Raw capture — raw/graphify.md

url: https://github.com/safishamsi/graphify/ date_fetched: 2026-07-05 backfilled: true

🇺🇸 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

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, Kilo Code, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Amp, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, Devin CLI, 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-htmlOn an open harness where every system uses the same model, the same budgets, and a judge blind-validated against a second judge (90.6% agreement, Cohen's kappa 0.81), graphify has the best retrieval recall of any memory system tested on LOCOMO (about 10x mem0), ties for best on LongMemEval-S (76%), and builds its graph with zero LLM credits. It also beats a grep+read baseline on real code-intelligence tasks (ERPNext, ~1M LOC) at a fraction of the token cost.

Full methodology, per-system tables, judge validation, and reproduction commands: BENCHMARKS.md.

macOS quick install (Homebrew):

brew install python@3.12 uvWindows quick install:

winget install astral-sh.uvUbuntu/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 isgraphifyy(double-y). Othergraphify*packages on PyPI are not affiliated. The CLI command is stillgraphify.

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 installThat's it. Open your AI assistant and type /graphify .

To install the assistant skill into the current repository instead of your user profile, add --project:

graphify install --project
graphify install --project --platform codex

Project-scoped installs write under the current directory, for example .claude/skills/graphify/SKILL.md or .agents/skills/graphify/SKILL.md (plus a references/ sidecar the skill loads on demand), and print a git add hint for files that can be committed. Per-platform commands that support project-scoped installs accept the same flag, for example graphify claude install --project or graphify codex install --project.

PowerShell note:Usegraphify .not/graphify .— the leading slash is a path separator in PowerShell.

graphify: command not found?uv tool install/pipx installput thegraphifycommand in their tool bin dir (~/.local/bin). If your shell can't find it right after install — common on a fresh macOS + zsh setup — that dir isn't on yourPATHyet: runuv tool update-shell(orpipx ensurepath), then open a new terminal. With plainpip, add~/.local/bin(Linux) or~/Library/Python/3.x/bin(Mac) to your PATH, or runpython -m graphify.

Running withName the package, not the command:uvx/uv tool runinstead of installing?uvx --from graphifyy graphify install. Plainuvx graphify …fails (No solution found … no versions of graphify) becauseuv tool runreads the first word as apackage, and the package isgraphifyy— thegraphifycommand lives inside it.

Avoidif possible. The skill resolves Python at runtime frompip installon Mac/Windowsgraphify-out/.graphify_python; if that points to a different environment than wherepipinstalled the package, you'll getModuleNotFoundError: No module named 'graphify'.uv tool installandpipx installisolate the package in their own env and avoid this entirely.

Git hooks and uv tool / pipx:graphify hook installembeds the current interpreter path directly into the hook scripts at install time, so the post-commit hook fires correctly even in GUI git clients and CI runners where~/.local/binis not on PATH. If you reinstall or upgrade graphify, re-rungraphify hook installto refresh the embedded path.

Codex users also need multi_agent = true under [features] in ~/.codex/config.toml for parallel extraction. CodeBuddy uses the same Agent tool and PreToolUse hook mechanism as Claude Code. Factory Droid uses the Task tool for parallel subagent dispatch. OpenClaw and Aider use sequential extraction (parallel agent support is still early on those platforms). Trae uses the Agent tool for parallel subagent dispatch and does not support PreToolUse hooks — AGENTS.md is the always-on mechanism.

--platform agents (alias --platform skills) targets the generic cross-framework Agent-Skills locations: the spec's user-global ~/.agents/skills/ (read by npx skills and spec-compliant frameworks) for a global install, and ./.agents/skills/ for a project (--project) install. The bare graphify install stays single-platform (Claude Code) by design — use the named agents platform when you want the skill discoverable by any framework that reads .agents/skills.

Codex uses

$graphifyinstead of/graphify.

Install only what you need:

Run this once in your project after building a graph:

This writes a small config file that tells your assistant to consult the knowledge graph for codebase questions — preferring scoped queries like graphify query "<question>" over reading the full report or grepping raw files. On platforms that support payload-bearing hooks (Claude Code, Gemini CLI), a hook fires automatically before search-style tool calls (and, on Claude Code, before reading source files one by one via the Read/Glob tools) and nudges your assistant toward the graph path. On the others (Codex, OpenCode, Cursor, etc.), the persistent instruction files (AGENTS.md, .cursor/rules/, etc.) provide the same query-first guidance. GRAPH_REPORT.md is still available for broad architecture review.

CodeBuddy does the same two things as Claude Code: writes a CODEBUDDY.md section telling CodeBuddy to read graphify-out/GRAPH_REPORT.md before answering architecture questions, and installs PreToolUse hooks (.codebuddy/settings.json) that fire before Bash search commands and file reads, nudging toward graphify query instead.

Codex writes to AGENTS.md and also installs a PreToolUse hook in .codex/hooks.json that fires before every Bash tool call — same always-on mechanism as Claude Code.

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).

Kilo Code installs the Graphify skill to ~/.config/kilo/skills/graphify/SKILL.md and a native /graphify command to ~/.config/kilo/command/graphify.md. graphify kilo install also writes AGENTS.md plus a native ** tool.execute.before plugin** (

.kilo/plugins/graphify.js + .kilo/kilo.json or .kilo/kilo.jsonc registration) so Kilo gets the same always-on graph reminder behavior through native .kilo config.Cursor writes .cursor/rules/graphify.mdc with alwaysApply: true — Cursor includes it in every conversation automatically, no hook needed.

• 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.

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:

uv tool 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.

/graphify .                        # build graph for current folder
/graphify ./docs --update          # re-extract only changed files
/graphify . --cluster-only         # rerun clustering without re-extracting
/graphify . --cluster-only --resolution 1.5      # more granular communities
/graphify . --cluster-only --exclude-hubs 99     # suppress utility super-hubs from god-node rankings
/graphify . --no-viz               # skip the HTML, just the report + JSON
/graphify . --wiki                 # build a markdown wiki from the graph
graphify export callflow-html      # Mermaid architecture/call-flow HTML (auto-regenerates on every git commit if hook is installed)
/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
graphify prs                       # PR dashboard: CI state, review status, worktree mapping
graphify prs 42                    # deep dive on PR #42 with graph impact
graphify prs --triage              # AI ranks your review queue (uses whatever backend is configured)
graphify prs --conflicts           # PRs sharing graph communities — merge-order risk

See the full command reference below.

Create a .graphifyignore in your project root — same syntax as .gitignore, including ! negation.

** .gitignore is respected automatically.** graphify reads the

.gitignore in each directory. If a .graphifyignore is also present, the two are merged—

.graphifyignore patterns are evaluated last, so they win on conflicts (including ! negations). Adding a .graphifyignore only ever excludes more; it never re-includes a file your .gitignore already excluded. Subdirectory scoping works the same way as git — an ignore file only affects its own subtree.```

.graphifyignore

node_modules/ dist/ *.generated.py

only index src/, ignore everything else

* !src/ !src/**

`graphify-out/` is meant to be committed to git so everyone on the team starts with a map.

**Recommended  .gitignore additions:**

graphify-out/cost.json # local only

graphify-out/cache/ # optional: commit for speed, skip to keep repo small

`manifest.json`is now portable — keys are stored as relative paths and re-anchored on load, so committing it is safe and avoids a full rebuild on first checkout.

**Workflow:**

- One person runs `/graphify .`and commits`graphify-out/`.
- Everyone pulls — their assistant reads the graph immediately.
- 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.
- When docs or papers change, run `/graphify --update`to refresh those nodes.

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 python -m graphify.serve --graph graphify-out/graph.json # --graph flag also accepted

register with Kimi Code:

kimi mcp add --transport stdio graphify -- python -m graphify.serve graphify-out/graph.json

or serve over HTTP so a whole team points at one URL (no local graphify needed):

python -m graphify.serve graphify-out/graph.json --transport http --port 8080 python -m graphify.serve graphify-out/graph.json --transport http --host 0.0.0.0 --api-key "$SECRET"

The MCP server gives your assistant structured access: `query_graph`, `get_node`, `get_neighbors`, `shortest_path`, `list_prs`, `get_pr_impact`, `triage_prs`.

`--transport stdio` (the default) spawns one local server per developer. `--transport http` serves the same tools over the MCP Streamable HTTP transport, so a single shared process can serve the graph for the whole team — clients point their IDE MCP config at `http://<host>:8080/mcp` instead of running graphify locally.

| Flag | Default | Purpose | 
|---|---|---|
| `--transport {stdio,http}` | `stdio` | Transport to serve on | 
| `--host` | `127.0.0.1` | HTTP bind host (use `0.0.0.0`to expose beyond localhost) | 
| `--port` | `8080` | HTTP bind port | 
| `--api-key` | env `GRAPHIFY_API_KEY` | Require `Authorization: Bearer <key>`(or`X-API-Key`) | 
| `--path` | `/mcp` | HTTP mount path | 
| `--json-response` | off | Return plain JSON instead of SSE streams | 
| `--stateless` | off | No per-session state (for load-balanced / CI deployments) | 
| `--session-timeout` | `3600` | Reap idle stateful sessions after N seconds ( `0`disables) | 

The default `127.0.0.1` bind is loopback-only. Set `--host 0.0.0.0` **and** `--api-key` together when exposing on a shared host. Run it in a container:

docker build -t graphify . docker run -p 8080:8080 -v "$(pwd)/graphify-out:/data" graphify \ /data/graph.json --transport http --host 0.0.0.0 --api-key "$SECRET"

WSL / Linux note:Ubuntu ships`python3`, not`python`. Use a venv to avoid conflicts:python3 -m venv .venv && .venv/bin/pip install "graphifyy[mcp]"

These are only needed for **headless / CI extraction** (`graphify extract`). When running via the `/graphify` skill inside your IDE, the model API is provided by your IDE session — no extra keys needed.

| Variable | Used for | When required | 
|---|---|---|
| `ANTHROPIC_API_KEY` | Claude (Anthropic) backend | `--backend claude` | 
| `ANTHROPIC_BASE_URL` | Anthropic-compatible endpoint URL (LiteLLM proxy, gateways, ...) | `--backend claude`(default:`https://api.anthropic.com`) | 
| `ANTHROPIC_MODEL` | Model name for the Claude backend — for custom endpoints, use the model name/alias your server exposes | `--backend claude`(default:`claude-sonnet-4-6`) | 
| `GEMINI_API_KEY`or`GOOGLE_API_KEY` | Google Gemini backend | `--backend gemini` | 
| `OPENAI_API_KEY` | OpenAI or OpenAI-compatible APIs | `--backend openai`(local servers accept any non-empty value) | 
| `OPENAI_BASE_URL` | OpenAI-compatible server URL (llama.cpp, vLLM, LM Studio, ...) | `--backend openai`(default:`https://api.openai.com/v1`) | 
| `OPENAI_MODEL` | Model name for the OpenAI backend — for self-hosted servers, use the model name/alias your server exposes (check its `/v1/models`endpoint), e.g.`LFM2.5-8B-A1B-UD-Q4_K_XL`for llama.cpp | `--backend openai`(default:`gpt-4.1-mini`) | 
| `DEEPSEEK_API_KEY` | DeepSeek backend | `--backend deepseek` | 
| `MOONSHOT_API_KEY` | Kimi Code backend | `--backend kimi` | 
| `OLLAMA_BASE_URL` | Ollama local inference URL | `--backend ollama`(default:`http://localhost:11434`) | 
| `OLLAMA_MODEL` | Ollama model name | `--backend ollama`(default: auto-detect) | 
| `GRAPHIFY_OLLAMA_NUM_CTX` | Override Ollama KV-cache window size | optional — auto-sized by default | 
| `GRAPHIFY_OLLAMA_KEEP_ALIVE` | Minutes to keep Ollama model loaded | optional — set `0`to unload after each chunk | 
| `AZURE_OPENAI_API_KEY` | Azure OpenAI Service backend | `--backend azure` | 
| `AZURE_OPENAI_ENDPOINT` | Azure resource endpoint URL | `--backend azure`(required alongside API key) | 
| `AZURE_OPENAI_API_VERSION` | Azure API version override | optional — default `2024-12-01-preview` | 
| `AZURE_OPENAI_DEPLOYMENT`or`GRAPHIFY_AZURE_MODEL` | Azure deployment name | optional — default `gpt-4o` | 
| `AWS_*`/`~/.aws/credentials` | AWS Bedrock — standard credential chain | `--backend bedrock`(no API key, uses IAM) | 
| `GRAPHIFY_MAX_WORKERS` | AST parallelism thread count | optional — also `--max-workers`flag | 
| `GRAPHIFY_MAX_OUTPUT_TOKENS` | Raise output cap for dense corpora | optional — e.g. `32768`for large files | 
| `GRAPHIFY_API_TIMEOUT` | Per-call timeout in seconds for HTTP, claude-cli, and Anthropic SDK backends (default: 600) | optional — also `--api-timeout`flag | 
| `GRAPHIFY_MAX_RETRIES` | How many times to retry a rate-limited (429) request before giving up (default: 6; honors `Retry-After`) | optional — raise for strict per-org limits (e.g. kimi); `0`disables | 
| `GRAPHIFY_FORCE` | Force graph rebuild even with fewer nodes | optional — also `--force`flag | 
| `GRAPHIFY_GOOGLE_WORKSPACE` | Auto-enable Google Workspace export | optional — set to `1` | 
| `GRAPHIFY_TRIAGE_BACKEND` | Backend for `graphify prs --triage` | optional — auto-detected from available keys | 
| `GRAPHIFY_TRIAGE_MODEL` | Model override for triage | optional — e.g. `claude-opus-4-7` | 
| `GRAPHIFY_QUERY_LOG` | Override query log path (default: `~/.cache/graphify-queries.log`) | optional — set to empty or `/dev/null`to silence | 
| `GRAPHIFY_QUERY_LOG_DISABLE` | Set to `1`to disable query logging entirely | optional | 
| `GRAPHIFY_QUERY_LOG_RESPONSES` | Set to `1`to also log full subgraph responses (off by default) | optional | 
| `GRAPHIFY_MAX_GRAPH_BYTES` | Override the 512 MiB graph.json size cap — e.g. `700MB`,`2GB`, or plain bytes | optional — useful for very large corpora | 
| `GRAPHIFY_LLM_TEMPERATURE` | Override LLM temperature for semantic extraction — e.g. `0.7`, or`none`to omit | optional — auto-omitted for o1/o3/o4/gpt-5 reasoning models | 

- **Code files**— processed locally via tree-sitter. Nothing leaves your machine. A code-only corpus requires no API key —- `graphify extract`runs fully offline.
- **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),- `DEEPSEEK_API_KEY`(DeepSeek), a running Ollama instance (- `OLLAMA_BASE_URL`), AWS credentials via the standard provider chain (Bedrock - no API key needed, uses IAM), or the- `claude`CLI binary (Claude Code - no API key needed, uses your Claude subscription). The- `--dedup-llm`flag uses the same key.
- **Data residency**—- `graphify extract`auto-detects which provider to use based on which API key is set (priority: Gemini → Kimi → Claude → OpenAI → DeepSeek → Azure → Bedrock → Ollama). For code with data-residency requirements, use- `--backend ollama`(fully local) or pass an explicit- `--backend`flag. Kimi (- `MOONSHOT_API_KEY`) routes to Moonshot AI servers in China.
- No telemetry, no usage tracking, no analytics.
- **Query logging**— every- `graphify query`,- `graphify path`,- `graphify explain`, and MCP- `query_graph`call is logged to- `~/.cache/graphify-queries.log`in JSON Lines format (timestamp, question, corpus, nodes returned, duration). Full subgraph responses are- **not**stored by default. Set- `GRAPHIFY_QUERY_LOG_DISABLE=1`to opt out, or- `GRAPHIFY_QUERY_LOG=/dev/null`to silence without disabling the code path.

** graphify: command not found after installing**
The CLI is installed but its bin directory isn't on your shell's 

`PATH`. Pick the fix for how you installed:- **uv**(- `uv tool install graphifyy`): the command lands in uv's tool bin dir (- `~/.local/bin`), which a fresh macOS/zsh setup often doesn't have on- `PATH`. Run- `uv tool update-shell`, then open a new terminal. (Find the dir with- `uv tool dir --bin`.)
- **pipx**(- `pipx install graphifyy`): run- `pipx ensurepath`, then open a new terminal.
- **pip**(- `pip install graphifyy`): pip installs scripts to a user bin dir that may not be on- `PATH`— add- `~/Library/Python/3.x/bin`(macOS) or- `~/.local/bin`(Linux) to your- `PATH`in- `~/.zshrc`/- `~/.bashrc`, or just run- `python -m graphify`.

** uvx graphify … or uv tool run graphify … fails to resolve graphify**
The PyPI package is 

`graphifyy`; `graphify` is only the command it provides. `uv tool run` treats the first word as a *package name*, so it looks for a package called

`graphify` and reports `No solution found … no versions of graphify`. Name the package explicitly: `uvx --from graphifyy graphify install` (same as `uv tool run --from graphifyy graphify install`). Or `uv tool install graphifyy` once and then call `graphify` directly.** python -m graphify works but graphify command doesn't**
Your shell's 

`PATH` doesn't include the bin directory the command was installed to. Prefer `uv tool install` / `pipx install` over plain `pip`, then run `uv tool update-shell` / `pipx ensurepath` and open a new terminal (see the install notes above).** /graphify . causes "path not recognized" in PowerShell**
PowerShell treats a leading 

`/` as a path separator. Use `graphify .` (no slash) on Windows.**Graph has fewer nodes after  --update or rebuild**
If a refactor deleted files, the old nodes linger. Pass 

`--force` (or set `GRAPHIFY_FORCE=1`) to overwrite even when the rebuild has fewer nodes.**Graph has duplicate nodes for the same entity (ghost duplicates)**
Ghost duplicates (same symbol appearing twice — once from AST extraction with a source location, once from semantic extraction without) are now automatically merged at build time. If you see this in a graph built before v0.8.33, run a full re-extract to clean up:

`graphify extract . --force`**Ollama runs out of VRAM / context window exceeded**
The KV-cache window is auto-sized but may be too large for your GPU. Reduce it:

`GRAPHIFY_OLLAMA_NUM_CTX=8192 graphify extract ./docs --backend ollama --token-budget 4000`** LLM returned invalid JSON / Unterminated string warnings**
The model's JSON response hit its output-token limit and was cut off mid-string. graphify auto-recovers (it splits the chunk and re-extracts the halves, and an oversized single document is first sliced at heading/paragraph boundaries so the whole file is still covered), so these warnings are noisy but not data loss. To reduce the churn, raise the output cap or shrink each chunk's output:

GRAPHIFY_MAX_OUTPUT_TOKENS=16384 graphify extract . --mode deep # lift the cap graphify extract . --mode deep --token-budget 4000 # smaller input chunks -> smaller output

With a cloud gateway like OpenRouter, prefer `--backend openai` (set `OPENAI_BASE_URL`) over the Ollama shim — it's a cleaner OpenAI-compatible path. If the model has its own max-output ceiling, lowering `--token-budget` is the reliable lever.

**Graph HTML is too large to open in a browser (>5000 nodes)**
Skip HTML generation and use the JSON directly:

graphify cluster-only ./my-project --no-viz graphify query "..."

** graph.json has conflict markers after two devs commit at once**
Run 

`graphify hook install` — it sets up a git merge driver that union-merges `graph.json` automatically so conflicts never happen.**Extraction returns empty nodes/edges for docs or PDFs**
Docs, PDFs, and images require an LLM call — code-only corpora need no key. Check that your API key is set and the backend is correct:

`ANTHROPIC_API_KEY=sk-... graphify extract ./docs --backend claude`**Skill version mismatch warning in your IDE**
Your installed graphify version is different from the skill file. Update:

uv tool upgrade graphifyy graphify install # overwrites the skill file

/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 --obsidian --obsidian-dir ~/vault # write into an existing vault (never overwrites your own notes or .obsidian config) /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 --falkordb # generate cypher.txt for FalkorDB /graphify ./raw --falkordb-push falkordb://localhost:6379 /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 save-result --question "Q" --answer "A" --nodes Foo Bar --outcome useful # record how a Q&A turned out (work memory; outcome ∈ useful|dead_end|corrected) graphify reflect # aggregate graphify-out/memory/ outcomes into reflections/LESSONS.md graphify reflect --if-stale # no-op when LESSONS.md is already newer than every input (cheap to run each session) graphify reflect --out docs/LESSONS.md # write the lessons doc somewhere else graphify reflect --graph graphify-out/graph.json # group lessons by community + write the work-memory overlay (.graphify_learning.json) # the overlay tags nodes preferred/tentative/contested (recency-weighted, with provenance); # graphify explain / query then show a "Lesson:" hint, flagged "code changed — re-verify" when the source moved on graphify uninstall # remove from all platforms in one shot graphify uninstall --purge # also delete graphify-out/ graphify uninstall --project --platform codex # remove project-scoped install files only graphify hook install # post-commit + post-checkout hooks graphify hook uninstall graphify hook status

always-on assistant instructions - platform-specific

graphify claude install # CLAUDE.md + PreToolUse hook (Claude Code) graphify claude uninstall graphify codebuddy install # CODEBUDDY.md + PreToolUse hook (CodeBuddy) graphify codebuddy uninstall graphify codex install # AGENTS.md + PreToolUse hook in .codex/hooks.json (Codex) graphify opencode install # AGENTS.md + tool.execute.before plugin (OpenCode) graphify kilo install # native Kilo skill + /graphify command + AGENTS.md + .kilo plugin graphify kilo uninstall graphify cursor install # .cursor/rules/graphify.mdc (Cursor) graphify cursor uninstall graphify gemini install # GEMINI.md + BeforeTool hook (Gemini CLI) graphify gemini uninstall graphify copilot install # skill file (GitHub Copilot CLI) graphify copilot uninstall graphify aider install # AGENTS.md (Aider) graphify aider uninstall graphify claw install # AGENTS.md (OpenClaw) graphify claw uninstall graphify droid install # AGENTS.md (Factory Droid) graphify droid uninstall graphify trae install # AGENTS.md (Trae) graphify trae uninstall graphify trae-cn install # AGENTS.md (Trae CN) graphify trae-cn uninstall graphify hermes install # AGENTS.md + ~/.hermes/skills/ (Hermes) graphify hermes uninstall graphify amp install # skill file (Amp) graphify amp uninstall graphify agents install # ~/.agents/skills/ + AGENTS.md (cross-framework; alias: graphify skills) graphify agents uninstall graphify kiro install # .kiro/skills/ + .kiro/steering/graphify.md (Kiro IDE/CLI) graphify kiro uninstall graphify pi install # skill file (Pi coding agent) graphify pi uninstall graphify devin install # skill file + .windsurf/rules/graphify.md (Devin CLI) graphify devin uninstall graphify antigravity install # .agents/rules + .agents/workflows (Google Antigravity) graphify antigravity uninstall graphify extract ./docs # headless LLM extraction for CI (no IDE needed) graphify extract ./docs --backend gemini # explicit backend: gemini, kimi, claude, openai, deepseek, ollama, bedrock, or claude-cli 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 OPENAI_BASE_URL=http://localhost:8080/v1 OPENAI_MODEL=my-model graphify extract ./docs --backend openai # any OpenAI-compatible server (llama.cpp, vLLM, LM Studio) ANTHROPIC_BASE_URL=http://localhost:4000 ANTHROPIC_MODEL=my-model graphify extract ./docs --backend claude # any Anthropic-compatible endpoint (LiteLLM proxy, gateways) GRAPHIFY_OLLAMA_NUM_CTX=32768 graphify extract ./docs --backend ollama # override KV-cache window (auto-sized by default) GRAPHIFY_OLLAMA_KEEP_ALIVE=0 graphify extract ./docs --backend ollama # unload model after each chunk (saves VRAM on small GPUs) graphify extract ./docs --backend bedrock # AWS Bedrock via IAM - no API key, uses AWS credential chain graphify extract ./docs --backend claude-cli # route through Claude Code CLI - no API key, uses your Claude subscription graphify extract ./docs --backend azure # Azure OpenAI (set AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT) graphify extract ./docs --max-workers 16 # AST parallelism (also GRAPHIFY_MAX_WORKERS) graphify extract --postgres "postgresql://user:pass@host/db" # introspect live PostgreSQL schema directly graphify extract ./my-workspace --cargo # introspect Rust Cargo workspace dependencies directly 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 --mode deep # richer semantic extraction via extended system prompt graphify extract ./docs --no-cluster # raw extraction only, skip clustering graphify extract ./docs --timing # print per-stage wall-clock timings to stderr (also works on cluster-only) graphify extract ./docs --force # overwrite graph.json even if new graph has fewer nodes (use after refactors or to clear ghost duplicates) 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 --as myrepo # register a project graph into ~/.graphify/global-graph.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 prs # PR dashboard: CI, review, worktree, graph impact graphify prs 42 # deep dive on PR #42 graphify prs --triage # AI triage ranking (auto-detects backend from env) graphify prs --worktrees # worktree → branch → PR mapping graphify prs --conflicts # PRs sharing graph communities (merge-order risk) graphify prs --base main # filter to PRs targeting a specific base branch graphify prs --repo owner/repo # run against a different GitHub repo GRAPHIFY_TRIAGE_BACKEND=kimi graphify prs --triage # use a specific backend for triage graphify clone https://github.com/karpathy/nanoGPT graphify merge-graphs a.json b.json --out merged.json graphify --version # print installed version graphify watch ./src graphify check-update ./src graphify update ./src graphify update ./src --no-cluster # skip reclustering, write raw AST graph only graphify update ./src --force # overwrite even if new graph has fewer nodes graphify cluster-only ./my-project graphify cluster-only ./my-project --graph path/to/graph.json # custom graph location graphify cluster-only ./my-project --max-concurrency 16 --batch-size 200 # parallel community labeling (large graphs) graphify cluster-only ./my-project --resolution 1.5 # more, smaller communities graphify cluster-only ./my-project --exclude-hubs 99 # exclude p99 degree nodes from partitioning graphify cluster-only ./my-project --no-label # keep "Community N" placeholders graphify cluster-only ./my-project --backend=gemini # backend for community naming graphify cluster-only ./my-project --backend=gemini --model gemini-2.5-pro # specific model graphify label ./my-project # (re)name communities with the configured backend graphify label ./my-project --backend=openai --model gpt-4o # force a specific backend and model

Community names:inside an agent (Claude Code, Gemini CLI) the agent names communities itself. When you run the bare CLI,`cluster-only`auto-names them with the configured backend (built-in or custom OpenAI-compatible provider) — pass`--no-label`to keep`Community N`, or run`graphify label`to (re)generate names on demand.

- 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

**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

The project uses uv for dev workflow. Install it once, then:

git clone https://github.com/safishamsi/graphify.git cd graphify git checkout v8 # active development branch

Create the project venv and install graphify + all extras + the dev group

(pytest). uv installs the dev dependency group by default; pass --no-dev to

skip it.

uv sync --all-extras

Verify the editable install:

uv run graphify --version uv run python -c "import graphify; print(graphify.file)"

uv run pytest tests/ -q # run the full suite uv run pytest tests/test_extract.py -q # one module uv run pytest tests/ -q -k "python" # filter by name ``` macOS note: the test suite includes both

sample.f90andsample.F90fixtures. These collide on case-insensitive HFS+ / APFS file systems. Run on Linux or in a Docker container if you need to test both Fortran variants simultaneously.

• Active development happens on the v8branch.
• Commit style: fix: <description>/feat: <description>/docs: <description>
• Before opening a PR, run uv run pytest tests/ -qand confirm it passes.
• Add a fixture file to tests/fixtures/and tests totests/test_languages.pyfor any new language extractor.

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.