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Graphify-Labs/graphify

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store. observed · 2026-08-28

github.com/Graphify-Labs/graphify · homepage · Python · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

77/100

  • Activity 99
  • Release rhythm 87
  • Longevity 10

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0
  • age_days: 152
  • days_rel: 8
  • days_push: 8
  • n_releases_24m: 196

Full methodology

Adoption not part of the score

111080 stars · 10801 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Graphify is an open-source (Apache 2.0) Python CLI and /graphify skill that turns a codebase—including docs, SQL schemas, configs, and PDFs—into a queryable knowledge graph using deterministic local tree-sitter AST parsing, with no vector store or embeddings. It integrates with AI coding assistants like Claude Code, Cursor, Codex, and Gemini CLI, and exposes the graph via a CLI and an MCP server, returning answers as explicit paths with file:line citations.

Use cases

  • map my codebase into a knowledge graph for my AI assistant
  • query who owns billing without grepping through files
  • understand how auth connects to the database in a large repo
  • generate an architecture report and interactive graph of a repository
  • give Claude Code or Cursor grounded code navigation with citations
  • index docs, SQL schemas, and Terraform alongside source code
  • run codebase retrieval fully on-device with no telemetry

When to choose

  • you want deterministic, auditable code retrieval with file:line citations instead of fuzzy vector search
  • you use an AI coding assistant (Claude Code, Cursor, Codex, Gemini CLI, etc.) and want it to traverse real call/import edges
  • you need on-device parsing with no code leaving your machine and no telemetry
  • you want to index non-code assets like Markdown, PDFs, SQL schemas, and Terraform into one graph
  • you need graph export to Neo4j, FalkorDB, Obsidian, or GraphML

When to avoid

  • you need semantic embedding-based retrieval over unstructured natural-language corpora rather than code structure
  • you want a hosted SaaS with team features out of the box (the enterprise layer is early access)
  • your project is in a language outside the 36 supported tree-sitter grammars
  • you need real-time incremental indexing of very rapidly changing code at large scale

Facets

cli-tool · maturity active

parser search-engine rag mcp developer-tools cli code-review developer-tools large-language-models apis python cli cross-platform windows self-hosted knowledge-graph graphrag tree-sitter ast-parsing code-analysis code-search claude-code cursor codex gemini-cli mcp-server on-device no-vector-store leiden-clustering ai-coding-assistants ai-agents retrieval-augmented-generation command-line macos linux

10 sources

Member repositories

RepositoryRoleHealth v2
Graphify-Labs/graphifymain77

For agents

markdown · JSON · MCP: product_card(name="Graphify-Labs/graphify")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem