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. Works in Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, and Google Antigravity. ``` /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 ``` For a readable architecture page with Mermaid call-flow diagrams, run: ```bash graphify export callflow-html ``` --- ## Install **Requires Python 3.10+** ```bash uv tool install graphifyy && graphify install # or: pipx install graphifyy && graphify install # or: pip install graphifyy && graphify install ``` > **Official package:** The PyPI package is `graphifyy` (double-y). Other `graphify*` packages on PyPI are not affiliated. The CLI command is still `graphify`. > **PowerShell note:** Use `graphify .` not `/graphify .` — the leading slash is a path separator in PowerShell and will cause a "not recognized" error. > **`graphify: command not found`?** Use `uv tool install graphifyy` or `pipx install graphifyy` — both put the CLI on PATH automatically. With plain `pip`, add `~/.local/bin` (Linux) or `~/Library/Python/3.x/bin` (Mac) to your PATH, or run `python -m graphify`. ### Pick your platform | Platform | Install command | |----------|----------------| | Claude Code (Linux/Mac) | `graphify install` | | Claude Code (Windows) | `graphify install --platform windows` | | Codex | `graphify install --platform codex` | | OpenCode | `graphify install --platform opencode` | | GitHub Copilot CLI | `graphify install --platform copilot` | | VS Code Copilot Chat | `graphify vscode install` | | Aider | `graphify install --platform aider` | | OpenClaw | `graphify install --platform claw` | | Factory Droid | `graphify install --platform droid` | | Trae | `graphify install --platform trae` | | Trae CN | `graphify install --platform trae-cn` | | Gemini CLI | `graphify install --platform gemini` | | Hermes | `graphify install --platform hermes` | | Kimi Code | `graphify install --platform kimi` | | Kiro IDE/CLI | `graphify kiro install` | | Pi coding agent | `graphify install --platform pi` | | Cursor | `graphify cursor install` | | Google Antigravity | `graphify antigravity install` | > Codex users: also add `multi_agent = true` under `[features]` in `~/.codex/config.toml`. > Codex uses `$graphify` instead of `/graphify`. --- ## Make your assistant always use the graph Run this once in your project after building a graph: | Platform | Command | |----------|---------| | Claude Code | `graphify claude install` | | Codex | `graphify codex install` | | OpenCode | `graphify opencode install` | | GitHub Copilot CLI | `graphify copilot install` | | VS Code Copilot Chat | `graphify vscode install` | | Aider | `graphify aider install` | | OpenClaw | `graphify claw install` | | Factory Droid | `graphify droid install` | | Trae | `graphify trae install` | | Trae CN | `graphify trae-cn install` | | Cursor | `graphify cursor install` | | Gemini CLI | `graphify gemini install` | | Hermes | `graphify hermes install` | | Kimi Code | `graphify install --platform kimi` | | Kiro IDE/CLI | `graphify kiro install` | | Pi coding agent | `graphify pi install` | | Google Antigravity | `graphify antigravity install` | This writes a small config file that tells your assistant to read `GRAPH_REPORT.md` before answering questions about your codebase. On platforms that support hooks (Claude Code, Codex, Gemini CLI), a hook fires automatically before every file-read call — your assistant navigates by the graph instead of grepping through everything. To remove graphify from all platforms at once: `graphify uninstall` (add `--purge` to also delete `graphify-out/`). Or use the per-platform command (e.g. `graphify claude uninstall`). --- ## What's in the report - **God nodes** — the most-connected concepts in your project. Everything flows through these. - **Surprising connections** — links between things that live in different files or modules. Ranked by how unexpected they are. - **The "why"** — inline comments (`# NOTE:`, `# WHY:`, `# HACK:`), docstrings, and design rationale from docs are extracted as separate nodes linked to the code they explain. - **Suggested questions** — 4–5 questions the graph is uniquely positioned to answer. - **Confidence tags** — every inferred relationship is marked `EXTRACTED`, `INFERRED`, or `AMBIGUOUS`. You always know what was found vs guessed. --- ## What files it handles | Type | Extensions | |------|-----------| | Code (29 languages) | `.py .ts .js .jsx .tsx .mjs .go .rs .java .c .cpp .h .hpp .rb .cs .kt .scala .php .swift .lua .luau .zig .ps1 .ex .exs .m .mm .jl .vue .svelte .groovy .gradle .dart .v .sv .sql .f .f90 .f95 .f03 .f08 .pas .pp .dpr .dpk .lpr .inc .dfm .lfm .lpk` | | Docs | `.md .mdx .qmd .html .txt .rst .yaml .yml` | | Office | `.docx .xlsx` (requires `pip install graphifyy[office]`) | | Google Workspace | `.gdoc .gsheet .gslides` (opt-in; requires `gws` auth and `--google-workspace`; Sheets need `pip install graphifyy[google]`) | | PDFs | `.pdf` | | Images | `.png .jpg .webp .gif` | | Video / Audio | `.mp4 .mov .mp3 .wav` and more (requires `pip install graphifyy[video]`) | | YouTube / URLs | any video URL (requires `pip install graphifyy[video]`) | Code is extracted locally with no API calls (AST via tree-sitter). Everything else goes through your AI assistant's model API. Google Drive for desktop `.gdoc`, `.gsheet`, and `.gslides` files are shortcut pointers, not document content. To include native Google Docs, Sheets, and Slides in a headless extraction, install and authenticate the [`gws` CLI](https://github.com/googleworkspace/cli), then run: ```bash pip install "graphifyy[google]" # needed for Google Sheets table rendering gws auth login -s drive graphify extract ./docs --google-workspace ``` You can also set `GRAPHIFY_GOOGLE_WORKSPACE=1`. Graphify exports shortcuts into `graphify-out/converted/` as Markdown sidecars, then extracts those files. --- ## Common commands ```bash /graphify . # build graph for current folder /graphify ./docs --update # re-extract only changed files /graphify . --cluster-only # rerun clustering without re-extracting /graphify . --no-viz # skip the HTML, just the report + JSON /graphify . --wiki # build a markdown wiki from the graph graphify export callflow-html # architecture/call-flow HTML from graphify-out/ /graphify query "what connects auth to the database?" /graphify path "UserService" "DatabasePool" /graphify explain "RateLimiter" /graphify add https://arxiv.org/abs/1706.03762 # fetch a paper and add it /graphify add # transcribe and add a video graphify hook install # auto-rebuild on git commit graphify merge-graphs a.json b.json # combine two graphs ``` See the [full command reference](#full-command-reference) below. --- ## Ignoring files Create a `.graphifyignore` in your project root — same syntax as `.gitignore`, including `!` negation: ``` # .graphifyignore node_modules/ dist/ *.generated.py # only index src/, ignore everything else * !src/ !src/** ``` --- ## Team setup `graphify-out/` is meant to be committed to git so everyone on the team starts with a map. **Recommended `.gitignore` additions:** ``` graphify-out/manifest.json # mtime-based, breaks after git clone graphify-out/cost.json # local only # graphify-out/cache/ # optional: commit for speed, skip to keep repo small ``` **Workflow:** 1. One person runs `/graphify .` and commits `graphify-out/`. 2. Everyone pulls — their assistant reads the graph immediately. 3. Run `graphify hook install` to auto-rebuild after each commit (AST only, no API cost). This also sets up a git merge driver so `graph.json` is never left with conflict markers — two devs committing in parallel get their graphs union-merged automatically. 4. When docs or papers change, run `/graphify --update` to refresh those nodes. --- ## Using the graph directly ```bash # query the graph from the terminal graphify query "show the auth flow" graphify query "what connects DigestAuth to Response?" --graph graphify-out/graph.json # expose the graph as an MCP server (for repeated tool-call access) python -m graphify.serve graphify-out/graph.json # register with Kimi Code: kimi mcp add --transport stdio graphify -- python -m graphify.serve graphify-out/graph.json ``` The MCP server gives your assistant structured access: `query_graph`, `get_node`, `get_neighbors`, `shortest_path`. > **WSL / Linux note:** Ubuntu ships `python3`, not `python`. Use a venv to avoid conflicts: > ```bash > python3 -m venv .venv && .venv/bin/pip install "graphifyy[mcp]" > ``` --- ## Privacy - **Code files** — processed locally via tree-sitter. Nothing leaves your machine. - **Video / audio** — transcribed locally with faster-whisper. Nothing leaves your machine. - **Docs, PDFs, images** — sent to your AI assistant for semantic extraction (via the `/graphify` skill, using whatever model your IDE session runs). Headless `graphify extract` requires `GEMINI_API_KEY` / `GOOGLE_API_KEY` (Gemini), `MOONSHOT_API_KEY` (Kimi), `ANTHROPIC_API_KEY` (Claude), `OPENAI_API_KEY` (OpenAI), a running Ollama instance (`OLLAMA_BASE_URL`), or AWS credentials via the standard provider chain (Bedrock - no API key needed, uses IAM). The `--dedup-llm` flag uses the same key. - No telemetry, no usage tracking, no analytics. --- ## Full command reference ``` /graphify # run on current directory /graphify ./raw # run on a specific folder /graphify ./raw --mode deep # more aggressive relationship extraction /graphify ./raw --update # re-extract only changed files /graphify ./raw --directed # preserve edge direction /graphify ./raw --cluster-only # rerun clustering on existing graph /graphify ./raw --no-viz # skip HTML visualization /graphify ./raw --obsidian # generate Obsidian vault /graphify ./raw --wiki # build agent-crawlable markdown wiki /graphify ./raw --svg # export graph.svg /graphify ./raw --graphml # export for Gephi / yEd /graphify ./raw --neo4j # generate cypher.txt for Neo4j /graphify ./raw --neo4j-push bolt://localhost:7687 /graphify ./raw --watch # auto-sync as files change /graphify ./raw --mcp # start MCP stdio server /graphify add https://arxiv.org/abs/1706.03762 /graphify add /graphify add https://... --author "Name" --contributor "Name" /graphify query "what connects attention to the optimizer?" /graphify query "..." --dfs --budget 1500 /graphify path "DigestAuth" "Response" /graphify explain "SwinTransformer" graphify uninstall # remove from all platforms in one shot graphify uninstall --purge # also delete graphify-out/ graphify hook install # post-commit + post-checkout hooks graphify hook uninstall graphify hook status graphify claude install / uninstall graphify codex install / uninstall graphify opencode install graphify cursor install / uninstall graphify gemini install / uninstall graphify copilot install / uninstall graphify aider install / uninstall graphify claw install / uninstall graphify droid install / uninstall graphify trae install / uninstall graphify trae-cn install / uninstall graphify hermes install / uninstall graphify kiro install / uninstall graphify antigravity install / uninstall graphify extract ./docs # headless LLM extraction for CI (no IDE needed) graphify extract ./docs --backend gemini # explicit backend: gemini, kimi, claude, openai, ollama, or bedrock graphify extract ./docs --backend gemini --model gemini-3.1-pro-preview graphify extract ./docs --backend ollama # local Ollama (set OLLAMA_BASE_URL / OLLAMA_MODEL) - no API key needed for loopback graphify extract ./docs --backend bedrock # AWS Bedrock via IAM - no API key, uses AWS credential chain graphify extract ./docs --max-workers 16 # AST parallelism (also GRAPHIFY_MAX_WORKERS) graphify extract ./docs --token-budget 30000 # smaller semantic chunks for local/small models graphify extract ./docs --max-concurrency 2 # fewer parallel LLM calls (useful for local inference) graphify extract ./docs --api-timeout 900 # longer HTTP timeout for slow local models (default 600s) graphify extract ./docs --google-workspace # export .gdoc/.gsheet/.gslides via gws before extraction graphify extract ./docs --no-cluster # raw extraction only, skip clustering graphify extract ./docs --dedup-llm # LLM tiebreaker for ambiguous entity pairs (uses same API key) graphify extract ./docs --global --as myrepo # extract and register into the cross-project global graph GRAPHIFY_MAX_OUTPUT_TOKENS=32768 graphify extract ./docs --backend claude # raise output cap for dense corpora graphify export callflow-html # graphify-out/-callflow.html graphify export callflow-html --max-sections 8 # cap generated architecture sections graphify export callflow-html --output docs/arch.html graphify export callflow-html ./some-repo/graphify-out graphify global add graphify-out/graph.json myrepo # register a project graph into ~/.graphify/global.json graphify global remove myrepo # remove a project from the global graph graphify global list # show all registered repos + node/edge counts graphify global path # print path to the global graph file graphify clone https://github.com/karpathy/nanoGPT graphify merge-graphs a.json b.json --out merged.json graphify watch ./src graphify check-update ./src graphify update ./src graphify cluster-only ./my-project graphify cluster-only ./my-project --graph path/to/graph.json # custom graph location ``` --- ## Learn more - [How it works](docs/how-it-works.md) — the extraction pipeline, community detection, confidence scoring, benchmarks - [ARCHITECTURE.md](ARCHITECTURE.md) — module breakdown, how to add a language - [Optional integrations](docs/docker-mcp-sqlite.md) — Docker MCP Toolkit + SQLite --- ## Built on graphify — Penpax [**Penpax**](https://graphifylabs.ai) is the always-on layer built on top of graphify — it applies the same graph approach to your entire working life: meetings, browser history, emails, files, and code, updating continuously in the background. Built for people whose work lives across hundreds of conversations and documents they can never fully reconstruct. No cloud, fully on-device. **Free trial launching soon.** [Join the waitlist →](https://graphifylabs.ai) ---

Contributing

**Worked examples** are the most useful contribution. Run `/graphify` on a real corpus, save the output to `worked/{slug}/`, write an honest `review.md` covering what the graph got right and wrong, and open a PR. **Extraction bugs** — open an issue with the input file, the cache entry (`graphify-out/cache/`), and what was missed or wrong. See [ARCHITECTURE.md](ARCHITECTURE.md) for module responsibilities and how to add a language.