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

Cut agent token spend by 80%. Graphify Cloud is always-on memory that governs your agents, keeps code quality high, and proves correctness with formal verification. Start a free 14-day trial →

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.

Note

Want this always-on? Graphify Cloud keeps the graph live across your entire SDLC: monorepo and cross-repo support, formal verification, and code review with a bird's-eye view of your SDLC, plus connectors for Sentry, Jira, and more so incidents and tickets sit in the same graph as your code. Start a free 14-day trial 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):

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

The persisted graph includes graph.schema_version so integrations can detect incompatible format changes, plus graph.graphify_version identifying the Graphify release that produced it.

Works in Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 15+ more — pick your platform.

---

Documentation

Full guides and reference live at docs.graphify.com:

• Quickstart — build your first graph
• CLI reference — every command and flag
• Ask better graph questions — querying patterns
• Configuration — environment variables and tuning
• Supported inputs — languages and file types
• Team workflows and PR review
• Troubleshooting and How it works

---

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

Tip

Running this across many repos or a monorepo? Graphify Cloud does it continuously and cuts agent token spend by 80%, with cross-repo links, SDLC-wide code review, formal verification, and Sentry/Jira connectors. Start a free 14-day trial →

---

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
ERPNext cross-tool (n=6)	key-fact coverage	82.0%	grep/read baseline 70.8%
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

Important

Official package: The PyPI package is graphifyy (double-y). Other graphify* packages on PyPI are not affiliated. The CLI command is still graphify.

The official source repository is Graphify-Labs/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.

Tip

Hitting command not found, a pip-on-Mac/Windows issue, PowerShell quoting, uvx usage, git-hook PATH quirks, or strict mode? See Installation and Troubleshooting.

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 (graspologic on Python < 3.13; native backend on 3.13+)	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]"
robot	Robot Framework .robot/.resource extraction (suites, test cases, keywords, keyword-call and resource/library import edges)	uv tool install "graphifyy[robot]"
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:

Run graphify <platform> install once in your project, for example graphify claude install or graphify codex install (or graphify install --platform <name>).

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

GRAPH_REPORT.md summarizes the god nodes, communities, and key paths for broad architecture review. How the graph is built and what it contains: How graphify works.

---

What files it handles

graphify parses ~40 programming languages locally with tree-sitter AST, and maps docs, PDFs, images, and audio/video through an optional semantic pass. Full list: Supported inputs.

---

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 commit + branch checkout (run `graphify update .` after `git pull` — see "Recommended workflow" below)
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

graphify honors .gitignore and supports --exclude patterns and a project .graphifyignore. Details: Configuration.

---

Team setup

Commit or share the graph so your whole team queries the same context, and review pull requests with graph context. See Share context with your team and Review pull requests.

---

Using the graph directly

Beyond your AI assistant, query graph.json straight from the CLI with graphify query, graphify path, and graphify explain. See Ask better graph questions and the query reference.

---

Environment variables

Backends, API keys, hook behavior, and tuning are controlled by environment variables. Full table: Configuration.

---

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

Common install and extraction issues and their fixes: Troubleshooting.

---

Full command reference

Every command and flag, with examples: CLI reference.

---

Learn more

• docs.graphify.com — full documentation: guides, command reference, and integrations
• 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 Cloud

Graphify Cloud is the always-on layer built on top of graphify. Instead of a graph you rebuild on demand per folder, it keeps one live graph across your entire software development lifecycle:

• 80% fewer tokens — always-on memory means your agents stop re-reading and re-explaining the codebase every session.
• Monorepo and cross-repo — one connected graph across every service and repository, not a graph per folder.
• Formal verification — check architectural and dependency invariants against the live graph.
• Code review with a bird's-eye view of your SDLC — see how a change ripples across the whole system before you merge.
• SDLC connectors — pull in Sentry, Jira, and more, so incidents, tickets, and code live in one graph.
• Always-on — updates continuously in the background across code, docs, and meetings.

Start a free 14-day trial at app.graphify.com →

---

Contributing

Contributions are welcome. See CONTRIBUTING.md for the development setup, the test and CI-parity commands, the git workflow, and what makes a strong contribution (worked examples and extraction bug reports are the most useful). Architecture and how to add a language: ARCHITECTURE.md.

New here? Say hi on Discord or in GitHub Discussions.

---

Contributors

Made with contrib.rocks.

---

Translations

The README is available in 32 languages. Use the language switcher at the top of this file to read it in yours, or browse docs/translations/. To improve a translation or add a new one, open a pull request against the matching file there.

---

Building on graphify

Building something in the graphify ecosystem? That is encouraged. If your project uses "graphify" in its name (for example graphify-dashboard or graphify-action), please add a short note to your README clarifying that it is community-built and not affiliated with or endorsed by Graphify Labs. "graphify" and the graphify logo are marks of Graphify Labs; please do not use them in a way that implies official status.

---

Community and links