code-graph-rag

Name: code-graph-rag
Author: vitali87

Warning

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

2,311stars

385forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/vitali87/code-graph-rag

Getting Started

Guides for using mcp servers skills like code-graph-rag.

Best MCP Servers in 2026
Category-by-category picks: databases, dev tools, productivity, browser automation.
What is an AI Skills Marketplace?
Definitions, how marketplaces work, and how to choose between them in 2026.
Getting Started with AI Skills
First-time install walkthrough for Claude Code, Codex CLI, and ChatGPT.

Security ReportWarning

Last scanned: 4/24/2026

{
  "issues": [
    {
      "type": "clone-failed",
      "message": "Could not clone repository",
      "severity": "medium"
    }
  ],
  "status": "WARNING",
  "scannedAt": "2026-04-24T06:08:36.063Z",
  "semgrepRan": false,
  "npmAuditRan": false,
  "pipAuditRan": false
}

README.md

Frequently Asked Questions

What is code-graph-rag?

code-graph-rag is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by vitali87. The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs. It has 2,311 GitHub stars.

Is code-graph-rag safe to use?

code-graph-rag returned warnings in SkillsLLM's automated security scan. It has no critical vulnerabilities, but review the flagged issues in the Security Report section before adding it to your workflow.

How do I install code-graph-rag?

Clone the repository with "git clone https://github.com/vitali87/code-graph-rag" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is code-graph-rag written in?

code-graph-rag is primarily written in Python. It is open-source under vitali87 on GitHub, so you can review or fork the full source.

Are there alternatives to code-graph-rag?

Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh code-graph-rag against similar tools.

MCP for Beginners

Build MCP servers that give AI assistants real capabilities

36 minBeginner

Comments (0)

to leave a comment.

No comments yet. Be the first to share your thoughts!

Related Skills

superpowers

by obra

An agentic skills framework & software development methodology that works.

234,966

Popular in MCP Servers

Top skills in this category by stars

Scrapling

by D4Vinci

🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!

69,318

UltraRAG mcphub

Code-Graph-RAG: A Graph-Based RAG System for Any Codebases

An accurate Retrieval-Augmented Generation (RAG) system that analyzes multi-language codebases using Tree-sitter, builds comprehensive knowledge graphs, and enables natural language querying of codebase structure and relationships as well as editing capabilities.

Latest News 🔥

PHP Language Support: Full PHP language support added — classes, interfaces, traits, enums, namespaces, PHP 8 attributes, and call graph analysis. Contributed by @rs-ipps.
C Language Support: Full C language support added — functions, structs, unions, enums, preprocessor includes, and call graph analysis. Contributed by @dj0nes.
Visualise any GitHub repo instantly! Just change github.com to gitcgr.com in any repo URL — that's it, only 3 letters! Get an interactive graph of the entire codebase structure. Try it now: gitcgr.com

🚀 Features

Multi-Language Support:

Language	Status	Extensions	Functions	Classes/Structs	Modules	Package Detection	Additional Features
C	Fully Supported	.c	✓	✓	✓	✓	Functions, structs, unions, enums, preprocessor includes
C#	Fully Supported	.cs	✓	✓	✓	-	Namespaces (block and file-scoped), classes/structs/records/interfaces/enums, generics, inheritance/interfaces/overrides, typed call resolution with overloads, using directives
C++	Fully Supported	.cpp, .h, .hpp, .cc, .cxx, .hxx, .hh, .ixx, .cppm, .ccm	✓	✓	✓	✓	Constructors, destructors, operator overloading, templates, lambdas, C++20 modules, namespaces, preprocessor macros
Dart	Fully Supported	.dart	✓	✓	✓	-	Classes, mixins, extensions, enhanced enums, factory/named constructors, Flutter widgets, package/relative/dart: imports, part directives, pubspec dependencies
Go	Fully Supported	.go	✓	✓	✓	-	Receiver methods with cross-file binding, structs, interfaces, type declarations, function-local types
Java	Fully Supported	.java	✓	✓	✓	-	Generics, annotations, modern features (records/sealed classes), concurrency, reflection
JavaScript	Fully Supported	.js, .jsx	✓	✓	✓	-	ES6 modules, CommonJS, prototype methods, object methods, arrow functions
Lua	Fully Supported	.lua	✓	-	✓	-	Local/global functions, metatables, closures, coroutines
PHP	Fully Supported	.php	✓	✓	✓	-	Classes, interfaces, traits, enums, namespaces, PHP 8 attributes
Python	Fully Supported	.py	✓	✓	✓	✓	Type inference, decorators, nested functions
Rust	Fully Supported	.rs	✓	✓	✓	✓	impl blocks, associated functions, macro_rules! macros
TypeScript (TSX)	Fully Supported	.tsx	✓	✓	✓	-	All TypeScript features plus JSX elements and components
TypeScript	Fully Supported	.ts	✓	✓	✓	-	Interfaces, type aliases, enums, namespaces, ES6/CommonJS modules
Scala	In Development	.scala, .sc	✓	✓	✓	-	Case classes, objects

🌳 Tree-sitter Parsing: Uses Tree-sitter for robust, language-agnostic AST parsing
📊 Knowledge Graph Storage: Uses Memgraph to store codebase structure as an interconnected graph
🗣️ Natural Language Querying: Ask questions about your codebase in plain English
🤖 AI-Powered Cypher Generation: Supports both cloud models (Google Gemini), local models (Ollama), and OpenAI models for natural language to Cypher translation
🤖 OpenAI Integration: Leverage OpenAI models to enhance AI functionalities.
📝 Code Snippet Retrieval: Retrieves actual source code snippets for found functions/methods
✍️ Advanced File Editing: Surgical code replacement with AST-based function targeting, visual diff previews, and exact code block modifications
⚡️ Shell Command Execution: Can execute terminal commands for tasks like running tests or using CLI tools.
🚀 Interactive Code Optimization: AI-powered codebase optimization with language-specific best practices and interactive approval workflow
📚 Reference-Guided Optimization: Use your own coding standards and architectural documents to guide optimization suggestions
🧹 Dead Code Detection: Report functions and methods unreachable from any entry point by walking CALLS/REFERENCES edges from roots (with a CI-friendly --fail-on-found)
🔗 Dependency Analysis: Parses pyproject.toml to understand external dependencies
🎯 Nested Function Support: Handles complex nested functions and class hierarchies
🔄 Language-Agnostic Design: Unified graph schema across all supported languages

🏗️ Architecture

The system consists of two main components:

Multi-language Parser: Tree-sitter based parsing system that analyzes codebases and ingests data into Memgraph
RAG System (codebase_rag/): Interactive CLI for querying the stored knowledge graph

📋 Prerequisites

Python 3.12+
Docker & Docker Compose (for Memgraph)
cmake (required for building pymgclient dependency)
ripgrep (rg) (required for shell command text searching)
For cloud models: Google Gemini API key
For local models: Ollama installed and running
uv package manager

Installing cmake and ripgrep

On macOS:

brew install cmake ripgrep

On Linux (Ubuntu/Debian):

sudo apt-get update
sudo apt-get install cmake ripgrep

On Linux (CentOS/RHEL):

sudo yum install cmake
sudo dnf install ripgrep
# Note: ripgrep may need to be installed from EPEL or via cargo

🛠️ Installation

System-wide install (recommended for end users)

cgr is published to PyPI and can be installed system-wide so it works from any target repo without activating a project virtualenv. Install with the treesitter-full (all languages) and semantic (vector search) extras:

# with uv (recommended)
uv tool install "code-graph-rag[treesitter-full,semantic]"

# or with pipx
pipx install "code-graph-rag[treesitter-full,semantic]"

For a Python-only install, omit the extras. For local development from a clone, use uv tool install --editable "/path/to/code-graph-rag[treesitter-full,semantic]".

After install, cgr is on PATH. From any repository, run:

cd ~/path/to/some-target-repo
cgr daemon up        # one-time: start the shared memgraph + qdrant stack
cgr start            # auto-sync the current repo and drop into the agent

cgr start defaults --repo-path to the current directory and auto-syncs the graph incrementally on entry. Pass --no-sync to skip the sync, or --no-start-stack if memgraph/qdrant already run else