graphify

graphify is a RAG toolkit - Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

Overview

Early access to the graphify platform is open before the public v1 launch: app.graphify.com

Type '/graphify' in your AI coding assistant and it maps your entire project (code, docs, PDFs, images, videos) into a knowledge graph you can query instead of grepping through files.

• Code maps for free, fully local. Code is parsed with tree-sitter AST: deterministic, no LLM, nothing leaves your machine. (Docs, PDFs, images and video use your assistant's model, or a configured API key, for a semantic pass.) - Every edge is explained. Each connection is tagged 'EXTRACTED' (explicit in the source) or 'INFERRED' (resolved by graphify), so you can tell what was read directly from what was inferred. - Not a vector index. No embeddings, no vector store: a real graph you traverse. Ask a question, trace the path between two things, or explain one concept.

The FastAPI codebase mapped by graphify. Every node is a concept, colors are detected communities, and the whole thing is clickable in graph.html.

Works in Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 15+ more — pick your platform.

Once the graph is built you query it instead of reading files. Real output, graphify run on the FastAPI codebase shown above:

graphify is open-source, written primarily in Python, with 121,898 GitHub stars under the Apache 2.0 license. The latest release is v0.9.70 (2026-09-27).

Key capabilities

From the project's documentation:

• God nodes — the most-connected concepts in your project. Everything flows through these.
• Suggested questions — 4–5 questions the graph is uniquely positioned to answer.
• Video / audio — transcribed locally with faster-whisper. Nothing leaves your machine.
• No telemetry, no usage tracking, no analytics.
• pipx (pipx install graphifyy): run pipx ensurepath, then open a new terminal.
• docs.graphify.com — full documentation: guides, command reference, and integrations

Install

A quick way to get started (always check the official docs for the latest):

pip install graphifyy # may need PATH setup — see note below

How it fits a local-AI stack

graphify runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related RAG toolkits in the directory:

• AnythingLLM
• Haystack
• Khoj
• "langroid"

Sources

• Source code & docs: Graphify-Labs/graphify
• Official website: https://www.graphify.com

Stats from GitHub, 2026-09-28.