CodeGraph: Open-Source Tool to Supercharge AI Coding Agents with Knowledge Graphs

What is CodeGraph?

CodeGraph is a specialized tool built for AI coding agents (like Claude Code, Codex, Gemini, Cursor, and OpenCode). It creates a structured knowledge graph of your entire codebase, including:

• Function definitions
• Variable symbols
• Call relationships
• Dependency chains

Instead of AI agents repeatedly scanning files (a slow and token-wasteful process), they query this pre-built knowledge graph. This drastically reduces tool calls, speeds up responses, and cuts costs.

Why CodeGraph is a Game-Changer

Let's look at the hard numbers from real-world tests:

• In a large codebase with 4,000 files, AI tool calls dropped from 52 to just 3 — a 17x efficiency boost.
• Across 7 industrial-grade projects, CodeGraph delivered:
  • 35% cost reduction on average.
  • 57% less token usage.
  • 46% faster response times.
  • 71% fewer tool calls.

For example, when testing with the VS Code codebase (TypeScript, ~10k files), CodeGraph reduced tool calls by 85%, cut tokens by 78%, and made responses 52% faster.

How to Use CodeGraph (Practical Setup)

Using CodeGraph is straightforward, especially since it natively supports popular AI coding tools like Claude Code, Cursor, and OpenCode. Here's how to get started:

Step 1: Install and Initialize

First, clone the repository and install dependencies:

git clone https://github.com/colbymchenry/codegraph.git
cd codegraph
npm install  # or yarn install

Step 2: Index Your Codebase

Run the indexing command to build the knowledge graph for your project. Replace ./your-project with your codebase path:

npx codegraph index ./your-project

Step 3: Integrate with Your AI Coding Agent

CodeGraph works out-of-the-box with tools like Claude Code and Cursor. For example, in Cursor, simply enable the CodeGraph plugin, and the AI will automatically query the knowledge graph instead of scanning files.

For custom integrations, use the provided API to fetch data from the knowledge graph. Here's a sample snippet to retrieve function call relationships:

import requests

def get_call_relationships(function_name):
    response = requests.get(
        "http://localhost:3000/codegraph/call-relationships",
        params={"function": function_name}
    )
    return response.json()

# Example usage
relationships = get_call_relationships("user_authenticate")
print(relationships)

Security and Compatibility

One major advantage of CodeGraph is its 100% local operation. Your code data never leaves your machine, so you don't have to worry about leaks. It also supports multiple programming languages and frameworks, making it versatile for projects like:

• Web apps (React, Vue, Angular)
• Backend services (Node.js, Python Django/Flask)
• Mobile apps (iOS, React Native)

Final Thoughts

CodeGraph is a must-have for any team using AI coding agents. It turns slow, inefficient code exploration into a fast, cost-effective process. With its open-source nature, strong community support (28.5k GitHub stars), and tangible performance gains, it's a tool that truly makes AI coding agents "self-evolve."

To get started, head to CodeGraph's GitHub repo and try it on your next project — your AI coding assistant will thank you.