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
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
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
- readme: https://github.com/Graphify-Labs/graphify · fetched 2026-08-28 · 0c8a5324898f
- homepage: https://www.graphify.com · fetched 2026-08-28 · 8b7e5401471f
- site_page: https://graphify.com/docs · fetched 2026-08-28 · b1e1d2630e5a
- site_page: https://graphify.com/docs/install · fetched 2026-08-28 · 99eb90840d12
- site_page: https://graphify.com/docs/tutorial · fetched 2026-08-28 · db6ee24b09fc
- site_page: https://graphify.com/pricing · fetched 2026-08-28 · 6edb6fbe2512
- site_page: https://graphify.com/changelog · fetched 2026-08-28 · 28d2258475d3
- site_page: https://graphify.com/security · fetched 2026-08-28 · c916d071a490
- site_page: https://graphify.com/faq · fetched 2026-08-28 · e41545d988e6
- site_page: https://graphify.com/integrations · fetched 2026-08-28 · 80837b15a601
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Graphify-Labs/graphify | main | 77 |
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