Code-Graph-RAG

Code-Graph-RAG parses a multi-language codebase with Tree-sitter, sharpened by compiler-grade frontends and runtime traces where available, builds a knowledge graph of its structure in Memgraph, and lets you query, edit, and optimise that code in plain English. It works across a monorepo of mixed languages under one unified graph schema.

1. Index: cgr start --update-graph parses a repository into a knowledge graph (sped up)

2. Ask: cgr start answers questions and edits code, grounded in that graph

Latest News 🔥

• Contextual Queries: Support for more detailed context queries has been added, including reasons and evaluation ingestor reads, enhancing the understanding of code interactions.
• Graph Improvements: The graph analysis capabilities have been expanded to follow cross-service links and implementors/overrides, providing a more comprehensive view of code dependencies.

See NEWS.md for the full history.

What It Does

Point Code-Graph-RAG at a repository and it reads every source file, extracts functions, classes, methods, modules, and the relationships between them, and stores the result as an interconnected graph. Once the graph exists you can:

• Ask questions about the codebase in natural language and get answers grounded in the real structure.
• Retrieve the actual source of any function, class, or method by name or by intent.
• Edit code through the agent with AST-based surgical patching and a diff preview before anything changes.
• Optimise code against language best practices or your own coding standards.
• Find dead code by walking call and reference edges from entry points.
• Search and rewrite structurally by AST pattern with ast-grep.
• Overlay runtime behaviour: trace a test run (or pull production eBPF profiles) with cgr trace and merge the calls that actually happened into the graph, exposing dispatch that static analysis cannot see.

How It Works

The system has two components:

1. Multi-language parser. A Tree-sitter based parser reads the codebase and ingests functions, classes, methods, modules, and their relationships into Memgraph under a single language-agnostic schema. Where a toolchain is available, compiler-grade frontends layer exact facts on top (libclang for C/C++, go/types for Go, and opt-in Roslyn, javac and Jedi for C#, Java and Python), and dynamic tracing merges calls observed at runtime. Tree-sitter stays the backbone: a trace only sees code that ran, and a compiler frontend only covers what its toolchain can build.
2. RAG system (codebase_rag/). An interactive CLI that turns natural language into Cypher queries, retrieves matching code, and drives AI-powered editing and optimisation.

Source Code -> Tree-sitter Parser -> AST Analysis -> Memgraph Knowledge Graph
                                                             |
User Query -> AI Model (Cypher Gen) -> Cypher Query -> Graph Results -> Response

See the Architecture Overview and Graph Schema for the full picture.

Supported Languages