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.
# Add to your Claude Code skills
git clone https://github.com/Graphify-Labs/graphifyLast scanned: 7/4/2026
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"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"
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"status": "PASSED",
"scannedAt": "2026-07-04T06:46:20.704Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how graphify compares with popular alternatives.
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 123,169 GitHub stars.
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.
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).
graphify is primarily written in Python. It is open-source under Graphify-Labs on GitHub, so you can review or fork the full source.
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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⚠️ Third-Party Software Notice
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
🇺🇸 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
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.
EXTRACTED (explicit in the source) or INFERRED (resolved by graphify), so you can tell what was read directly from what was inferred.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.
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 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 |
| 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.
| 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
Official package: The PyPI package is
graphifyy(double-y). Othergraphify*packag