#1313th of 31 in Skills, plugins and rules
graphify
Turns a codebase and docs into a queryable knowledge graph
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- Stars
- 125.2k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
Overview
Graphify installs as a skill in AI coding assistants such as Claude Code, Cursor, Codex and Gemini CLI. Running /graphify on a project builds a graph from code, docs, PDFs, images and video, and writes graph.html, GRAPH_REPORT.md and graph.json. Code is parsed locally with tree-sitter, and the CLI can query the graph, trace paths between nodes, or explain a concept.
Who it is for: Developers using AI coding assistants on large or unfamiliar codebases
Strengths
- Code parsing uses tree-sitter locally with no LLM calls
- Each edge is tagged EXTRACTED or INFERRED
- Installs into 20+ assistants via a single graphify install command
- Graph is plain graph.json plus an HTML viewer, no vector store
Weaknesses
- Docs, PDFs, images and video need an LLM backend for the semantic pass
- Many features (PDF, video, MCP, Neo4j) require separate pip extras
- Graph is built on demand; always-on updating is in the hosted product
- Benchmark sample sizes are small (n=6 to n=300), results are self-reported
What it needs
- no GPU
- Docker
- Needs Python 3.10+, uv or pipx
- Models: Anthropic Claude, OpenAI and compatible APIs, Google Gemini, AWS Bedrock, Azure OpenAI
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