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 alternatives
Other rag tools we track, ranked by the same independent score.
- RagieA managed service that automates document ingestion, parsing, and intelligent retrieval for AI applicationsmedium · 55%
- LightRAGlow · 16%
- Amazon Bedrock Knowledge BasesManaged retrieval-augmented generation service that connects AI applications to proprietary enterprise datamedium · 62%
- PathwayA streaming data framework for building real-time AI and ML applicationslow · 36%
- Contextual AIlow · 41%
- Kotaemonlow · 23%
Compare Ragas
Side by side against other rag tools, attribute by attribute, with a source on every value.
The Vioscale 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 |
|---|---|---|---|---|
| Pricing transparency | 80 | 0.08 | 6.7 | ✓ |
| Capabilities | 67 | 0.08 | 5.6 | ✓ |
| Price level | 100 | 0.05 | 5.2 | ✓ |
| Release cadence | 96 | 0.05 | 5.0 | ✓ |
| Stars | 79 | 0.03 | 2.1 | ✓ |
| Integrations | 17 | 0.09 | 1.6 | ✓ |
| Dependent projects | 5 | 0.06 | 0.3 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Development activity | 0 | 0.09 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Security score | 0 | 0.04 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 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
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 · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-26 · 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 · 2026-08-26 · 90% |