[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fu7wxsdiqjcaj":3},{"code":4,"message":5,"data":6,"timestamp":69},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":40,"summary_md":41,"topic":42,"github_topics":45,"license":65,"homepage":66,"updated_at":67,"history":68},"Graphify-Labs\u002Fgraphify",4,"Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A \u002Fgraphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.","Python",134308,12829,6875,204,[16,22,27,31,35],{"id":17,"title":18,"points":19,"comments":20,"posted_at":21},49217306,"Graphify – Less token use for Claude Code",3,0,"2026-08-07",{"id":23,"title":24,"points":25,"comments":20,"posted_at":26},49195383,"Graphify – Turn any codebase into a queryable knowledge graph",2,"2026-08-06",{"id":28,"title":29,"points":25,"comments":20,"posted_at":30},49094378,"Graphify: Codebase Knowledge Graph","2026-07-29",{"id":32,"title":33,"points":25,"comments":20,"posted_at":34},48903692,"Show HN: Turn any folder of code, SQL schemas into a queryable knowledge graph","2026-07-14",{"id":36,"title":37,"points":38,"comments":20,"posted_at":39},48794162,"Graphify: Turn any codebase into a queryable knowledge graph",1,"2026-07-05","Builds a local, queryable knowledge graph of a codebase so coding agents can navigate it instead of grepping.","## The problem\n\nCoding agents burn context by grepping and reading files until they stumble onto the right answer. On a large repo this is slow, expensive, and unreliable, because the agent has no model of how modules, types and documents relate. Graphify parses a project into a graph and lets the agent query that graph instead of re-reading source. The persisted graph.json becomes a reusable artifact, so repeated questions don't re-scan the tree.\n\n## What's different\n\nThe usual response to this problem is a vector index: embed chunks, retrieve top-k. Graphify explicitly rejects that. Code is parsed with tree-sitter AST deterministically, with no LLM and no embeddings, and the output is a real graph you traverse. Two consequences set it apart. First, every edge is tagged `EXTRACTED` or `INFERRED`, so you can distinguish what was read from source from what was derived by resolution. Second, queries are structural (`explain`, `path A B`, scoped subgraphs) rather than similarity search, which suits call-chain and dependency questions that embeddings handle poorly. It also folds docs, PDFs, images and ADRs into the same graph, with a semantic pass that does use a model.\n\n## Who it's for\n\nDevelopers using Claude Code, Cursor, Codex, Gemini CLI or similar assistants on medium-to-large codebases, especially ones with heavy cross-file coupling or a lot of design documentation. It is a good fit for teams worried about sending source to a hosted index. Anyone who mainly wants semantic search over prose, or who works in a small repo where grep is already fast, will not get much from it. Engineers wanting a pure RAG stack should look at vector-store tooling instead.\n\n## Cost of adoption\n\nRequires Python 3.10+ and uv or pipx; installation is `uv tool install graphifyy` followed by `graphify install` to register the skill with your assistant. The code pass is local, but the docs\u002Fmedia pass calls your assistant's model or a configured API key, so privacy guarantees only fully hold for code. The output format carries a schema version, which is a maturity signal but also means integrations can break across releases. Apache-2.0. The README is heavily oriented around the commercial Graphify Cloud upsell, so watch for feature gating between the OSS CLI and the paid product.\n\n## Verdict\n\nGraphify is young — created in April 2026 — yet already shows 134k stars and +6,875 in a single week, which looks more like a spike than a settled adoption curve. The core idea, deterministic AST graph plus explained edges, is genuinely different from embedding-based retrieval, and the zero-LLM code pass is a real cost and privacy advantage. The risk is two-fold: a star count that outruns a 203-day-old codebase, and a commercial roadmap that could pull capability behind the cloud product. Worth a trial on a large repo you already know well, so you can judge graph quality against your own understanding before depending on it.",{"slug":43,"name":44},"developer-tools","Developer Tools",[46,47,48,49,50,51,52,53,43,54,55,56,57,58,59,60,61,62,63,64],"ai-agents","antigravity","ast","claude-code","code-analysis","code-search","codex","cursor","gemini","graphify","graphrag","knowledge-graph","leiden","llm","mcp","openclaw","rag","skills","tree-sitter","Apache-2.0","https:\u002F\u002Fwww.graphify.com","2026-10-24T15:35:37Z",[],1792868380]