Graphify

Overview

Graphify is an open-source (MIT) skill that maps any folder — code, docs, PDFs, images, videos, SQL schemas, scripts — into a queryable knowledge graph you traverse instead of grepping files. You type /graphify . in an AI coding assistant (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, 15+ hosts) and it builds the graph locally. It is the most-starred project in the vault’s ecosystem (85.8k⭐ on GitHub; the site reports 100 repos graphified with 854k nodes / 1.9M edges).

Key Facts

• License: MIT (free for personal + commercial)
• Install: pip install graphifyy (note: two y’s) or uv tool install graphifyy; then graphify install registers the skill
• Stack: 100% Python; tree-sitter AST parsing (deterministic, no LLM) for code; NetworkX for the graph; Leiden community detection (no embeddings, no vector store)
• Code parsing: 36 tree-sitter grammars covering Python, TypeScript, JavaScript, Go, Rust, Java, C/C++, CUDA, Metal, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Elixir, shell, JSON, Scala, and many more
• Output: three files — graph.html (interactive, clickable), GRAPH_REPORT.md (highlights + surprising connections + suggested questions), graph.json (full graph, queryable)
• Graph features: god nodes (most-connected concepts), communities (Leiden clustering), cross-file links (calls/imports/inherits resolved), query/path/explain capabilities, rationale + doc refs as first-class nodes
  • graphify explain <x> — explain a node, listing all edges with EXTRACTED/INFERRED tags
  • graphify path A B — shortest path between two nodes
  • graphify query "<question>" — scoped subgraph for a plain-language question
• Edge transparency: every edge tagged EXTRACTED (explicit in source) or INFERRED (resolved by graphify) — you can tell read-directly from inferred
• MCP server: python -m graphify.serve graph.json — stdio by default, optional Streamable HTTP transport for team sharing with --transport http, API key support, and per-session state management
• Local-first / privacy: code parsed on-device, nothing leaves the machine; only the semantic pass over docs/media calls a backend, and only if you configure one. Supports multiple backends: OpenAI, Anthropic, Gemini, DeepSeek, Bedrock, Azure, Ollama
• Benchmarks: evaluated as long-term memory (LOCOMO recall@10: 0.497, QA accuracy: 45.3% vs supermemory 49.7%/mem0 27.3%) and as code-intelligence layer
• Always-on integration: strict mode (graphify claude install --strict) blocks first raw source read and redirects to graph; soft nudge mode (graphify install) fires once per session
• Automation: graphify hook install embeds interpreter path and sets up git-aware merge driver so graph.json never has conflict markers

Relationships

• “belongs-to” knowledge-graph — Graphify is a production knowledge-graph builder; closest in spirit to cognee and swarmvault for the explicit-graph layer
• “operates-with” llm-wiki — it can run over a wiki/ folder, but builds ITS OWN graph from the files; it does not read OKF frontmatter or wikilinks as native source
• “operates-with” model-context-protocol — exposes an MCP server (stdio + optional HTTP)
• “sibling-of” cognee — sibling explicit-graph option; Cognee is memory-engine (Neo4j/Postgres + 14-tool MCP), Graphify is codebase→graph skill with edge transparency
• “sibling-of” rightmemory — sibling typed-edge approach for coding agents
• “belongs-to” agent-memory-systems — appears in benchmarks as a long-term-memory layer
• “sibling-of” wikilinks — Graphify’s EXTRACTED/INFERRED edge tags mirror this vault’s rule that inferred links must be visually distinct from manual ones

Implications

Graphify is the strongest explicit-graph / navigation option for the vault’s Fase C (gap #3 explicit graph, gap #1 navigation at scale). Two caveats for OUR vault:

1. Format: it builds its own graph.json from raw files — it does not import our OKF frontmatter or wikilinks. So it would be a parallel graph layer, not an extension of the OKF artifact. (Same trade-off we noted for cognee and nashsu-llm-wiki.)
2. Best fit: over a CODEBASE or a large wiki/ corpus (100+ pages) where index.md grep breaks. For our current 75-page vault, wikilinks still suffice (per connection-methods and the comprehensive report’s “no graph before index.md breaks” rule).

The EXTRACTED/INFERRED distinction is exactly the discipline this vault already enforces for inferred connections — Graphify makes that visible at the edge level, which is why it is worth ingesting despite the format mismatch.

External Links

• Repository: https://github.com/Graphify-Labs/graphify
• Site: https://graphify.net/
• MCP servers directory: https://graphify.net/mcpservers/
• Graphify vs alternatives: https://graphify.net/graphify-vs-alternatives.html
• Claude Code integration: https://graphify.net/graphify-claude-code-integration.html

Sources

^[https://github.com/Graphify-Labs/graphify] ^[https://graphify.net/]