Introduction

nodesify-graphify turns any folder into a queryable knowledge graph — deterministic AST extraction in Rust, optional local-embedding semantics, zero API keys, everything on your machine.

You drop into an unfamiliar repo and need to know: what is load-bearing here, what breaks if I change this, where does auth live, how do these two modules connect. Reading everything costs the whole context window. The graph answers in ~3,000 tokens — measured at 73–79× fewer tokens per query on real repos (printed honestly after every run, computed from real file sizes vs actual query output).

Three things a folder full of files can't give you

1. Structure that survives the session — hub files, god nodes, communities, and the blast radius of any change, stored in SQLite and refreshed incrementally as code changes.
2. An honest audit trail — every edge is labeled EXTRACTED / INFERRED / AMBIGUOUS with a numeric confidence score. You always know what was found in the source versus deduced, and --detail high filters to only declared facts.
3. Answers for agents and humans — query it from the CLI, from any AI agent via MCP, or just read the exported markdown wiki with plain file links.

Where to go next

• Getting started — install and run your first graph
• CLI reference — every command and flag
• Wiki and exports — markdown wiki, Obsidian vault, HTML viewer, Neo4j
• Semantic enrichment — local embeddings and LLM backends
• Architecture — how the pipeline works under the hood

Worked examples with honest reviews — the tool run on itself and on its Python ancestor, including what the graph got wrong — live in the worked/ directory of the repository.