graphify

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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). Needs Python 3.10+ and uv (or pipx):

uv tool install graphifyy      # install the CLI (or: pipx install graphifyy)
graphify install               # detects your assistant and registers the skill

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.

One install, any assistant, any OS. Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 20+ more, on macOS, Linux, and Windows: pick your platform.

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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, ending with the path's overall confidence: that of its weakest hop.

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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 →

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Benchmarks: 0.497 recall@10, 0 LLM credits

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.

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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

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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

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Install

Important