lead · github
Graphify turns your codebase into a queryable knowledge graph, no embeddings required
Parse docs, schemas, configs, and PDFs into deterministic graphs that AI agents can reason over without hallucination risk. Local, AST-based, and built for Claude Code, Cursor, Codex, and Gemini.
Graphify-Labs/graphify ↗Here's what Graphify does: you feed it your codebase, documentation, database schemas, config files, and PDFs. It parses everything locally using abstract syntax trees (the same thing compilers use to understand code) and builds a structured graph that shows how everything connects. Then you can ask an AI agent questions like "what tables does the user service touch?" or "where is the auth logic?" and get precise answers instead of hallucinated guesses. No vector store, no embeddings, no probabilistic nonsense. Every edge in the graph explains exactly why it exists. For builders, this means your AI tools have reliable, deterministic context about your project without weeks of manual indexing work.
Share kit
Graphify: deterministic knowledge graphs for your codebase
Turn docs, schemas, configs, and PDFs into queryable graphs that AI agents can reason over without hallucination risk. Local AST parsing, every connection explained. Works with Claude Code, Cursor, Codex, and Gemini CLI.
Most AI agents query your codebase via embeddings and hope for the best. Graphify parses it into a deterministic knowledge graph instead. Docs, schemas, configs, PDFs—all connected, all explainable, all local. No vector store required. Shipping with Claude Code, Cursor, Codex, and Gemini.
Tired of AI agents hallucinating about your codebase? Graphify builds deterministic knowledge graphs from your docs, schemas, configs, and PDFs—then lets Claude, Cursor, Codex, and Gemini query them with full precision. Local AST parsing. No embeddings. Every edge explained. Ship context your agents can actually trust.
Just integrated Graphify into a client's codebase. Turned 40 SQL schemas, 200+ doc files, and scattered configs into a queryable knowledge graph in under 5 minutes. No vector store hallucinations. No embedding drift. Every edge traced back to the AST. The win: their agent can now actually reason about database relationships and business logic without me hand-indexing a wiki. Deterministic parsing means you get the same graph every run. If you're building with Claude Code or Cursor and drowning in unstructured context, this shifts the game. Local, fast, explainable. https://github.com/Graphify-Labs/graphify
graphify does something different: it parses your entire codebase, docs, schemas, and PDFs into a queryable graph. AST-based, deterministic, runs locally. No embeddings. No hallucinations. Your AI agents get the actual relationship map of your code, not a fuzzy approximation. https://github.com/Graphify-Labs/graphify