First stable release.
What's in v1
- Knowledge graph from any mix of code, papers, docs, and images
- Leiden community detection with cohesion scores
- God nodes and surprising connections ranked by composite score
- Honest audit trail: every edge tagged EXTRACTED, INFERRED, or AMBIGUOUS
- Interactive HTML visualization
- Obsidian vault export with graph.canvas
- Wiki export (--wiki): Wikipedia-style articles per community for agent navigation
- GraphRAG-ready graph.json persistent across sessions
- SHA256 cache: re-runs only process changed files
- --watch: auto-rebuilds graph on code changes without LLM
- git commit hook: graphify hook install for per-commit rebuilds
- MCP stdio server (--mcp)
- Neo4j export (--neo4j)
- SVG and GraphML export
Install
pip install graphifyy && graphify install
Then in Claude Code: /graphify .
Worked examples
Reproducible benchmarks with input files and real output in worked/:
- worked/httpx/ - 6-file Python library
- worked/mixed-corpus/ - code + paper
- worked/karpathy-repos/ - 52-file corpus, 71.5x token reduction
v2 plans
- Hypergraph support (hyper-edges connecting more than 2 nodes)
- Poincare ball embeddings for hierarchical corpora
- Deeper agentic workflow integration