# vitali87/code-graph-rag

The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs

Repository: https://github.com/vitali87/code-graph-rag
Canonical: https://ross.abutalabs.com/products/code-graph-rag
Homepage: https://code-graph-rag.com
Language: Python
License: MIT
License Family: permissive
Topics: ai, ast, claude-code, code-analysis, code-understanding, codebase-search, developer-tools, graph-database, knowledge-graph, llm, mcp, mcp-server, memgraph, monorepo, multi-language, python, rag, retrieval-augmented-generation, semantic-search, tree-sitter
Last push: 2026-08-26T21:43:51+00:00

## Health v2 (maintenance only)
Score: 85/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 31
- inputs: {"age_days": 443, "days_push": 7, "days_rel": 7, "gap_med": 3.5, "n_releases_24m": 21}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4828, forks 638 (observed 2026-08-28T04:09:00.594675+00:00)

## What it is
Code-Graph-RAG is a Python CLI tool and MCP server that parses multi-language codebases with Tree-sitter, builds a knowledge graph of code structure in Memgraph, and enables natural-language querying, editing, and optimization of code. It supports 14 languages under one unified graph schema and integrates with AI assistants like Claude Code.

## Use cases
- understand a large multi-language monorepo with AI
- ask natural language questions about codebase structure
- find which functions handle authentication across services
- trace data flow and taint through code
- detect dead code and duplicate code
- structurally search and replace code by AST pattern
- integrate codebase intelligence into Claude Code via MCP

## When to choose
- you maintain a large monorepo spanning multiple languages
- grep and IDE find-references can't answer cross-language structural questions
- you want an MCP server giving AI assistants deep codebase context
- you need call-chain, dependency, or data-flow analysis as graph queries

## When to avoid
- you have a small single-language project where grep and IDE tooling suffice
- you don't want to run Memgraph and Qdrant infrastructure locally
- your language is unsupported (e.g. Scala is still in development)
- you need fully offline analysis without any LLM provider

## Facets
- artifact type: cli-tool
- maturity: active
- function: rag, parser, search-engine, nlp, llm-inference, mcp, developer-tools, code-review
- domain: developer-tools, large-language-models, parsers, programming-languages
- platform: python, cli, cross-platform
- tags: knowledge-graph, tree-sitter, memgraph, code-analysis, monorepo, ast, cypher, semantic-search, mcp-server, static-analysis, retrieval-augmented-generation, docker

## Member repositories
- vitali87/code-graph-rag (main) score 85

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.594675+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:30.461566+00:00, confidence not recorded.
  - readme: https://github.com/vitali87/code-graph-rag (fetched 2026-08-28T04:09:00.594675+00:00, sha d719426138de)
  - homepage: https://code-graph-rag.com (fetched 2026-08-29T09:01:23.685886+00:00, sha 293487643348)
  - site_page: https://code-graph-rag.com/features (fetched 2026-08-29T09:01:23.695553+00:00, sha e6c3c8114428)
  - site_page: https://docs.code-graph-rag.com (fetched 2026-08-29T09:01:23.697678+00:00, sha e97a6b4b5cff)
  - site_page: https://code-graph-rag.com/about (fetched 2026-08-29T09:01:23.699308+00:00, sha 95811fe4c2b6)
  - site_page: https://code-graph-rag.com/enterprise (fetched 2026-08-29T09:01:23.701037+00:00, sha 78de420afd5a)
- Data as of 2026-08-30T08:39:29.467469+00:00.