Code-Graph-RAG is a retrieval-augmented generation system that enables querying, understanding, and editing multi-language codebases using AI and knowledge graphs.
The tool addresses the challenge of navigating and comprehending large monorepos by building a semantic knowledge graph of code structure. It parses source files across multiple programming languages using tree-sitter, extracts abstract syntax trees, and stores the resulting code relationships in a graph database. This graph representation allows an LLM to retrieve contextually relevant code segments when answering questions or suggesting edits, rather than relying on simple text search or token limits.
The project suits teams working with large, multi-language monorepos who need to understand cross-module dependencies and make informed code changes. It integrates with Claude through an MCP server interface, allowing developers to query their codebase directly within Claude Code or other compatible tools. The tool is designed for scenarios where traditional code search or IDE navigation becomes unwieldy, and where semantic understanding of relationships between code components matters more than keyword matching.
The project maintains active continuous integration with automated testing and code quality checks. Development follows a structured approach with security assessments and best practices compliance tracked through multiple external standards. The codebase demonstrates consistent attention to code coverage and quality metrics across releases.
