Skip to main content
Graphify Local turns a folder of code, docs, papers, images, or videos into a knowledge graph you can query from a terminal, a coding agent, or an MCP client. It runs on your machine and is published on PyPI as graphifyy under the Apache-2.0 license. Coding agents spend most of their context re-reading files to rediscover how a codebase fits together. Graphify Local parses the code once, records every function, class, import, and call as a node or an edge, and lets the agent ask the graph instead. On the Graphify repository itself, graphify benchmark reports about 127 times fewer tokens per question than reading the corpus.
Use Graphify Local when:
  • Your code can’t leave your machine, or you want to run everything offline.
  • You want the graph as files (graph.json, graph.html, GRAPH_REPORT.md) that you can commit, diff, and export.
  • You want to script or extend the pipeline yourself.
If you’d rather we build, host, and refresh graphs for your GitHub repositories, use Graphify.

​
How it works

1

Detect

graphify/detect.py walks the folder, honours .gitignore and .graphifyignore, and sorts files into code, documents, papers, images, and video.
2

Extract

Code is parsed locally with tree-sitter grammars, so the structural graph is deterministic and needs no API key. Documents and media can optionally go through an LLM for semantic extraction.
3

Resolve

Calls, imports, and inheritance are bound across files by per-language resolvers. Edges that can’t be bound to exactly one definition are marked AMBIGUOUS instead of guessed.
4

Cluster and report

Leiden community detection groups related nodes. Graphify then writes the graph, an interactive HTML view, and a Markdown report of hubs, communities, and suggested questions.
Every edge carries a confidence label: EXTRACTED (read from the source), INFERRED (derived by a resolver or an LLM), or AMBIGUOUS (several possible targets). See How Graphify works for the full pipeline.

​
What you get

​
Three ways to use it

​
Graphify Local and Graphify

Both build the same kind of graph. The difference is who runs it.
The PyPI package is graphifyy, with two y’s. The command it installs is graphify, plus graphify-mcp for the MCP server.
No, not for code. Code extraction, resolution, clustering, and queries all run locally. An LLM is only used for documents, papers, images, video transcripts, and community names. Add --code-only to skip it entirely.
No. Queries traverse the graph (breadth-first or depth-first) from nodes that match your question, then trim the result to a token budget.
Python 3.10 or later.

​
What’s next

Quickstart

Install the skill and map a repository from your agent.

MCP server quickstart

Serve the graph to any MCP client over stdio or HTTP.

Try it in Graphify

We build and host the graph for you. Start with a 14-day Pro trial, then keep up to 3 repositories on Free. No card required.