graphify

Name: graphify
Author: Graphify-Labs

Verified

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

107,554stars

10,444forks

Python

Installation

# Add to your Claude Code skills
git clone https://github.com/Graphify-Labs/graphify

Getting Started

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Security ReportVerified

Last scanned: 7/4/2026

{
  "issues": [
    {
      "file": "README.md",
      "line": 57,
      "type": "remote-install",
      "message": "Install command (remote install script piped to a shell — review the source before running): \"curl -LsSf https://astral.sh/uv/install.sh \\| sh\"",
      "severity": "low"
    }
  ],
  "status": "PASSED",
  "scannedAt": "2026-07-04T06:46:20.704Z",
  "npmAuditRan": true,
  "pipAuditRan": true,
  "promptInjectionRan": true
}

README.md

Frequently Asked Questions

What is graphify?

graphify is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Graphify-Labs. Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. It has 107,554 GitHub stars.

Is graphify safe to use?

Yes. graphify passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install graphify?

Clone the repository with "git clone https://github.com/Graphify-Labs/graphify" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is graphify written in?

graphify is primarily written in Python. It is open-source under Graphify-Labs on GitHub, so you can review or fork the full source.

Are there alternatives to graphify?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh graphify against similar tools.

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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.

Want this always-on, updating in the background across your code, docs, and meetings rather than only on demand? That is what we are building at graphify.com, and early access is open now at app.graphify.com.

Get started (30 seconds):

uv tool install graphifyy      # install the CLI (or: pipx install graphifyy)
graphify install               # register the skill with your AI assistant

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

Works in Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 15+ more — 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:

Capability	What you get
God nodes	The most-connected concepts, so you see what everything flows through
Communities	The graph split into subsystems (Leiden), with LLM-free labels
Cross-file links	`calls` / `imports` / `inherits` / `mixes_in` resolved across ~40 languages via tree-sitter AST
Query, path, explain	Ask a question, trace the path between two things, or explain one concept, all against `graph.json`
Rationale + doc refs	`# NOTE:` / `# WHY:` comments and ADR/RFC citations become first-class nodes linked to the code
Beyond code	Docs, PDFs, images, and video/audio all map into the same graph
Local-first	Code is parsed locally with tree-sitter (no LLM, nothing leaves your machine); only the semantic pass over docs/media calls a backend, and only if you configure one

Benchmarks

Benchmark	Metric	graphify	Field
LOCOMO (n=300)	recall@10	0.497	mem0 0.048, supermemory 0.149
LOCOMO (n=300)	QA accuracy	45.3%	supermemory 49.7%, mem0 27.3%
LongMemEval-S (n=50)	QA accuracy	76%	tied with dense RAG
Graph build	LLM credits	0	per-token for most systems

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.

Prerequisites

Requirement	Minimum	Check	Install
Python	3.10+	`python --version`	python.org
uv (recommended)	any	`uv --version`	`curl -LsSf https://astral.sh/uv/install.sh \| sh`
pipx (alternative)	any	`pipx --version`	`pip install pipx`

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

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

Step 1 — install the package:

# Recommended (isolated env; if 'graphify' isn't found after, run: uv tool update-shell):
uv tool install graphifyy

# Alternatives:
pipx install graphifyy
pip install graphifyy  # may need PATH setup — see note below

Step 2 — register the skill with your AI assistant:

graphify install

That's it. Open your AI assistant an