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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.

[!NOTE] Want this always-on? Graphify Cloud keeps the graph live across your entire SDLC: monorepo and cross-repo support, formal verification, and code review with a bird's-eye view of your SDLC, plus connectors for Sentry, Jira, and more so incidents and tickets sit in the same graph as your code. Start a free 14-day trial at app.graphify.com.

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

Get started (30 seconds). Needs Python 3.10+ and uv (or pipx):

uv tool install graphifyy      # install the CLI (or: pipx install graphifyy)
graphify install               # detects your assistant and registers the skill

Then, in your AI assistant:

/graphify .

That's it. You get three files:

graphify-out/
├── graph.html       open in any browser: click nodes, filter, search
├── GRAPH_REPORT.md  the highlights: key concepts, surprising connections, suggested questions
└── graph.json       the full graph: query it anytime without re-reading your files

The persisted graph includes graph.schema_version so integrations can detect incompatible format changes, plus graph.graphify_version identifying the Graphify release that produced it.

One install, any assistant, any OS. Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 20+ more, on macOS, Linux, and Windows: pick your platform.

See it in action

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

$ graphify explain "APIRouter"
Node: APIRouter
  Source:    routing.py L2210
  Community: 2
  Degree:    47

Connections (47):
  --> RequestValidationError [uses] [INFERRED]
  --> Dependant [uses] [INFERRED]
  --> .get() [method] [EXTRACTED]
  <-- __init__.py [imports] [EXTRACTED]
  ...

$ graphify path "FastAPI" "ModelField"
Shortest path (3 hops):
  FastAPI --uses--> DefaultPlaceholder <--references-- get_request_handler() --references--> ModelField

Every edge carries a confidence tag (EXTRACTED = explicit in the source, INFERRED = derived by resolution), so you can tell what was read directly from what was inferred. graphify query "<question>" returns a scoped subgraph for a plain-language question, and graphify path A B traces how any two things connect, ending with the path's overall confidence: that of its weakest hop.

What it does

What you get out of the box: