Graphify: The Knowledge Graph That Makes AI Coding Agents 10x Smarter

By Deepak Bagada, CEO at SaaSNext. I deployed Graphify across 3 production codebases (a Python monolith, a TypeScript microservices app, and a polyglot data platform) in July 2026 and measured its impact on token consumption, query accuracy, and developer onboarding speed.

Graphify hit 80K GitHub stars in under 4 months and earned a place in Y Combinator's Summer 2026 batch. The pitch is simple: type /graphify . in your AI coding assistant and it turns your entire project into a queryable knowledge graph. No more grepping through files. No more re-reading the same modules. No more 3-week onboarding ramps. This guide covers what Graphify does, how it works under the hood, how to use it across 20+ coding assistants, and the real-world impact I measured on token consumption and onboarding speed.

What Is Graphify Graphify is an open-source (MIT License) knowledge graph skill for AI coding assistants. When you type /graphify . in any supported assistant, it analyzes your entire project — all code files, documentation, PDFs, images, and even video transcripts — and builds a structured knowledge graph of how everything relates to everything else. The command produces three files: graph.html (interactive browser visualization you can click, filter, and search), GRAPH_REPORT.md (key concepts, surprising connections, and suggested questions), and graph.json (the full graph, queryable anytime without re-reading files). Graphify registers as a skill in Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Devin CLI, Kiro, and 15+ other assistants. All code parsing is done on-device using tree-sitter with zero API calls.

How Graphify Works Under the Hood Graphify uses tree-sitter to parse code into ASTs (abstract syntax trees) across 33 languages. It extracts functions, classes, imports, call graphs, database schemas, and architecture relationships. Every entity becomes a node; every relationship becomes an edge. NetworkX builds the graph structure. Leiden clustering then detects hidden communities of related concepts — the functions and modules that change together, the database tables accessed by the same API handlers, the shared utilities that have more dependents than anyone realized. The entire process runs on-device. No data ever leaves your machine. For a 100K-file monorepo, initial graph construction takes approximately 5-8 minutes. Incremental updates via --watch complete in under 2 seconds.

The Problem Graphify Solves AI coding assistants are powerful, but they operate on flat-file context. They read files — sometimes many at once — but they rebuild understanding from scratch on every query. If you ask how does the login flow connect to the users table, the assistant greps through files, reconstructing call chains and data flow each time. Graphify pre-computes that map. The first time you run /graphify ., the graph captures every relationship. Every subsequent query skips the grep and reads the graph directly. In my testing on a 50K-file TypeScript codebase, Graphify reduced token consumption per query by 52% compared to ungrepified Claude Code sessions. For a developer making 50 queries per day, that is approximately 13,000 tokens saved daily. At GPT-5.5 pricing of $15/M output tokens, that is $0.20 saved per day per developer — or $2,400/year for a 50-person team.

Graphify for Developer Onboarding The most transformative use case I observed was developer onboarding. A new engineer joining a 500K-line Python monolith ran /graphify . on day one and received a complete codebase map with identified god modules, dependency clusters, and surprising connections in under 8 minutes. Before Graphify, that same onboarding took 3 weeks of reading docs, asking colleagues, and grepping through files. The graph.html visualization gave the engineer an immediate mental model of the codebase architecture. The GRAPH_REPORT.md surfaced the ten most-connected modules that any change touches. The graph.json enabled the engineer's Claude Code to answer queries about data flow and module dependencies from day one with senior-developer accuracy.

Supported Platforms Graphify works in 20+ AI coding assistants. The full supported list: Claude Code, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Amp, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, Devin CLI, and Google Antigravity. One developer builds the graph, commits graphify-out/ to the repo, and every teammate's assistant queries the same graph regardless of which platform they use.

Limitations Very large monorepos (500K+ files) may take 15+ minutes for initial graph construction. Use --watch for incremental updates after the first build. Image and video extraction requires vision model integration — basic text extraction from images works, but deep video understanding requires additional processing. Tree-sitter coverage of 33 languages covers most modern production stacks (Python, TypeScript, Go, Rust, Java, C/C++, Swift, Kotlin, Ruby, PHP, and more) but may miss niche or legacy languages.

FAQ Q: How much does Graphify cost? A: Graphify is free and open source (MIT License). All processing is on-device with zero API calls. Q: Does Graphify work offline? A: Yes. All parsing and graph construction happens locally. No internet connection required after the initial pip install. Q: Can I use Graphify with any AI coding assistant? A: Graphify works with 20+ assistants. The /graphify . command syntax is the same across all of them. Q: What happens when I change my code? A: Run /graphify . again or use --watch for automatic incremental updates. Graphify only re-indexes changed files. Q: How long does Graphify take to set up? A: pip install graphifyy then /graphify . in your assistant. Under 2 minutes for installation and first graph build on a medium-sized codebase.