{"data":{"slug":"infiniflow-ragflow","name":"ragflow","tagline":"Retrieval-Augmented Generation 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":86541,"forks":10167,"topics":["agent-harness","agentic-ai","agentic-retrieval","agentic-search","ai","ai-agents","context-engine","context-engineering","context-management","harness-engineering","knowledge-compilation","llm-apps","rag","retrieval-augmented-generation"],"archived":false,"github_pushed_at":"2026-07-31T14:59:12+00:00","maintenance_label":"Very active","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":1993,"watchers":349,"ai_summary":"RAGFlow integrates Retrieval-Augmented Generation with AI agents to enhance context management for LLM applications.","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":"2026-08-01T06:00:39.878592+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":"agentic-ai","name":"agentic-ai"},{"slug":"context-management","name":"context management"},{"slug":"rag","name":"rag"},{"slug":"retrieval-augmented-generation","name":"retrieval-augmented-generation"}],"trust":{"provenance":{"is_fork":false,"github_id":730534580,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-01T06:00:39.089Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":10,"days_since_push":0,"last_release_at":"2026-07-07T13:26:03Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":4,"high_count":0,"last_scan_at":"2026-07-11T10:28:33.840Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-01T06:00:39.513Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-01T06:00:39.513Z","managed_saas":false},"languages":{"value":["go","python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-01T06:00:39.513Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-01T06:00:39.513Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-01T06:00:39.513Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services."],"requires_docker":true},"constraints":{"requires_docker":true},"when_to_use":["- You need an integrated RAG system with AI agent capabilities for better context management in your applications.","- Your project requires advanced retrieval-augmented generation features that can be further enhanced by intelligent agents.","- You are working on a project that involves using LLMs and benefits from an open-source solution under the Apache-2.0 license."],"when_not_to_use":["- If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go.","- Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB).","- You cannot use external LLM services and embedding services, as RAGFlow relies on them to function."],"source":"enrich:decision_facts","observed_at":"2026-07-11T10:42:19.269Z"},"constraint_facets":{"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services."},{"label":"Adopt for","value":"RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license."},{"label":"License detail","value":"Apache-2.0 License"}]}}