{"repo":"Graphify-Labs/graphify","free":true,"listed":false,"github":"https://github.com/Graphify-Labs/graphify","clone":"git clone https://github.com/Graphify-Labs/graphify.git","description":"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.","language":"Python","stars":107038,"topics":["ai-agents","antigravity","ast","claude-code","code-analysis","code-search","codex","cursor","developer-tools","gemini","graphrag","knowledge-graph","leiden","llm","mcp","openclaw","rag","skills","tree-sitter"],"license":"Apache-2.0","category":"dev_tool","readme_excerpt":"<p align=\"center\">\n  <a href=\"https://graphify.com\"><img src=\"https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/logo.png\" width=\"300\" height=\"140\" alt=\"Graphify\"/></a>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://trendshift.io/repositories/25296?utm_source=repository-badge&amp;utm_medium=badge&amp;utm_campaign=badge-repository-25296\" target=\"_blank\" rel=\"noopener noreferrer\"><img src=\"https://trendshift.io/api/badge/repositories/25296\" alt=\"Graphify-Labs%2Fgraphify | Trendshift\" width=\"250\" height=\"55\"/></a>\n</p>\n\n<div align=\"center\">\n<details><summary><b>Read this in other languages</b></summary>\n\n🇺🇸 <a href=\"README.md\">English</a> | 🇨🇳 <a href=\"docs/translations/README.zh-CN.md\">简体中文</a> | 🇯🇵 <a href=\"docs/translations/README.ja-JP.md\">日本語</a> | 🇰🇷 <a href=\"docs/translations/README.ko-KR.md\">한국어</a> | 🇩🇪 <a href=\"docs/translations/README.de-DE.md\">Deutsch</a> | 🇫🇷 <a href=\"docs/translations/README.fr-FR.md\">Français</a> | 🇪🇸 <a href=\"docs/translations/README.es-ES.md\">Español</a> | 🇮🇳 <a href=\"docs/translations/README.hi-IN.md\">हिन्दी</a> | 🇧🇷 <a href=\"docs/translations/README.pt-BR.md\">Português</a> | 🇷🇺 <a href=\"docs/translations/README.ru-RU.md\">Русский</a> | 🇸🇦 <a href=\"docs/translations/README.ar-SA.md\">العربية</a> | 🇮🇷 <a href=\"docs/translations/README.fa-IR.md\">فارسی</a> | 🇮🇹 <a href=\"docs/translations/README.it-IT.md\">Italiano</a> | 🇵🇱 <a href=\"docs/translations/README.pl-PL.md\">Polski</a> | 🇳🇱 <a href=\"docs/translations/README.nl-NL.md\">Nederlands</a> | 🇹🇷 <a href=\"docs/translations/README.tr-TR.md\">Türkçe</a> | 🇺🇦 <a href=\"docs/translations/README.uk-UA.md\">Українська</a> | 🇻🇳 <a href=\"docs/translations/README.vi-VN.md\">Tiếng Việt</a> | 🇮🇩 <a href=\"docs/translations/README.id-ID.md\">Bahasa Indonesia</a> | 🇸🇪 <a href=\"docs/translations/README.sv-SE.md\">Svenska</a> | 🇬🇷 <a href=\"docs/translations/README.el-GR.md\">Ελληνικά</a> | 🇷🇴 <a href=\"docs/translations/README.ro-RO.md\">Română</a> | 🇨🇿 <a href=\"docs/translations/README.cs-CZ.md\">Čeština</a> | 🇫🇮 <a href=\"docs/translations/README.fi-FI.md\">Suomi</a> | 🇩🇰 <a href=\"docs/translations/README.da-DK.md\">Dansk</a> | 🇳🇴 <a href=\"docs/translations/README.no-NO.md\">Norsk</a> | 🇭🇺 <a href=\"docs/translations/README.hu-HU.md\">Magyar</a> | 🇹🇭 <a href=\"docs/translations/README.th-TH.md\">ภาษาไทย</a> | 🇺🇿 <a href=\"docs/translations/README.uz-UZ.md\">Oʻzbekcha</a> | 🇹🇼 <a href=\"docs/translations/README.zh-TW.md\">繁體中文</a> | 🇵🇭 <a href=\"docs/translations/README.fil-PH.md\">Filipino</a> | 🇮🇱 <a href=\"docs/translations/README.he-IL.md\">עברית</a>\n\n</details>\n</div>\n\n<p align=\"center\">\n  <a href=\"https://pypi.org/project/graphifyy/\"><img src=\"https://img.shields.io/pypi/v/graphifyy\" alt=\"PyPI\"/></a>\n  <a href=\"https://pepy.tech/project/graphifyy\"><img src=\"https://img.shields.io/pepy/dt/graphifyy?color=blue&label=downloads\" alt=\"Downloads\"/></a>\n  <a href=\"https://discord.gg/598Ad9zQZ\"><img src=\"https://img.shields.io/badge/Discord-Join-5865F2?style=flat&logo=discord&logoColor=white\" alt=\"Discord\"/></a>\n  <a href=\"https://www.youtube.com/@graphifylabs\"><img src=\"https://img.shields.io/badge/YouTube-Graphify%20Labs-FF0000?style=flat&logo=youtube&logoColor=white\" alt=\"YouTube\"/></a>\n  <a href=\"https://www.linkedin.com/company/graphify-labs\"><img src=\"https://img.shields.io/badge/LinkedIn-Graphify%20Labs-0077B5?logo=linkedin\" alt=\"LinkedIn\"/></a>\n  <a href=\"https://www.ycombinator.com/companies/graphify-labs\"><img src=\"https://img.shields.io/badge/Y%20Combinator-S26-F0652F?style=flat&logo=ycombinator&logoColor=white\" alt=\"YC S26\"/></a>\n</p>\n\n<p align=\"center\">\n  <b>Early access to the graphify platform is open before the public v1 launch: <a href=\"https://app.graphify.com/login\">app.graphify.com</a></b>\n</p>\n\nType `/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.\n\n- **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.)\n- **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.\n- **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.\n\n> 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](https://graphify.com)**, and early access is open now at **[app.graphify.com](https://app.graphify.com/login)**.\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/graph-hero.png\" alt=\"graphify's interactive graph.html showing the FastAPI codebase as a force-directed knowledge graph with a legend of detected communities\" width=\"900\">\n</p>\n<p align=\"center\">\n  <em>The FastAPI codebase mapped by graphify. Every node is a concept, colors are detected communities, and the whole thing is clickable in graph.html.</em>\n</p>\n\n**Get started** (30 seconds):\n\n```bash\nuv tool install graphifyy      # install the CLI (or: pipx install graphifyy)\ngraphify install               # register the skill with your AI assistant\n```\n\nThen, in your AI assistant:\n\n```\n/graphify .\n```\n\nThat's it. You get **three files**:\n\n```\ngraphify-out/\n├── graph.html       open in any browser — click nodes, filter, search\n├── GRAPH_REPORT.md  the highlights: key concepts, surprising connections, suggested questions\n└── graph.json       the full graph — query it anytime without re-reading your files\n```\n\n**Works in** Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 15+ more — [pick your platform](#install).\n\n---\n\n## See it in action\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/demo-path.svg\" alt=\"graphify path query: a terminal asks for the shortest path between FastAPI and ModelField, and the answer lights up hop by hop across the knowledge graph\" width=\"900\">\n</p>\n\nOnce the graph is built you query it instead of reading files. Real output, graphify run on the FastAPI codebase shown above:\n\n```text\n$ graphify explain \"APIRouter\"\nNode: APIRouter\n  Source:    routing.py L2210\n  Community: 2\n  Degree:    47\n\nConnections (47):\n  --> RequestValidationError [uses] [INFERRED]\n  --> Dependant [uses] [INFERRED]\n  --> .get() [method] [EXTRACTED]\n  <-- __init__.py [imports] [EXTRACTED]\n  ...\n\n$ graphify path \"FastAPI\" \"ModelField\"\nShortest path (3 hops):\n  FastAPI --uses--> DefaultPlaceholder <--references-- get_request_handler() --references--> ModelField\n```\n\nEvery 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.\n\n---\n\n## What it does\n\nWhat you get out of the box:\n\n| Capability | What you get |\n|---|---|\n| **God nodes** | The most-connected concepts, so you see what everything flows through |\n| **Communities** | The graph split into subsystems (Leiden), with LLM-free labels |\n| **Cross-file links** | `calls` / `imports` / `inherits` / `mixes_in` resolved across ~40 languages via tree-sitter AST |\n| **Query, path, explain** | Ask a question, trace the path between two things, or explain one concept, all against `graph.json` |\n| **Rationale + doc refs** | `# NOTE:` / `# WHY:` comments and ADR/RFC citations become first-class nodes linked to the code |\n| **Beyond code** | Docs, PDFs, images, and video/audio all map into the same graph |\n| **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 |\n\n---\n\n## Benchmarks\n\n| Benchmark | Metric | graphify | Field |\n|---|---|---|---|\n| LOCOMO (n=300) | recall@10 | **0.497** | mem0 0.048, supermemory 0.149 |\n| LOCOMO (n=300) | QA accuracy | 45.3% | supermemory 49.7%, mem0 27.3% |\n| LongMemEval-S (n=50) | QA accuracy | **76%** | tied with dense RAG |\n| Graph build | LLM credits | **0** | per-token for most systems |\n\nEvery 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](./BENCHMARKS.md)**.\n\n---\n\n## Prerequisites\n\n| Requirement | Minimum | Check | Install |\n|---|---|---|---|\n| Python | 3.10+ | `python --version` | [python.org](https://www.python.org/downloads/) |\n| uv *(recommended)* | any | `uv --version` | `curl -LsSf https://astral.sh/uv/install.sh \\| sh` |\n| pipx *(alternative)* | any | `pipx --version` | `pip install pipx` |\n\n**macOS quick install (Homebrew):**\n```bash\nbrew install python@3.12 uv\n```\n\n**Windows quick install:**\n```powershell\nwinget install astral-sh.uv\n```\n\n**Ubuntu/Debian:**\n```bash\nsudo apt install python3.12 python3-pip pipx\n# or install uv:\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n---\n\n## Install\n\n> **Official package:** The PyPI package is `graphifyy` (double-y). Other `graphify*` packages on PyPI are not affiliated. The CLI command is still `graphify`.\n\n**Step 1 — install the package:**\n\n```bash\n# Recommended (isolated env; if 'graphify' isn't found after, run: uv tool update-shell):\nuv tool install graphifyy\n\n# Alternatives:\npipx install graphifyy\npip install graphifyy  # may need PATH setup — see note below\n```\n\n**Step 2 — register the skill with your AI assistant:**\n\n```bash\ngraphify install\n```\n\nThat's it. Open your AI assistant and type `/graphify .`\n\nTo install the assistant skill into the 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