graphifyy 0.7.13

AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kiro, Pi, Google Antigravity) - turn any folder of code, docs, papers, images, or videos into a queryable knowledge graph

🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch | 🇫🇷 Français | 🇪🇸 Español | 🇮🇳 हिन्दी | 🇧🇷 Português | 🇷🇺 Русский | 🇸🇦 العربية | 🇮🇹 Italiano | 🇵🇱 Polski | 🇳🇱 Nederlands | 🇹🇷 Türkçe | 🇺🇦 Українська | 🇻🇳 Tiếng Việt | 🇮🇩 Bahasa Indonesia | 🇸🇪 Svenska | 🇬🇷 Ελληνικά | 🇷🇴 Română | 🇨🇿 Čeština | 🇫🇮 Suomi | 🇩🇰 Dansk | 🇳🇴 Norsk | 🇭🇺 Magyar | 🇹🇭 ภาษาไทย | 🇹🇼 繁體中文

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:

graphify export callflow-html

---

Install

Requires Python 3.10+

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, then run:

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

/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 <youtube-url>                       # 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 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

# 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:

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 <video-url>
/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/<project>-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 — the extraction pipeline, community detection, confidence scoring, benchmarks
• ARCHITECTURE.md — module breakdown, how to add a language
• Optional integrations — Docker MCP Toolkit + SQLite

---

Built on graphify — Penpax

Penpax 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 →

---

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 for module responsibilities and how to add a language.