{"data":{"slug":"graphify-labs-graphify","name":"graphify","tagline":"Turn any code or documentation into a queryable knowledge graph","github_url":"https://github.com/Graphify-Labs/graphify","owner":"Graphify-Labs","repo":"graphify","owner_avatar_url":"https://avatars.githubusercontent.com/u/297659074?v=4","primary_language":"Python","stars":107507,"forks":10441,"topics":["ai-agents","antigravity","ast","claude-code","code-analysis","code-search","codex","cursor","developer-tools","gemini","graphrag","knowledge-graph","leiden","llm","mcp","openclaw","rag","skills","tree-sitter"],"archived":false,"github_pushed_at":"2026-08-17T18:42:58+00:00","maintenance_label":"Very active","stars_delta_30d":16918,"url":"https://www.graphcanon.com/tools/graphify-labs-graphify","markdown_url":"https://www.graphcanon.com/tools/graphify-labs-graphify.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/graphify-labs-graphify","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=graphify-labs-graphify","description":"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.","homepage_url":"https://www.graphify.com","license":"MIT","open_issues":978,"watchers":360,"ai_summary":"Graphify is an AI coding assistant skill that transforms various types of files (code, SQL schemas, R scripts, shell scripts, docs, papers, images, videos) into a unified, searchable knowledge graph.","readme_excerpt":"# or install uv:\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n---\n\n---\n\n## Install\n\n> **Official package:** The PyPI package is `graphifyy` (double-y). Other `graphify*` packages on PyPI are not affiliated. The CLI command is still `graphify`.\n\n**Step 1 — install the package:**\n\n```bash\n\n---\n\n# Create the project venv and install graphify + all extras + the dev group","github_created_at":"2026-04-03T15:49:07+00:00","created_at":"2026-07-07T17:36:10.383725+00:00","updated_at":"2026-08-18T00:02:20.507185+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"}],"tags":[{"slug":"claude-code","name":"claude-code"},{"slug":"codex","name":"codex"},{"slug":"gemini","name":"gemini"},{"slug":"knowledge-graph","name":"knowledge-graph"},{"slug":"rag","name":"rag"},{"slug":"skills","name":"skills"}],"trust":{"provenance":{"is_fork":false,"github_id":1200597263,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T00:02:19.442Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":30,"days_since_push":0,"last_release_at":"2026-08-17T18:42:58Z","stars_delta_30d":16918,"open_issues_delta_30d":441},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:06:57.068Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T00:02:20.203Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-18T00:02:20.203Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-18T00:02:20.203Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-18T00:02:20.203Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-18T00:02:20.203Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T00:02:20.203Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.","Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"requires_docker":false},"when_to_use":["When you need to turn diverse file types (code, SQL schemas, documents, images) into a single queryable data structure that can be searched and analyzed together.","For projects where you require an extensive understanding of the interconnectivity between your application's code, database schema, and infrastructure documentation."],"when_not_to_use":["If the project exclusively involves text-based content without requiring integration or querying across different file types (e.g., plain documents with no need for cross-referencing).","Avoid using Graphify if you are looking for a tool that focuses solely on visual graph representation without the depth of semantic querying capabilities."],"source":"enrich:decision_facts","observed_at":"2026-07-11T11:55:32.163Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed."},{"label":"Adopt for","value":"Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types."}]}}