📚 Code Graph RAG: Knowledge Graphs for Monorepo Search

Navigating a massive monorepo is notoriously difficult for standard code search tools. Code Graph RAG tackles this by combining knowledge graphs with LLMs to rethink how we query and understand multi-language codebases. It aims to solve the context-loss problem that typical vector-based code search suffers from. The technical direction here is quite interesting. By leveraging Abstract Syntax Trees (AST) and graph databases, it maps out the actual structural relationships and call graphs within the code. With its MCP server integration, it can feed this deep structural context directly into tools like Claude Code, allowing AI assistants to make more informed edits. Built with Python and holding over 2,200 stars, it represents a solid step toward structural code understanding. The hard part is maintaining graph accuracy at scale, but using knowledge graphs to ground AI in codebase realities is a highly relevant approach for developer tooling.