What is Ragas?
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.
Ragas pricing
We don't have Ragas's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What Ragas does
The capabilities that matter for rag tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- 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
- ✓
Platform & deployment
Independently observed- CLI
- Self-hosted
Integrations (3)
Independently observed- LlamaIndex
- LangSmith
- OpenAI
Security & compliance
Known vulnerabilities: 2 (2 in the last 12 months), max severity HIGH sourcea count reflects scale & disclosure, not quality
Ragas FAQ
Common questions about Ragas, answered from independent, dated evidence.
What is Ragas?
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
Is Ragas free to use?
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
What platforms does Ragas support?
Ragas supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://ragas.io
Can Ragas be self-hosted?
Yes. Ragas can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://ragas.io
What does Ragas integrate with?
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
Is Ragas open source?
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.
Ragas alternatives
Other rag tools we track, ranked by the same independent score.
- LightRAGA lightweight framework that combines knowledge graphs and vector embeddings for efficient information retrievalmedium · 53%
- RagieA managed AI context platform for agents, assistants, and applicationsmedium · 73%
- Amazon Bedrock Knowledge BasesA managed service connecting AI applications to enterprise data with automatic indexing and retrievalmedium · 74%
- PathwayA streaming data framework for building real-time AI and ML applicationslow · 35%
- RAGFlowAn open-source platform combining retrieval-augmented generation with AI agent capabilities to provide contextual support for language modelslow · 9%
- Contextual AImedium · 61%
Compare Ragas
Side by side against other rag tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Ragas, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Release cadence | 96 | 0.08 | 7.8 | ✓ |
| Capabilities | 67 | 0.08 | 5.4 | ✓ |
| Stars | 79 | 0.05 | 3.7 | ✓ |
| Integrations | 17 | 0.05 | 0.9 | ✓ |
| Dependent projects | 5 | 0.05 | 0.3 | ✓ |
| Development activity | 0 | 0.14 | 0.0 | - |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 66% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Open source: Yes · Pricing model: free_open_source · Architecture model: open_source_cli · Built in rag eval metrics: Yes · Llm as a judge prompt grading framework: Yes · Synthetic test dataset generation from documents: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 3 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-09-24 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-09-24 · 95% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| 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 | highsource · 2026-09-24 · 90% |
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