If you use Claude Code (claude.ai/code) to explore, debug, or refactor large repositories, you've felt the pain: every new question forces the model to re-read hundreds of files from scratch. "Explain the RAG system" triggers a full walk-through of every directory - expensive, slow, and repetitive.
Andrej Karpathy highlighted this exact problem in his viral posts about LLM knowledge bases and the /raw folder workflow. He asked for a better way: something that ingests once, organizes intelligently, and lets you query persistently without re-paying the token tax.
Graphify (by safishamsi) is the open-source answer - and it's now one of the most powerful skills in the 2026 Claude Code ecosystem.
In this definitive Claude Code Graphify tutorial, you will learn exactly how to install Graphify in 2 minutes on any Claude Code environment, convert any folder (25+ programming languages plus PDFs, images, and videos) into a living knowledge graph, achieve 71.5x fewer tokens per query on real-world corpora, surface god nodes and surprising cross-domain connections and design rationales automatically, and make your Claude Code workflows dramatically cheaper, faster, and more insightful.
What is Graphify? The Karpathy-Inspired Knowledge Graph Skill
Graphify is a multimodal AI coding assistant skill that transforms any directory into a persistent, queryable knowledge graph.
Core Technology: code parsing uses Tree-sitter AST plus call-graph extraction for 25+ languages (Python, JS/TS, Go, Rust, Java, C++, and more). A semantic layer lets your LLM (Claude 4 / Sonnet / Opus) extract concepts, relationships, and why decisions were made. The graph engine is NetworkX plus Leiden community detection, with no embeddings required. It is also multimodal, handling PDFs, screenshots, diagrams, whiteboard photos, and even video and audio with optional extras.
The magic: the first run costs tokens to build the graph, but every future query reads the tiny compressed graph instead of raw files, so token savings compound dramatically.
As the Graphify README puts it: graphify is the answer to that problem - 71.5x fewer tokens per query vs reading the raw files, persistent across sessions, honest about what it found vs guessed.
Why Use Graphify with Claude Code? The Tokenomics Advantage
The primary benefit is a 71.5x token reduction.
On a corpus of Karpathy repos plus 5 papers and 4 images (52 files), Graphify delivered a 71.5x reduction - 1.7k versus 123k tokens per query. On the graphify source plus the Transformer paper (4 files), it delivered a 5.4x reduction with a structural-clarity bonus. On the httpx Python library (6 files) the reduction was roughly 1x, but it was still valuable for surfacing god nodes.
For a typical 500-file monorepo or research project, you go from hundreds of thousands of tokens per query to low thousands - or even hundreds - after the initial build.
Complete Step-by-Step Claude Code Graphify Tutorial (2026)
Prerequisites: Python 3.10 or higher, Claude Code access (or a compatible tool such as Cursor, Aider, Gemini CLI, VS Code Copilot, or Google Antigravity), and a terminal with internet access for the initial install.
Step 1: one-line installation. Run pip install graphifyy && graphify install. Note that the PyPI package name is graphifyy (double y), while the CLI and skill command remain graphify.
Step 2: open Claude Code in your project. Navigate to the folder you want to graphify (or any subdirectory) and launch Claude Code.
Step 3: build your first knowledge graph. Type the single command /graphify . in the chat. Claude invokes the Graphify skill, Tree-sitter parses all code files instantly, the LLM performs semantic extraction on docs, images, and PDFs, and the graph is built, clustered, and exported.
Step 4: start querying immediately. After the build completes, ask anything - for example, /graphify query "How does authentication flow connect to the database layer and what design trade-offs were made?"
Conclusion: Graphify is Your Gateway to the Full 2026 Claude Code Power Stack
Graphify delivers exactly what Karpathy envisioned: a persistent, honest, insight-rich layer on top of your raw files that makes large-scale AI coding not just possible, but delightful and dramatically cheaper.