🏗️ CodeGraphContext (CGC)

Turn code repositories into a queryable graph for AI agents.

🌐 Languages:

• 🇬🇧 English
• 🇨🇳 中文
• 🇰🇷 한국어
• 🇺🇦 Українська
• 🇷🇺 Русский
• 🇯🇵 日本語
• 🇮🇳 தமிழ்
• 🇪🇸 Español (Soon)

🌍 Help translate CodeGraphContext to your language by raising an issue & PR on GitHub Issues!

Bridge the gap between deep code graphs and AI context.

A powerful MCP server and CLI toolkit that indexes local code into a graph database to provide context to AI assistants and developers. Use it as a standalone CLI for comprehensive code analysis or connect it to your favorite AI IDE via MCP for AI-powered code understanding.

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📍 Quick Navigation

• 🚀 Quick Start
• 📋 Prerequisites
• 🏃 How to Run the Project Locally
• 🌐 Supported Programming Languages
• 🛠️ CLI Toolkit
• 🤖 MCP Server
• 🗄️ Database Options
• 🔬 SCIP indexing (optional)

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✨ Experience CGC

👨🏻‍💻 Installation and CLI

Install in seconds with pip and unlock a powerful CLI for code graph analysis.

🛠️ Indexing in Seconds

The CLI intelligently parses your tree-sitter nodes to build the graph.

🤖 Powering your AI Assistant

Use natural language to query complex call-chains via MCP.

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Why CodeGraphContext?

CodeGraphContext is built for the moment when plain text search stops being enough. It turns a repository into a graph of files, symbols, calls, inheritance, imports, and relationships so you can move from "where is this defined?" to "how does this actually connect?" without jumping between tools.

flowchart LR
  A[Source code] --> B[Tree-sitter and SCIP indexers]
  B --> C[Graph database]
  C --> D[CLI queries]
  C --> E[MCP server]
  D --> F[Direct analysis]
  E --> G[AI assistants with repo context]

Comparison at a glance

Approach	Best for	Tradeoff
grep / file search	Exact string lookup	Misses relationships and code structure
RAG over code chunks	Natural-language retrieval	Can lose symbol-level precision
CodeGraphContext	Repository-wide reasoning	Requires an indexing step

Common use cases

• Trace call chains across files and packages.
• Understand class hierarchies, imports, and module boundaries.
• Find dead code, hotspots, and unexpected dependencies.
• Give an AI assistant the same structural context a maintainer would use.

FAQ

• Do I need a remote service? No. CGC works locally with embedded backends or with external graph databases when you want them.
• Is it only for one language? No. It supports a broad mix of mainstream languages and keeps expanding.
• Can I use it with an AI assistant? Yes. That is one of the main reasons the MCP server exists.

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Project Details

• Version: 0.5.1
• Authors: Shashank Shekhar Singh shashankshekharsingh1205@gmail.com
• License: MIT License (See LICENSE for details)
• Website: CodeGraphContext

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👨‍💻 Maintainer

CodeGraphContext is created and actively maintained by:

Shashank Shekhar Singh

• 📧 Email: shashankshekharsingh1205@gmail.com
• 🐙 GitHub: @Shashankss1205
• 🔗 LinkedIn: Shashank Shekhar Singh
• 🌐 Website: codegraphcontext.vercel.app

Contributions and feedback are always welcome! Feel free to reach out for questions, suggestions, or collaboration opportunities.

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Star History

Star History Chart ↗

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Features

• Code Indexing: Analyzes code and builds a knowledge graph of its components.
• Relationship Analysis: Query for callers, callees, class hierarchies, call chains and more.
• Pre-indexed Bundles: Load famous repositories instantly with .cgc bundles - no indexing required! (Learn more)
• Live File Watching: Watch directories for changes and automatically update the graph in real-time (cgc watch).
• Interactive Setup: A user-friendly command-line wizard for easy setup.
• Dual Mode: Works as a standalone CLI toolkit for developers and as an MCP server for AI agents.
• Multi-Language Support: Full support for 23 programming languages.
• Flexible Database Backend: FalkorDB Lite (Default), KuzuDB, LadybugDB, FalkorDB Remote, Nornic DB, or Neo4j (all platforms via Docker/native).

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🏗️ Architecture & Workflow

CodeGraphContext parses your source code and builds a comprehensive knowledge graph. This graph can be queried directly via the CLI toolkit or exposed to AI assistants through the MCP server.

flowchart TD
    A[Code Repository] -->|Parsed by| B[Tree-sitter / SCIP Indexing]
    B -->|Generates| C[Knowledge Graph]
    C -->|Stored in| D[(Graph Database)]
    D -->|Queried via| E[CLI Toolkit]
    D -->|Served by| F[MCP Server]
    F -->|Provides context to| G[🤖 AI Assistant]

    classDef default fill:#1f2937,stroke:#8b5cf6,stroke-width:2px,color:#fff;
    classDef db fill:#0f172a,stroke:#3b82f6,stroke-width:2px,color:#fff;
    classDef ai fill:#312e81,st