{"data":{"slug":"infiniflow-ragflow","name":"ragflow","tagline":"Retrieval-Augmented Generation (RAG) engine with agent capabilities","github_url":"https://github.com/infiniflow/ragflow","owner":"infiniflow","repo":"ragflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/69962740?v=4","primary_language":"Go","stars":90923,"forks":10767,"topics":["agent-harness","agentic-ai","agentic-nagive","agentic-retrieval","agentic-search","ai","ai-agents","context-engine","context-engineering","context-management","harness-engineering","knowledge-compilation","rag","retrieval-augmented-generation","search-harness"],"archived":false,"github_pushed_at":"2026-09-18T07:56:25+00:00","maintenance_label":"Very active","stars_delta_30d":4382,"url":"https://www.graphcanon.com/tools/infiniflow-ragflow","markdown_url":"https://www.graphcanon.com/tools/infiniflow-ragflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/infiniflow-ragflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=infiniflow-ragflow","description":"RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs","homepage_url":"https://ragflow.io","license":"Apache-2.0","open_issues":1440,"watchers":365,"ai_summary":"RAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs.","readme_excerpt":"## 🔧 Build a Docker Image\n\nThis image is approximately 2 GB in size and relies on external LLM and embedding services.\n\n```bash\ngit clone https://github.com/infiniflow/ragflow.git\ncd ragflow/\ndocker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .\n```\n\nOr if you are behind a proxy, you can pass proxy arguments:\n\n```bash\ndocker build --platform linux/amd64 \\\n  --build-arg http_proxy=http://YOUR_PROXY:PORT \\\n  --build-arg https_proxy=http://YOUR_PROXY:PORT \\\n  -f Dockerfile -t infiniflow/ragflow:nightly .\n```","github_created_at":"2023-12-12T06:13:13+00:00","created_at":"2026-07-07T17:36:08.622995+00:00","updated_at":"2026-09-20T04:28:30.496922+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":"agent-harness","name":"agent-harness"},{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"agentic-retrieval","name":"agentic-retrieval"},{"slug":"agentic-search","name":"agentic-search"},{"slug":"context-engine","name":"context-engine"},{"slug":"rag","name":"rag"},{"slug":"retrieval-augmented-generation","name":"retrieval-augmented generation"},{"slug":"search-harness","name":"search-harness"}],"trust":{"provenance":{"is_fork":false,"github_id":730534580,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:07:18.136Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":6,"days_since_push":0,"last_release_at":"2026-09-10T11:11:35Z","stars_delta_30d":4382,"open_issues_delta_30d":-553},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":3,"high_count":0,"last_scan_at":"2026-09-18T08:07:20.042Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:07:17.025Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-09-18T08:07:17.025Z","managed_saas":false},"languages":{"value":["go","python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:07:17.025Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-09-18T08:07:17.025Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:07:17.025Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.","If your project requires a Docker image that is approximately 2 GB in size and can be built with or without proxy support.","When you are working with Go and prefer tools that are compatible with this language."],"when_not_to_use":["If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services.","If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size.","When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image."],"source":"enrich:decision_facts","observed_at":"2026-09-18T11:40:02.938Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"RAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs, built in Go under the Apache-2.0 license."}]}}