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Code-Graph-RAG is a Python-based RAG system that parses multi-language codebases using Tree-sitter, builds knowledge graphs in Memgraph, and enables natural language queries and AI-powered code editing. It supports 10+ languages including Python, TypeScript, Java, Rust, and recently added PHP and C.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | vitali87/code-graph-rag |
| Owner | vitali87 |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 2.3k |
| Forks | 383 |
| Open issues | 34 |
| Latest release | v0.0.246 (2026-07-07) |
| Last updated | 2026-07-07 |
| Source | https://github.com/vitali87/code-graph-rag |
What code-graph-rag is
The system combines Tree-sitter AST parsing for language-agnostic code analysis with Memgraph graph storage for codebase structure representation. It integrates LLM backends (Google Gemini, OpenAI, Ollama) to translate natural language to Cypher queries and provides AST-based code editing, dependency analysis, and call graph generation across supported languages.
Get the code-graph-rag source
Clone the repository and explore it locally.
git clone https://github.com/vitali87/code-graph-rag.gitcd code-graph-rag# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Docker & Docker Compose for Memgraph and Qdrant services; cmake and ripgrep system dependencies must be pre-installed on all deployment machines.
- Python 3.12+ required; installation via PyPI (uv tool install or pipx) recommended; treesitter-full and semantic extras needed for multi-language and vector search support.
- LLM backend selection (Gemini, OpenAI, Ollama) must be configured at runtime; each choice has different API key, cost, and latency implications.
- Knowledge graph indexing speed is not documented; large monorepos (>100k files) may have unknown performance characteristics.
- File editing operates via AST-based surgical replacement; extensive testing on target codebase recommended before automation in CI/CD.
When to avoid it — and what to weigh
- Requirement for static security scanning or SAST — Tool focuses on code structure and RAG; does not provide vulnerability scanning, CVE detection, or compliance auditing capabilities.
- Need for production-grade uptime guarantees — Project is actively developed (v0.0.246) with frequent updates; stability and backward compatibility guarantees are not documented.
- Strict offline-only or air-gapped environments — Cloud model integrations (Google Gemini, OpenAI) are primary; Ollama local fallback exists but requires explicit setup.
- Enterprise support and SLA requirements — Enterprise support is mentioned on website but terms, SLA, response times, and commercial licensing details are not provided in repository data.
License & commercial use
MIT License. Permits unrestricted commercial use, modification, and redistribution with attribution and no warranty.
MIT is a permissive OSI license allowing commercial deployment without licensing fees or usage restrictions. Enterprise support and services are advertised on code-graph-rag.com but terms, pricing, and support scope are not specified in repository data; requires direct inquiry.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
LLM API keys must be managed securely (environment variables, secrets vaults); no encryption for stored graphs or logs is documented. Memgraph and Qdrant require network isolation in shared environments. Code-Graph-RAG parses and stores code structure in graph—ensure access controls match code repository permissions. Input validation for natural language queries and file editing operations is not described; AST-based surgery reduces injection risk but testing is advised.
Alternatives to consider
LangChain / LlamaIndex with custom retrievers
More flexible, language-agnostic; requires more manual integration; no built-in graph database or multi-language parsing.
GitHub Copilot for Business / Codeium
Cloud-first, real-time IDE integration, vendor-managed; less control over indexing, cannot self-host, higher per-seat cost.
Tabnine Enterprise with custom connectors
Specialized for code completion; weaker on structural analysis and monorepo navigation; proprietary model.
Build on code-graph-rag with DEV.co software developers
Start with Code-Graph-RAG: install via PyPI, spin up Memgraph in Docker, and query your repo in minutes. For enterprise support, visit code-graph-rag.com.
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code-graph-rag FAQ
Does Code-Graph-RAG require internet connectivity?
What is the scalability limit for codebase size?
Can I use Code-Graph-RAG in a CI/CD pipeline?
How does it handle private code repositories?
Senior engineers for your next build
Our engineers ship rag frameworks software for a living. DEV.co provides software development services, web development services, and ongoing support for teams standardizing on tools like code-graph-rag.
Ready to streamline codebase intelligence?
Start with Code-Graph-RAG: install via PyPI, spin up Memgraph in Docker, and query your repo in minutes. For enterprise support, visit code-graph-rag.com.