# FastRAG vs Ragas

| Attribute | FastRAG | Ragas |
|---|---|---|
| **vioscaleAI score** | 39.9 (54% (medium)) | 54 (32% (low)) |
| activity.commits_last_30d | 0 | - |
| adoption.dependent_repos | 0 | 1 |
| adoption.github_stars | 1,786 | 15,838 |
| content.faq | `[{"answer":"A toolkit for researchers and developers to construct and optimize RAG applications. It provides streamlined components for retrieval and generation, with support for multiple LLM backends and hardware accelerators, and maintains compatibility with the Haystack framework. It is indexed under RAG Tools.","source":"https://github.com/pricing","question":"What is FastRAG?","confidence":0.6},{"answer":"FastRAG 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://github.com/pricing","question":"Is FastRAG free to use?","confidence":0.6},{"answer":"FastRAG supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://github.com/pricing","question":"What platforms does FastRAG support?","confidence":0.6},{"answer":"Yes. FastRAG can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://github.com/pricing","question":"Can FastRAG be self-hosted?","confidence":0.6},{"answer":"We have confirmed 6 integrations for FastRAG, including Haystack, HuggingFace, Elastic, Qdrant, OpenVINO and LlamaCpp. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://github.com/pricing","question":"What does FastRAG integrate with?","confidence":0.6},{"answer":"Yes. FastRAG 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/intellabs/fastRAG","question":"Is FastRAG open source?","confidence":0.95}]` | `[{"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}]` |
| deployment.options | `{"on_prem":true,"self_hosted":true}` | `{"self_hosted":true}` |
| description.long | An open-source Python toolkit that combines document retrieval with large language models to create question-answering and generation systems. Provides optimized components and supports multiple inference backends including Intel accelerators. | 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. |
| features.capabilities | `{"open_source":true,"multimodal_rag":true,"llm_provider_agnostic":true,"embedding_provider_agnostic":true}` | `{"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}` |
| integrations.count | 7 | 3 |
| integrations.list | `[{"name":"Haystack"},{"name":"HuggingFace"},{"name":"Elastic"},{"name":"Qdrant"},{"name":"OpenVINO"},{"name":"LlamaCpp"},{"name":"ONNX Runtime","native":true,"category":"inference_runtime"},{"name":"Llama CPP","native":true,"category":"inference_engine"},{"name":"Intel Gaudi","native":true,"category":"hardware"},{"name":"Elasticsearch","native":true}]` | `[{"name":"LlamaIndex"},{"name":"LangSmith"},{"name":"OpenAI"}]` |
| language.primary | Python | Python |
| license.spdx | Apache-2.0 | Apache-2.0 |
| platform.support | `{"cli":true}` | `{"cli":true}` |
| pricing | `{"type":"open_source","freeTier":true,"sourceUrl":"https://github.com/pricing","retrievedAt":"2026-08-21T10:58:48.479Z"}` | `{"type":"open_source","freeTier":true,"sourceUrl":"https://ragas.io","retrievedAt":"2026-08-21T13:52:19.928Z"}` |
| pricing.free_tier | yes | yes |
| pricing.model | open_source | commercial |
| pricing.price_level | free | free |
| pricing.transparent | yes | yes |
| release.cadence_days | 22 | 7 |
| release.history | `[{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.1.2","date":"2024-11-25T10:09:51Z","type":"stable","version":"v3.1.2"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.1.1","date":"2024-11-24T14:22:46Z","type":"stable","version":"v3.1.1"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.1.0","date":"2024-11-07T14:21:16Z","type":"stable","version":"v3.1.0"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.0.2","date":"2024-07-09T09:49:53Z","type":"stable","version":"v3.0.2"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.0.1","date":"2024-07-02T13:38:09Z","type":"stable","version":"v3.0.1"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v3.0","date":"2024-05-22T15:13:25Z","type":"stable","version":"v3.0"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v2.0","date":"2023-12-24T16:24:05Z","type":"stable","version":"v2.0"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v1.3.0","date":"2023-06-20T12:06:34Z","type":"stable","version":"v1.3.0"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v1.2.1","date":"2023-06-13T10:57:48Z","type":"stable","version":"v1.2.1"},{"url":"https://github.com/IntelLabs/fastRAG/releases/tag/v1.2.0","date":"2023-05-21T11:05:07Z","type":"stable","version":"v1.2.0"}]` | `[{"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.trust_center | https://github.com/security | - |
| security.vulnerabilities | `{"count":0,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=fastrag&per_page=100","last_12m":0,"max_severity":null}` | `{"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 | FastRAG | Ragas |
|---|:--:|:--:|
| **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 2026-10-16T20:51:14.184Z. "-" = undocumented, not absent.*