Code-Graph-RAG

Name: Code-Graph-RAG
Author: vitali87

作者：vitali87版本 0.5.0i★ –i在你的电脑上运行

注册名称io.github.vitali87/code-graph-rag

安装

概览

作者提供的说明（英文原文）

Knowledge-graph RAG for multi-language codebases: index, query and edit code in natural language.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：ORCHESTRATOR_API_KEY, CYPHER_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： ORCHESTRATOR_PROVIDER, ORCHESTRATOR_MODEL, ORCHESTRATOR_API_KEY, CYPHER_PROVIDER, CYPHER_MODEL, CYPHER_API_KEY, MEMGRAPH_HOST, MEMGRAPH_PORT, TARGET_REPO_PATH

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "code-graph-rag": {
      "command": "uvx",
      "args": [
        "code-graph-rag",
        "mcp-server"
      ],
      "env": {
        "ORCHESTRATOR_PROVIDER": "your-value",
        "ORCHESTRATOR_MODEL": "your-value",
        "ORCHESTRATOR_API_KEY": "your-value",
        "CYPHER_PROVIDER": "your-value",
        "CYPHER_MODEL": "your-value",
        "CYPHER_API_KEY": "your-value",
        "MEMGRAPH_HOST": "your-value",
        "MEMGRAPH_PORT": "your-value",
        "TARGET_REPO_PATH": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add code-graph-rag --env ORCHESTRATOR_PROVIDER=your-value --env ORCHESTRATOR_MODEL=your-value --env ORCHESTRATOR_API_KEY=your-value --env CYPHER_PROVIDER=your-value --env CYPHER_MODEL=your-value --env CYPHER_API_KEY=your-value --env MEMGRAPH_HOST=your-value --env MEMGRAPH_PORT=your-value --env TARGET_REPO_PATH=your-value -- uvx code-graph-rag mcp-server

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "code-graph-rag": {
      "command": "uvx",
      "args": [
        "code-graph-rag",
        "mcp-server"
      ],
      "env": {
        "ORCHESTRATOR_PROVIDER": "your-value",
        "ORCHESTRATOR_MODEL": "your-value",
        "ORCHESTRATOR_API_KEY": "your-value",
        "CYPHER_PROVIDER": "your-value",
        "CYPHER_MODEL": "your-value",
        "CYPHER_API_KEY": "your-value",
        "MEMGRAPH_HOST": "your-value",
        "MEMGRAPH_PORT": "your-value",
        "TARGET_REPO_PATH": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "code-graph-rag": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "code-graph-rag",
        "mcp-server"
      ],
      "env": {
        "ORCHESTRATOR_PROVIDER": "your-value",
        "ORCHESTRATOR_MODEL": "your-value",
        "ORCHESTRATOR_API_KEY": "your-value",
        "CYPHER_PROVIDER": "your-value",
        "CYPHER_MODEL": "your-value",
        "CYPHER_API_KEY": "your-value",
        "MEMGRAPH_HOST": "your-value",
        "MEMGRAPH_PORT": "your-value",
        "TARGET_REPO_PATH": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

ORCHESTRATOR_PROVIDER env
LLM provider for the orchestrator agent (openai, anthropic, google, azure, cohere, ollama)
ORCHESTRATOR_MODEL env
Model name for the orchestrator agent
ORCHESTRATOR_API_KEY 机密 env
API key for the orchestrator LLM provider (not needed for the default local ollama provider)
CYPHER_PROVIDER env
LLM provider for Cypher query generation (openai, anthropic, google, azure, cohere, ollama)
CYPHER_MODEL env
Model name for Cypher query generation
CYPHER_API_KEY 机密 env
API key for the Cypher LLM provider (not needed for the default local ollama provider)
MEMGRAPH_HOST env
Hostname of the Memgraph database
MEMGRAPH_PORT env
Port of the Memgraph database
TARGET_REPO_PATH env
Path to the repository to analyze (auto-detected from working directory if not set)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 vitali87 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7234 个服务器的 0%
发布到注册中心的时间比此分类中其他 7234 个服务器的 91% 更新
基础检查结果等于或优于此分类中其他 7234 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Code-Graph-RAG 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：ORCHESTRATOR_API_KEY, CYPHER_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.5.0）于3天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT3天前更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT44天前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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 an embedded graph database that needs no server, 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.

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

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

Latest News 🔥

Rust Upgrade: Updated Rust to version 1.99.1-slim-trixie.
Signature Functionality: Added support for reading and writing JavaScript headers for change signatures, including the ability to change the signature of JavaScript functions or methods.
macOS Compatibility: The build now leaves macOS-14 support.

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:

Multi-language parser. A Tree-sitter based parser reads the codebase and ingests functions, classes, methods, modules, and their relationships into the graph 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.
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 -> Knowledge Graph

| User Query -> AI Model (Cypher Gen) -> Cypher Query -> Graph Results -> Response

The graph is stored in an embedded SQLite file by default. Memgraph and Neo4j are opt-in backends that run the same Cypher; Graph Backends compares them.

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

Supported Languages

Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, Dart, and Julia are fully supported. Scala is in development, and Ruby, Kotlin, Swift, Elixir, Haskell, Solidity, Bash, and Nix have structural support (modules, functions, classes where the language has them, and imports) through the pluggable ast-grep tier. See the Language Support matrix for per-language capabilities.

Installation

cgr is published to PyPI. Install it system-wide with the treesitter-full (tree-sitter grammars), ast-grep (Ruby, Kotlin, Swift, Elixir, Haskell, Solidity, Bash, Nix) and semantic (vector search) extras:

# with uv (recommended)

uv tool install "code-graph-rag[treesitter-full,ast-grep,semantic]"

or with pipx

pipx install "code-graph-rag[treesitter-full,ast-grep,semantic]"

Which version am I getting?

Three version lines exist and they intentionally differ:

where	what it tracks
git tags	every version, one per merge
GitHub Releases (binaries, signatures)	every 50th version, plus any security fix, breaking change or merge labelled `release now`
PyPI	every 50th version, plus any security fix, breaking change or merge labelled `release now`

So the newest tag on main usually runs ahead of the newest release, often by tens of patch versions; they coincide only just after a release. Nothing is stuck, the cadences differ by design. A security fix, a breaking change or a merge a maintainer labels release now does NOT wait for the cadence: each ships a release and a PyPI upload immediately.

uv tool install and pipx install give you the newest PyPI version, which is the newest RELEASE, not the newest tag. Interim tags exist so every merge is addressable; binaries and PyPI uploads follow the cadence above.

To run code newer than the latest release, install from git:

uv tool install "code-graph-rag[treesitter-full,ast-grep,semantic] @ git+https://github.com/vitali87/code-graph-rag@main"

To upgrade an existing install, run uv tool upgrade code-graph-rag (with pipx, pipx upgrade code-graph-rag, or pipx reinstall code-graph-rag for a git install). Upgrade covers the other install methods.

You also need Python 3.12+ and ripgrep. The graph lives in an embedded SQLite file by default, so there is no database server to run: no Docker and no cmake. Memgraph and Neo4j remain available as opt-in backends (Graph Backends). Full prerequisites, source installs, and environment setup are in the Installation guide.

[!NOTE]

The wheel is pure Python (py3-none-any), so it installs on any platform with Python 3.12 or newer. Older system interpreters (Debian Bookworm ships 3.11) need the interpreter pinned explicitly; see Installation for the commands.

Quick Start

# Parse a repository into the graph, then query it

cgr start --repo-path /path/to/repo --update-graph cgr start --repo-path /path/to/repo

The graph is written to ~/.cgr/graph.sqlite (set SQLITE_GRAPH_PATH to move it). To use Memgraph instead, install the driver with the memgraph extra, set GRAPH_BACKEND=memgraph, and start the packaged Memgraph + Qdrant stack with cgr daemon up.

Repeat the first command for each repository you want indexed; the graph is shared, and syncing one project leaves the others alone. To start over from an empty graph, add --clean — it deletes every project in the shared graph, not just this one, and asks for confirmation first when other projects would be destroyed.

A default index leaves out the opt-in capture groups; add them with --capture or CGR_CAPTURE.

The Quick Start guide walks through parsing, querying, and exporting in five minutes.

MCP Server

Code-Graph-RAG runs as an MCP server so Claude Code and other MCP clients can query and edit your codebase directly. See the MCP Server guide for setup.

Documentation

Getting Started

Installation
Quick Start
Configuration

User Guide

CLI Reference
Interactive Querying
Code Optimisation
Dead Code Detection
Dynamic Call Tracing
Graph Export
Real-Time Updates
C/C++ Semantic Mode
MCP Server
Porting Between Languages

Architecture

Overview
Graph Schema
Graph Backends
Language Support
Data-Flow Edges

Python SDK

Overview
Graph Loader
Cypher Generator
Semantic Search

Advanced

Adding Languages
Ignore Patterns
Building Binaries
Troubleshooting

Enterprise Services

Code-Graph-RAG is open source and free to use. For organisations that need more, we offer fully managed cloud-hosted solutions and on-premise deployments:

只显示了开头部分；其余内容请在仓库中阅读

Code-Graph-RAG

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Code-Graph-RAG

Latest News 🔥

What It Does

How It Works

Supported Languages

Installation

or with pipx

Which version am I getting?

Quick Start

MCP Server

Documentation

Enterprise Services