企业知识图谱构建平台

Early access to the graphify platform is open before the public v1 launch: app.graphify.com

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. - **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.) - **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. - **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. > 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**.

The FastAPI codebase mapped by graphify. Every node is a concept, colors are detected communities, and the whole thing is clickable in graph.html.

**Get started** (30 seconds): ```bash 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, Copilot, and 15+ more — pick your platform. ---

See it in action

Once the graph is built you query it instead of reading files. Real output, graphify run on the FastAPI codebase shown above: ```text $ 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 ""` returns a scoped subgraph for a plain-language question, and `graphify path A B` traces how any two things connect. ---

What it does

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

---

Benchmarks

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**. ---

Prerequisites

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):** ```bash brew install python@3.12 uv ``` **Windows quick install:** ```powershell winget install astral-sh.uv ``` **Ubuntu/Debian:** ```bash sudo apt install python3.12 python3-pip pipx

or install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh ``` ---

Install

> **Official package:** The PyPI package is `graphifyy` (double-y). Other `graphify*` packages on PyPI are not affiliated. The CLI command is still `graphify`. **Step 1 — install the package:** ```bash

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:** ```bash graphify install ``` That'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`: ```bash 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:** Use `graphify .` not `/graphify .` — the leading slash is a path separator in PowerShell. > **`graphify: command not found`?** `uv tool install` / `pipx install` put the `graphify` command 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 your `PATH` yet: run `uv tool update-shell` (or `pipx ensurepath`), then open a new terminal. With plain `pip`, add `~/.local/bin` (Linux) or `~/Library/Python/3.x/bin` (Mac) to your PATH, or run `python -m graphify`. > **Running with `uvx` / `uv tool run` instead of installing?** Name the package, not the command: `uvx --from graphifyy graphify install`. Plain `uvx graphify …` fails (`No solution found … no versions of graphify`) because `uv tool run` reads the first word as a *package*, and the package is `graphifyy` — the `graphify` command lives inside it. > **Avoid `pip install` on Mac/Windows** if possible. The skill resolves Python at runtime from `graphify-out/.graphify_python`; if that points to a different environment than where `pip` installed the package, you'll get `ModuleNotFoundError: No module named 'graphify'`. `uv tool install` and `pipx install` isolate the package in their own env and avoid this entirely. > **Git hooks and uv tool / pipx:** `graphify hook install` embeds 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/bin` is not on PATH. If you reinstall or upgrade graphify, re-run `graphify hook install` to refresh the embedded path. > **Strict mode (Claude Code):** `graphify install --project --strict` makes the assistant actually use the graph. The default install *nudges* it to run `graphify query` before reading files; strict mode *blocks* the first raw source read of a session and redirects it to the graph, then reverts to the nudge (so it fires at most once per session and never gets stuck). Toggle at runtime with `GRAPHIFY_HOOK_STRICT=1`/`0`; the default install is unchanged (soft nudge).

Pick your platform (20+ assistants, click to expand)

Platform	Install command
Claude Code (Linux/Mac)	`graphify install`
Claude Code (Windows)	`graphify install` (auto-detected) or `graphify install --platform windows`
CodeBuddy	`graphify install --platform codebuddy`
Codex	`graphify install --platform codex`
OpenCode	`graphify install --platform opencode`
Kilo Code	`graphify install --platform kilo`
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`
Amp	`graphify amp install`
Agent Skills (cross-framework)	`graphify install --platform agents` (alias `--platform skills`)
Kiro IDE/CLI	`graphify kiro install`
Pi coding agent	`graphify install --platform pi`
Cursor	`graphify cursor install`
Devin CLI	`graphify devin install`
Google Antigravity	`graphify antigravity install`

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, so AGENTS.md is the always-on mechanism. `--platform agents` (alias `--platform skills`) targets the generic cross-framework 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 `$graphify` instead of `/graphify`.

Optional extras (install only what you need)

Extra	What it adds	Install
`pdf`	PDF extraction	`uv tool install "graphifyy[pdf]"`
`office`	`.docx` and `.xlsx` support	`uv tool install "graphifyy[office]"`
`google`	Google Sheets rendering	`uv tool install "graphifyy[google]"`
`video`	Video/audio transcription (faster-whisper + yt-dlp)	`uv tool install "graphifyy[video]"`
`mcp`	MCP stdio server	`uv tool install "graphifyy[mcp]"`
`neo4j`	Neo4j push support	`uv tool install "graphifyy[neo4j]"`
`falkordb`	FalkorDB push support	`uv tool install "graphifyy[falkordb]"`
`svg`	SVG graph export	`uv tool install "graphifyy[svg]"`
`leiden`	Leiden community detection (Python < 3.13 only)	`uv tool install "graphifyy[leiden]"`
`ollama`	Ollama local inference	`uv tool install "graphifyy[ollama]"`
`openai`	OpenAI / OpenAI-compatible APIs	`uv tool install "graphifyy[openai]"`
`gemini`	Google Gemini API	`uv tool install "graphifyy[gemini]"`
`anthropic`	Anthropic Claude API (`--backend claude`, uses `ANTHROPIC_API_KEY`)	`uv tool install "graphifyy[anthropic]"`
`bedrock`	AWS Bedrock (uses IAM, no API key)	`uv tool install "graphifyy[bedrock]"`
`azure`	Azure OpenAI Service (`--backend azure`, uses `AZURE_OPENAI_API_KEY` + `AZURE_OPENAI_ENDPOINT`)	`uv tool install "graphifyy[openai]"`
`sql`	SQL schema extraction	`uv tool install "graphifyy[sql]"`
`postgres`	Live PostgreSQL introspection (`--postgres DSN`)	`uv tool install "graphifyy[postgres]"`
`dm`	BYOND DreamMaker `.dm`/`.dme` AST extraction (may need a C compiler + `python3-dev` if no wheel matches your platform)	`uv tool install "graphifyy[dm]"`
`terraform`	Terraform / HCL `.tf`/`.tfvars`/`.hcl` AST extraction	`uv tool install "graphifyy[terraform]"`
`pascal`	Pascal / Delphi `.pas`/`.dpr`/`.dpk`/`.inc` AST extraction (more accurate `calls`/`inherits` edges; falls back to a regex extractor when absent)	`uv tool install "graphifyy[pascal]"`
`ocaml`	OCaml `.ml`/`.mli` AST extraction	`uv tool install "graphifyy[ocaml]"`
`commonlisp`	Common Lisp `.lisp`/`.cl`/`.lsp`/`.asd` AST extraction	`uv tool install "graphifyy[commonlisp]"`
`chinese`	Chinese query segmentation (jieba)	`uv tool install "graphifyy[chinese]"`
`all`	Everything above	`uv tool install "graphifyy[all]"`

---

Make your assistant always use the graph

Run this once in your project after building a graph:

Platform	Command
Claude Code	`graphify claude install`
CodeBuddy	`graphify codebuddy install`
Codex	`graphify codex install`
OpenCode	`graphify opencode install`
Kilo Code	`graphify kilo install`
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`
Amp	`graphify amp install`
Agent Skills (cross-framework)	`graphify agents install` (alias `graphify skills install`)
Kiro IDE/CLI	`graphify kiro install`
Pi coding agent	`graphify pi install`
Devin CLI	`graphify devin install`
Google Antigravity	`graphify antigravity install`

This writes a small config file that tells your assistant to consult the knowledge graph for codebase questions, preferring scoped queries like `graphify query ""` over reading the full report or grepping raw files. - **Hook platforms** (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. - **Instruction-file platforms** (Codex, OpenCode, Cursor, etc.): 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`, which is what actually carries the always-on graph guidance on this platform. `graphify codex install` also registers a `PreToolUse` hook in `.codex/hooks.json` (`graphify hook-check`), but that entry is deliberately a **no-op**: Codex Desktop rejects `hookSpecificOutput.additionalContext` on `PreToolUse`, so emitting a nudge there would break Bash tool calls. Unlike Claude Code, where the hook (`graphify hook-guard`) does the nudging, on Codex the hook fires and intentionally does nothing, and `AGENTS.md` is the always-on mechanism. **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`, so Cursor includes it in every conversation automatically, no hook needed. 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 (37 tree-sitter grammars)	`.py .ts .mts .cts .js .jsx .tsx .mjs .go .rs .java .c .cpp .cc .cxx .h .hpp .cu .cuh .metal .rb .cs .kt .kts .scala .php .swift .lua .luau .toc .zig .ps1 .psm1 .psd1 .ex .exs .m .mm .ml .mli .jl .vue .svelte .astro .groovy .gradle .dart .v .sv .svh .sql .f .f90 .f95 .f03 .f08 .pas .pp .dpr .dpk .lpr .inc .dfm .lfm .lpk .sh .bash .json .dm .dme .dmi .dmm .dmf .sln .slnx .csproj .fsproj .vbproj .xaml .razor .cshtml` (`.dm`/`.dme` requires `uv tool install graphifyy[dm]`, `.ml`/`.mli` requires `uv tool install graphifyy[ocaml]`; `.mts`/`.cts` reuse the TypeScript grammar, `.cc`/`.cxx` and CUDA `.cu`/`.cuh` and Metal `.metal` reuse the C++ grammar)
Salesforce Apex	`.cls .trigger` (regex-based; classes, interfaces, enums, methods, triggers, SOQL/DML edges)
Terraform / HCL	`.tf .tfvars .hcl` (requires `uv tool install graphifyy[terraform]`)
OCaml	`.ml .mli` (requires `uv tool install graphifyy[ocaml]`)
Common Lisp	`.lisp .cl .lsp .asd` (requires `uv tool install graphifyy[commonlisp]`)
MCP configs	`.mcp.json` `mcp.json` `mcp_servers.json` `claude_desktop_config.json` — extracts server nodes, package refs, env var requirements
Package manifests	`apm.yml` `pyproject.toml` `go.mod` `pom.xml` — one canonical package node per package (by name) plus `depends_on` edges, so a package referenced from many manifests is a single hub
Docs	`.md .mdx .qmd .html .txt .rst .yaml .yml` (markdown `text` links and `[[wikilinks]]` become `references` edges between docs)
Office	`.docx .xlsx` (requires `uv tool install graphifyy[office]`)
Google Workspace	`.gdoc .gsheet .gslides` (opt-in; requires `gws` auth and `--google-workspace`; Sheets need `uv tool install graphifyy[google]`)
PDFs	`.pdf`
Images	`.png .jpg .webp .gif`
Video / Audio	`.mp4 .mov .mp3 .wav` and more (requires `uv tool install graphifyy[video]`)
YouTube / URLs	any video URL (requires `uv tool 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 , then run: ```bash 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. ---

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 . --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 # 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. ---

Ignoring files

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. Pass `--no-gitignore` to `graphify extract` when git-ignored generated or transpiled code belongs in the graph. This disables `.gitignore` and `.git/info/exclude`; `.graphifyignore` still applies. ```

.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/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:** 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 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`.

Shared HTTP server

`--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://: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 ` (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: ```bash 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: > ```bash > python3 -m venv .venv && .venv/bin/pip install "graphifyy[mcp]" > ``` ---

Environment variables

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, Anthropic SDK, and Bedrock 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_ENABLE`	Set to `1` to turn on the local query log at `~/.cache/graphify-queries.log` (records each query/path/explain question + corpus path). Off by default — nothing is written unless you opt in (#1797)	optional
`GRAPHIFY_QUERY_LOG`	Enable the query log and write it to this path instead of the default	optional — off unless this or `_ENABLE` is set
`GRAPHIFY_QUERY_LOG_DISABLE`	Set to `1` to force the query log off (wins over the enable vars)	optional
`GRAPHIFY_QUERY_LOG_RESPONSES`	When the log is enabled, also record 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_MAX_CONTEXTS`	Maximum number of non-default project graphs retained by one multi-project MCP server	optional — default: `8`; invalid values use `8`, and values below `1` use `1`
`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

---

Privacy

- **Code files** — processed locally via tree-sitter. Nothing leaves your machine. A code-only corpus requires no API key — `graphify extract` runs fully offline. On a mixed repo, add `--code-only` to index just the code and skip the docs/PDFs/images that would otherwise need an LLM. - **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. ---

Troubleshooting

**`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. **`uv run --with graphifyy python -m graphify` silently runs an older install** `uv run` uses your *system* Python, so if an older `graphifyy` also lives there (e.g. a past `pip install graphifyy`), Python can find that copy first on `sys.path` and `--with graphifyy` won't override it. It runs with no error, but you get the *old* version's behavior — e.g. env overrides like `OPENAI_BASE_URL` are silently ignored, so requests hit the default endpoint and fail with a 401 that looks like a bad key. The fingerprint is a `warning: skill is from graphify , package is ` line — that means a different install was loaded, not just a stale skill. Check which copy actually loaded: ```bash python -c "import graphify; print(graphify.__file__)" ``` Then run the installed command directly (it uses the uv-managed copy), or drop the stale system copy: ```bash uvx --from graphifyy graphify extract . --backend openai # names the package explicitly pip uninstall graphifyy # or remove the old system install ``` **`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. **`extract` exits with "extraction was incomplete ... refusing to overwrite"** When an extraction pass crashes or a walk can't fully read the corpus, the run would be smaller than a complete one, so `graphify extract` refuses to overwrite a larger existing graph with the partial result (protecting your `graph.json`). Fix the underlying failure and re-run, or pass `--allow-partial` to overwrite anyway. **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: ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash uv tool upgrade graphifyy graphify install # overwrites the skill file ``` **Claude Code prompt cache invalidated after every `graphify extract`** Graphify writes output files (`graph.json`, `graphify-out/`) into the workspace. If those paths aren't ignored, every write invalidates Claude Code's prompt cache, forcing a full re-upload at cache-write rates on the next turn. Add them to `.claudeignore`: ```text

.claudeignore

graph.json graphify-out/ ``` ---

Full command reference

``` /graphify # run on current directory /graphify ./raw # run on a specific folder /graphify ./raw --mode deep # more aggressive relationship extraction graphify extract ./raw --code-only # index code only — local AST, no API key (skips docs/PDFs/images); an `extract` flag, not a skill flag /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" -- "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 ( 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 ./src --no-gitignore # include git-ignored source; still honor .graphifyignore 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 repo GRAPHIFY_TRIAGE_BACKEND=kimi graphify prs --triage # use a specific backend for triage graphify clone 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. ---

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 - The Memory Layer — the book on the ideas behind graphify, the architecture end to end ---

graphify Enterprise

**graphify Enterprise** is the always-on layer built on top of graphify — it applies the same graph approach to your entire working context: meetings, files, docs, and code, updating continuously in the background. Built for people and teams whose work lives across hundreds of conversations and documents they can never fully reconstruct. **Join the waitlist at graphify.com.** Free trial launching soon. ---

Development setup

The project uses uv for dev workflow. Install it once, then: ```bash git clone 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: ```bash uv run graphify --version uv run python -c "import graphify; print(graphify.__file__)" ```

Running tests

```bash 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 ```

CI parity checks

The authoritative CI commands live in `./workflows/`. For local CI-style verification, use Python 3.10 or 3.12 and run: ```bash uv sync --all-extras --frozen uv run --frozen pytest tests/ -q --tb=short uv run --frozen python -m tools.skillgen --check uv run --frozen python -m tools.skillgen --audit-coverage uv run --frozen python -m tools.skillgen --schema-singleton uv run --frozen python -m tools.skillgen --monolith-roundtrip uv run --frozen python -m tools.skillgen --always-on-roundtrip uv run --frozen graphify --help uv run --frozen graphify install ``` Ruff is useful as an additional local check (`uv run --frozen ruff check .`), but is not currently a blocking CI job. Pyright is also local/advisory unless it is added to CI later. The Bandit and pip-audit CI steps currently use `continue-on-error`, so their findings are advisory rather than blocking. > macOS note: the test suite includes both `sample.f90` and `sample.F90` fixtures. 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. > Windows note: the native Windows test suite exercises symbolic links, long > paths, POSIX permissions, path separators, and UTF-8 filesystem behavior. > Enable Windows Developer Mode to allow unprivileged symbolic-link creation, or > run the tests from an elevated shell. Enable the Windows `LongPathsEnabled` > policy before relying on long-path tests. Restart affected shells or applications > after changing either setting. For exact parity with the blocking Actions > test matrix, run the suite in WSL or Linux; CI currently runs on Ubuntu with > Python 3.10 and 3.12. Pyright is available as a local advisory check, but it is > not currently a blocking CI job.

Git workflow

- Active development happens on the `v8` branch. - Commit style: `fix: ` / `feat: ` / `docs: ` - Before opening a PR, run `uv run pytest tests/ -q` and confirm it passes. - Add a fixture file to `tests/fixtures/` and tests to `tests/test_languages.py` for any new language extractor.