---
title: "Graphify: Queryable Knowledge Graph from Codebases"
url: https://stacklist.com/card/07292ccc-8198-443c-825b-6bcb6950f01c
source_url: "https://github.com/Graphify-Labs/graphify"
stack: https://stacklist.com/stack/539051c9-e760-4bd5-a86b-ac120e4ae368
summary: "Graphify is a graph visualization tool that maps entire projects (code, docs, PDFs, images, videos) into queryable knowledge graphs using tree-sitter AST parsing for code and semantic analysis for other media. It operates locally without LLMs for code analysis and provides interactive graph exploration with community detection, cross-file linking, and query capabilities."
tags: "graph-visualization, code-analysis, knowledge-graph, ast-parsing, developer-tools, local-first"
key_entities: "Graphify (technology), tree-sitter (technology), FastAPI (technology), Claude Code (technology), Cursor (technology), GitHub Copilot (technology), knowledge-graph (concept), AST-parsing (concept), community-detection (concept), graphify.com (organization)"
classification: "reference"
content_hash: "sha256:5761a2247e9450c0e07837e780fe57c5d79287e8eba7fb10abd7b23e0bd445e0"
acp_version: "0.2"
token_counts_approximate: 11856
visibility: public
agent_accessible: true
status: "final"
---

# Graphify: Queryable Knowledge Graph from Codebases

Read this in other languages 🇺🇸 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 | 🇮🇱 עברית 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 . You can join the waitlist there. 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): 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, GitHub 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: $ 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 "<question>" 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): brew install python@3.12 uv Windows quick install: winget install astral - sh.uv Ubuntu/Debian: 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: # 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 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 : 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 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 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 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 $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]" 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 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 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 "<question>" 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 (36 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 .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] ; .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] ) 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](./other.md) 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 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. 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 . --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. 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: 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. 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 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://<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] " 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, 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_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_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 <newer>, package is <older> line — that means a different install was loaded, not just a stale skill. Check which copy actually loaded: 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: 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: 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 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 : # .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 <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 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 ./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/<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 --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. 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. Contributing Development setup 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__) " Running tests 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.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. Git workflow Active development happens on the v8 branch. Commit style: fix: <description> / feat: <description> / docs: <description> 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. What to contribute 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. Community and links