lead · github
Graphify turns your codebase into a searchable knowledge graph, no hallucinations
Parse repos, docs, schemas, and configs into deterministic graphs that AI agents can actually reason about. Works with Claude Code, Cursor, and Gemini CLI.
Graphify-Labs/graphify ↗When you hand a codebase to an AI agent, it usually gets chunked into vectors and fed into a neural network that guesses what matters. Graphify does something simpler: it reads your actual code structure, documentation, and database schemas, then builds a graph where every connection is explicit and traceable. No fuzzy similarity matching, no hallucinated relationships. An agent querying that graph gets exact answers about what calls what, where data flows, and why. For teams onboarding AI into existing projects, this means faster context window usage and fewer nonsense suggestions. For solo builders, it's the difference between an agent that confidently breaks your app and one that understands it.
Share kit
Graphify: deterministic knowledge graphs for your codebase
Turn repos, docs, and schemas into queryable graphs without vector hallucination. Graphify uses AST parsing to build explicit, traceable connections between code artifacts, then plugs into Claude Code, Cursor, and Gemini CLI as a skill. Real use case: faster onboarding for AI agents, cleaner context windows, fewer confident-wrong suggestions. Check the repo if your workflow involves handing codebases to LLM tools.
most AI code tools chunk your repo into vectors and hope the model guesses right. graphify builds an actual knowledge graph instead. every edge is explicit. no hallucination. works as a skill in claude code, cursor, gemini cli. deterministic parsing means your agent knows what it doesn't know.
Knowledge graphs, not vectors. Graphify parses your codebase, docs, SQL schemas, and configs into an explicit, queryable graph structure. Every relationship is traceable. No similarity matching, no hallucinated connections. Built for teams integrating AI agents into existing projects. AST-driven, deterministic, and it slots into Claude Code, Cursor, and Gemini CLI. Faster context, fewer confidence errors.
Just shipped a workflow that turns messy codebases into queryable knowledge graphs. No vector stores, no hallucinations, just deterministic AST parsing that actually explains every edge. The friction: when you onboard an AI agent into a codebase, context gets fuzzy fast. Vector RAG pulls irrelevant chunks. Graph queries are precise. Graphify takes your repo, docs, SQL schemas, configs, PDFs and builds a local knowledge graph you can ask questions against. Works as a skill in Claude Code, Cursor, Codex, Gemini CLI. Why it matters: faster knowledge transfer for teams, cleaner agent reasoning, zero hallucination tax from embeddings. GitHub: https://github.com/Graphify-Labs/graphify
built something for the agentic era: turn your entire codebase + docs into a queryable knowledge graph. no vectors, no hallucinations. just deterministic parsing and precise retrieval. works in Claude Code, Cursor, Codex. solves the real problem: agents need context that doesn't lie. https://github.com/Graphify-Labs/graphify