# Ragas vs RAGFlow

| Attribute | Ragas | RAGFlow |
|---|---|---|
| **vioscaleAI score** | 54 (32% (low)) | 53.5 (9% (low)) |
| adoption.dependent_repos | 1 | - |
| adoption.github_stars | 15,838 | - |
| content.faq | `[{"answer":"A Python-based framework that evaluates RAG applications through automatic metrics covering faithfulness, relevance, and recall. Includes tools to synthetically generate test datasets customized for specific use cases, enabling developers to assess LLM application performance at both component and end-to-end levels. It is indexed under RAG Tools.","source":"https://ragas.io","question":"What is Ragas?","confidence":0.6},{"answer":"Ragas is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.","source":"https://ragas.io","question":"Is Ragas free to use?","confidence":0.6},{"answer":"Ragas supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://ragas.io","question":"What platforms does Ragas support?","confidence":0.6},{"answer":"Yes. Ragas can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://ragas.io","question":"Can Ragas be self-hosted?","confidence":0.6},{"answer":"We have confirmed 3 integrations for Ragas, including LlamaIndex, LangSmith and OpenAI. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://ragas.io","question":"What does Ragas integrate with?","confidence":0.6},{"answer":"Yes. Ragas is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.","source":"https://github.com/vibrantlabsai/ragas","question":"Is Ragas open source?","confidence":0.95}]` | `[{"answer":"An AI platform that combines retrieval-augmented generation with autonomous agent features, enabling developers to create production-ready AI systems from complex data sources. It supports multiple deployment models (cloud or self-hosted Docker), integrates with various LLM providers and data sources, and provides streamlined workflows for enterprises of any scale. It is indexed under RAG Tools.","source":"https://ragflow.io","question":"What is RAGFlow?","confidence":0.6},{"answer":"RAGFlow is open source, so it can be self-hosted and used at no licence cost. Pricing changes often, so verify at source before relying on it.","source":"https://ragflow.io","question":"Is RAGFlow free to use?","confidence":0.6},{"answer":"RAGFlow supports the web, Windows, macOS, Linux and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://ragflow.io","question":"What platforms does RAGFlow support?","confidence":0.6},{"answer":"Yes. RAGFlow can be deployed cloud / SaaS and self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://ragflow.io","question":"Can RAGFlow be self-hosted?","confidence":0.6},{"answer":"We have confirmed 13 integrations for RAGFlow, including Discord, Feishu, Telegram, Line, Confluence, S3, Notion and Google Drive, plus 5 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://ragflow.io","question":"What does RAGFlow integrate with?","confidence":0.6}]` |
| deployment.options | `{"self_hosted":true}` | `{"cloud":true,"self_hosted":true}` |
| description.long | A Python-based framework that evaluates RAG applications through automatic metrics covering faithfulness, relevance, and recall. Includes tools to synthetically generate test datasets customized for specific use cases, enabling developers to assess LLM application performance at both component and end-to-end levels. | RAGFlow is an open-source RAG engine that streamlines data processing and integration for building production-ready AI systems. It supports multiple data formats and provides configurable LLM and embedding options with agent-based workflows. |
| features.capabilities | `{"open_source":true,"pricing_model":"free_open_source","architecture_model":"open_source_cli","built_in_rag_eval_metrics":true,"llm_as_a_judge_prompt_grading_framework":true,"synthetic_test_dataset_generation_from_documents":true}` | `{"open_source":true,"multimodal_rag":true,"citation_attribution":true,"llm_provider_agnostic":true,"managed_ingestion_pipeline":true,"embedding_provider_agnostic":true,"multi_hop_agentic_retrieval":true}` |
| integrations.count | 3 | 12 |
| integrations.list | `[{"name":"LlamaIndex"},{"name":"LangSmith"},{"name":"OpenAI"}]` | `[{"name":"Discord"},{"name":"Feishu"},{"name":"Telegram"},{"name":"Line"},{"name":"Confluence"},{"name":"S3"},{"name":"Notion"},{"name":"Google Drive"},{"name":"OpenAI"},{"name":"DeepSeek"},{"name":"Gemini"},{"name":"OpenClaw"},{"name":"OpenAI GPT-5","native":true,"category":"llm"},{"name":"Google Gemini 3 Pro","native":true,"category":"llm"},{"name":"DeepSeek v4","native":true,"category":"llm"},{"name":"MCP","native":true}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| platform.support | `{"cli":true}` | `{"web":true,"linux":true}` |
| pricing | `{"type":"open_source","freeTier":true,"sourceUrl":"https://ragas.io","retrievedAt":"2026-08-21T13:52:19.928Z"}` | `{"type":"open_source","freeTier":true,"sourceUrl":"https://ragflow.io","retrievedAt":"2026-09-05T21:04:46.611Z"}` |
| pricing.free_tier | yes | yes |
| pricing.model | commercial | open_source |
| pricing.price_level | free | free |
| pricing.transparent | yes | yes |
| release.cadence_days | 7 | - |
| release.history | `[{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.4.3","date":"2026-01-13T17:47:29Z","type":"stable","version":"v0.4.3"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.4.2","date":"2025-12-23T17:13:41Z","type":"stable","version":"v0.4.2"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.4.1","date":"2025-12-10T16:28:51Z","type":"stable","version":"v0.4.1"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.4.0","date":"2025-12-03T16:22:29Z","type":"stable","version":"v0.4.0"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.9","date":"2025-11-11T17:24:46Z","type":"stable","version":"v0.3.9"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.8","date":"2025-10-28T19:09:17Z","type":"stable","version":"v0.3.8"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.7","date":"2025-10-14T16:21:37Z","type":"stable","version":"v0.3.7"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.6","date":"2025-10-03T03:56:31Z","type":"stable","version":"v0.3.6"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.5","date":"2025-09-17T19:13:15Z","type":"stable","version":"v0.3.5"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.5rc2","date":"2025-09-17T17:40:25Z","type":"prerelease","version":"v0.3.5rc2"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.5rc1","date":"2025-09-17T17:31:23Z","type":"prerelease","version":"v0.3.5rc1"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.4","date":"2025-09-10T23:57:28Z","type":"stable","version":"v0.3.4"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.3","date":"2025-09-04T17:59:51Z","type":"stable","version":"v0.3.3"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.3rc1","date":"2025-09-04T17:46:09Z","type":"prerelease","version":"v0.3.3rc1"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.2","date":"2025-08-19T12:03:50Z","type":"stable","version":"v0.3.2"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.2rc3","date":"2025-08-19T12:01:12Z","type":"prerelease","version":"v0.3.2rc3"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.2-rc2","date":"2025-08-19T11:49:23Z","type":"prerelease","version":"v0.3.2-rc2"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.2-rc1","date":"2025-08-19T10:28:33Z","type":"prerelease","version":"v0.3.2-rc1"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.1","date":"2025-08-11T11:15:03Z","type":"stable","version":"v0.3.1"},{"url":"https://github.com/vibrantlabsai/ragas/releases/tag/v0.3.0","date":"2025-07-17T05:32:24Z","type":"stable","version":"v0.3.0"}]` | - |
| security.vulnerabilities | `{"count":2,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=ragas&per_page=100","last_12m":2,"max_severity":"HIGH"}` | - |

## Capabilities (RAG Tools)

| Capability | Ragas | RAGFlow |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Hybrid retrieval | - | - |
| Reranking integration | - | - |
| Multi hop agentic retrieval | - | ✓ |
| Embedding provider agnostic | - | ✓ |
| LLM provider agnostic | - | ✓ |
| Built in rag eval metrics | ✓ | - |
| Citation attribution | - | ✓ |
| Multimodal rag | - | ✓ |
| Managed ingestion pipeline | - | ✓ |
| Open source | ✓ | ✓ |

*Source: vioscaleAI. Generated 2026-10-15T23:57:25.349Z. "-" = undocumented, not absent.*