Graphify: Instant Knowledge Graph for Claude Code/Antigravity (FREE)
Claude Code / Antigravity + Graphify = Instant Knowledge Graph
https://github.com/safishamsi/graphify
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Claude Code rebuilds your entire project understanding from scratch every session — re-reading the same files, burning tokens, and starting blind every time you open a new chat. Graphify fixes this: it maps your project once into a knowledge graph, and Claude reads that graph automatically at the start of every session instead of re-reading your files.
In this tutorial, I break down exactly how Graphify works (three passes — zero tokens for code, one-time cost for docs), run a real 10-question test in two identical Claude Code sessions to measure the actual token savings, and walk through the full setup from scratch. I also break down what the "71.5x fewer tokens" benchmark actually measures — and when the savings are real for your project.
⏰ Timestamps:
0:00 Why Claude Code Reads Your Files Every Session
1:14 What Graphify Does for Claude Code
1:59 Graphify Architecture: 3 Passes Explained
3:37 Real Token Savings Test: 10 Questions
5:06 How to Install Graphify Step by Step
6:24 Graph Output and How to Update It
7:59 Real-World Impact + 71x Claim Debunked
9:36 Graphify for Research, Content, and Business Folders
🔑 Key Takeaways:
• Run pip install graphifyy — double y (the single-y package on PyPI is a completely different tool)
• Pass 1 (code parsing) runs entirely on your machine with zero token cost — only Pass 3 touches Claude's API, and only once
• Graph overhead means the first 2-3 questions cost slightly more; savings compound after the crossover point
• The 71.5x benchmark compares against pasting all files into context at once — a workflow almost nobody uses in real Claude Code sessions
❓ FAQ:
Q: Does Graphify actually save tokens in Claude Code?
A: Yes, but not 71x in a typical session. In a real 10-question Claude Code session on the browser-use project, Graphify used 113,000 tokens vs 120,000 without — about 7-8% savings. Savings compound with session length, project size, and mixed code+docs projects.
Q: How do I install Graphify for Claude Code?
A: Run pip install graphifyy (double y — the single-y package is a different tool), then graphify install to register the always-on hook, then /graphify inside your Claude Code session to build the graph. Full walkthrough at 5:06.
Q: What is a knowledge graph in the context of Graphify?
A: Graphify's knowledge graph is a map of nodes (functions, classes, documents, concepts) and edges (relationships like calls, imports, references), organized into neighborhoods. Claude reads a summary of this graph at the start of every session via a PreToolUse hook — replacing dozens of file reads with a single structured map.
Q: Does Graphify work on non-code projects?
A: Yes. Graphify works on any folder — research papers, meeting recordings, strategy documents, content vaults. Pass 1 (code parsing) is skipped on text-only projects, but Pass 3 still extracts concepts and builds a navigable graph from markdown, PDFs, and documents.
🔗 Resources & Links:
- [LINK: Graphify GitHub Repository — install instructions and documentation]
- [LINK: browser-use GitHub — the project used in this demo]
- [LINK: FuturMinds Claude Code Tutorials Playlist]
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Whether you're looking for a complete Graphify setup guide, trying to reduce Claude Code token usage on a growing project, or want to understand how knowledge graphs improve AI coding sessions, this walkthrough covers everything from install to real benchmark analysis.