Graphify

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):

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.

What it does

What you get out of the box:

Tip

Running this across many repos or a monorepo? Graphify Cloud does it continuously and cuts agent token spend by 80%, with cross-repo links, SDLC-wide code review, formal verification, and Sentry/Jira connectors. Start a free 14-day trial →

Benchmarks: 0.497 recall@10, 0 LLM credits

Every system ran on the same harness with the same model and budgets, scored by a judge blind-validated against a second judge (90.6% agreement, Cohen's kappa 0.81). Full per-system tables, the code-intelligence result, and reproduction commands: BENCHMARKS.md.

Documentation

Full guides and reference live at docs.graphify.com:

• Quickstart: build your first graph
• CLI reference: every command and flag
• Ask better graph questions: querying patterns
• Configuration: environment variables and tuning
• Supported inputs: languages and file types
• Team workflows and PR review
• Troubleshooting and How it works

Prerequisites

macOS quick install (Homebrew):

brew install python@3.12 uv

Windows quick install:

winget install astral-sh.uv

Ubuntu/Debian:

sudo apt install python3.12 python3-pip pipx
# or install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh

Install

Important

Official package: The PyPI package is graphifyy (double-y). Other graphify* packages on PyPI are not affiliated. The CLI command is still graphify.

The official source repository is Graphify-Labs/graphify.

The two-command quickstart is at the top of this README. This section covers the install methods, project-scoped installs, and every supported platform.

Install methods (pick one):

uv tool install graphifyy   # recommended, isolated env (if 'graphify' isn't found after, run: uv tool update-shell)
pipx install graphifyy      # alternative
pip install graphifyy       # may need PATH setup, see Troubleshooting below

Then run graphify install once. It detects which assistant you run and registers the skill, so there is no per-assistant setup. Open your AI assistant and type /graphify .

To install the assistant skill into the current repository instead of your user profile, add --project:

graphify install --project
graphify install --project --platform codex

Project-scoped installs write under the current directory, for example .claude/skills/graphify/SKILL.md or .agents/skills/graphify/SKILL.md (plus a references/ sidecar the skill loads on demand), and print a git add hint for files that can be committed. Per-platform commands that support project-scoped installs accept the same flag, for example graphify claude install --project or graphify codex install --project.

Tip

Hitting command not found, a pip-on-Mac/Windows issue, PowerShell quoting, uvx usage, git-hook PATH quirks, or strict mode? See Installation and Troubleshooting.