README

Read this in other languages

English | 简体中文 | 日本語 | 한국어 | Deutsch | Français | Español | हिन्दी | Português | Русский | العربية | فارسی | Italiano | Polski | Nederlands | Türkçe | Українська | Tiếng Việt | Bahasa Indonesia | Svenska | Ελληνικά | Română | Čeština | Suomi | Dansk | Norsk | Magyar | ภาษาไทย | Oʻzbekcha | 繁體中文 | Filipino | עברית





  
  
  
  
  
  
  
  
  
  



  Cut agent token spend by 80%. Graphify Cloud is always-on memory that governs your agents, keeps code quality high, and proves correctness with formal verification. Start a free 14-day trial →


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):

bash
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:

text
$ graphify explain "APIRouter"
Node: APIRouter
  Source:    routing.py L2210

Top issues

Discussions

Latest release

graphify 0.9.104

- Security: skill the /graphify runbook no longer substitutes the scan path into Python code. Every generated code block (detect, AST and semantic extraction, build, diagnose, label, manifest, and --update) now reads the resolved scan root from the trusted graphify-out/.graphify_root sidecar that Step 1 writes through a quoted heredoc, instead of pasting the agent-supplied path into a string literal. A directory whose name carried a quote or $(...) could previously break out of Path('INPUT_PATH') and run as code; it is now read as inert data. Covers the assembled hosts and th