builds a comprehensive graph database using Neo4j to enable AI-powered code understanding and assistance.
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CodeRAG transforms your codebase into an intelligent knowledge graph using Neo4j, enabling AI assistants to understand your code at a deeper level. By mapping classes, methods, relationships, and dependencies, it empowers AI to provide more accurate, context-aware assistance with code reviews, onboarding, refactoring, documentation, and legacy analysis. It integrates with AI coding assistants through the Model Context Protocol (MCP), offering features like automated scanning, quality metric calculations, and guided prompts for code exploration.
Características Principales
- Automated scanning of TypeScript, JavaScript, Java, and Python projects
- Smart analysis to identify classes, methods, interfaces, inheritance, and dependencies
- Calculation of quality metrics like CK metrics and package coupling
- Integration with AI coding assistants like Claude Code, Windsurf, Cursor, and VS Code Continue
- Interactive workflows for code analysis and exploration
- 1 GitHub stars
Casos de Uso
- Identify tightly coupled code, circular dependencies, and architectural issues for refactoring
- Get AI assistance for code reviews with codebase context
- Help new team members quickly understand complex projects