Graphify-Labs/graphify

What it does

• 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 / mixesin 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

Install

The two-command quickstart is at the top of this README. This section covers the install methods, project-scoped installs, and every supported platform.

Then run graphify install once. It detects which assistant you run and registers the skill, so there is no per-assistant setup. Open your AI assistant and type /graphify .

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

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.

• 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

uv tool install graphifyy   # recommended, isolated env (if 'graphify' isn't found after, run: uv tool update-shell)
pipx install graphifyy      # alternative
pip install graphifyy       # may need PATH setup, see Troubleshooting below

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.

Prerequisites

• 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

Project configuration

Create a .graphifyrc in the scan root for settings that belong to the project rather than to one run. One key=value per line; lines beginning with # are comments.

Ambiguous extensions. Some extensions mean different languages in different projects: .inc is PHP on pfSense, Pascal in a Delphi tree, SQL or assembly elsewhere; .h is C or C++; .m is Objective-C or MATLAB. graphify has to pick one global default, and when it picks wrong the file does not fail: it parses as the wrong language and yields a handful of incidental nodes, so the graph looks populated while the real symbols are missing. Declare what the extension means in your repo:

The declaration flows through classification, extractor selection, grammar and resolution, imports, graphify watch, and the AST cache (a file re-parsed under a different language never reuses the old entry), and the same .graphifyrc is read by extract, update, the hooks, the MCP server, and the /graphify skill. A typo is reported once on stderr and the scan continues with the defaults. How declarations interact with caching, incremental rebuilds, and import resolution: Configuration.

# .graphifyrc
# A language name:
language.inc=php
# Or an extension graphify already knows:
language.tpl=.ts
# Baked into the git hooks (see Team setup):
viz_node_limit=0

Troubleshooting

Common install and extraction issues and their fixes: Troubleshooting.

概述

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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.

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):

• 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
• 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.

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:

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.