8.2 L4

Ragas

Name: ragas
Rating: 8.2

Native Assessed · Docs reviewed · Mar 26, 2026 Confidence 0.56 Last evaluated Mar 26, 2026

Verify before you commit

Trust read first, source links second, build decision third.

Use this page to sanity-check Ragas quickly. We surface the evidence tier, freshness, and failure posture here, then put the official links where you can actually act on them, especially on mobile.

Try through Rhumb

Methodology Trust process Current self-assessment Dispute this score

Evidence

Assessed

Docs reviewed · Mar 26, 2026

Freshness

Updated 2026-03-26T14:30:43.669+00:00

Mar 26, 2026

Failures

Clear

No active failures listed

Score breakdown

Dimension	Score	Bar
Execution Score Measures reliability, idempotency, error ergonomics, latency distribution, and schema stability.	8.4
Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously.	7.9
Aggregate AN Score Composite score: 70% execution + 30% access readiness.	8.2

Autonomy breakdown

P1 Payment Autonomy

—

G1 Governance Readiness

—

W1 Web Agent Accessibility

—

Overall Autonomy

Pending

Active failure modes

No active failure modes reported.

Reviews

Published review summaries with trust provenance attached to each card.

How are reviews sourced?

Docs-backed Built from public docs and product materials.

Test-backed Backed by guided testing or evaluator-run checks.

Runtime-verified Verified from authenticated runtime evidence.

Ragas provides the measurement layer for RAG quality — define a test dataset (question, ground truth, context, answer), run the evaluation suite, and get quantitative scores for faithfulness (does the answer use only retrieved context?), answer relevancy (does the answer address the question?), context precision (is retrieved context actually relevant?), and context recall (does retrieved context cover the answer?). For agents: evaluate RAG pipeline quality programmatically, generate synthetic test sets from your corpus, track metric changes across pipeline versions. Works with any LLM (via LangChain or direct OpenAI API) for the judge model. Confidence is docs-derived.: Comprehensive Agent-Usability Assessment

Docs-backed

Python SDK: pip install ragas. Evaluate: from ragas import evaluate; from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall; from datasets import Dataset. dataset = Dataset.from_dict({"question": ["..."], "answer": ["..."], "contexts": [["ctx1", "ctx2"]], "ground_truth": ["..."]}). result = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_precision, context_recall]). result.to_pandas() → DataFrame with per-question scores. Synthetic test set: from ragas.testset import TestsetGenerator; gen = TestsetGenerator.with_openai(); testset = gen.generate_with_llamaindex_docs(documents, test_size=100). LangChain integration: RagasEvaluatorChain wraps evaluation chain. Custom LLM judge: metrics accept llm parameter for any LangChain-compatible LLM.

keel-expansion Mar 26, 2026

Ragas provides the measurement layer for RAG quality — define a test dataset (question, ground truth, context, answer), run the evaluation suite, and get quantitative scores for faithfulness (does the answer use only retrieved context?), answer relevancy (does the answer address the question?), context precision (is retrieved context actually relevant?), and context recall (does retrieved context cover the answer?). For agents: evaluate RAG pipeline quality programmatically, generate synthetic test sets from your corpus, track metric changes across pipeline versions. Works with any LLM (via LangChain or direct OpenAI API) for the judge model. Confidence is docs-derived.: API Design & Integration Surface

Docs-backed

No external Ragas API — runs entirely locally using your choice of LLM for the judge model. LLM judge credentials: OpenAI API key (or any LangChain-compatible provider) set as environment variable. Ragas Cloud (Ragas App): experimental hosted evaluation tracking — API key from ragas.io. Data: evaluation dataset stays local; only judge LLM API calls go to external providers. No auth layer for the framework itself.

keel-expansion Mar 26, 2026

Ragas provides the measurement layer for RAG quality — define a test dataset (question, ground truth, context, answer), run the evaluation suite, and get quantitative scores for faithfulness (does the answer use only retrieved context?), answer relevancy (does the answer address the question?), context precision (is retrieved context actually relevant?), and context recall (does retrieved context cover the answer?). For agents: evaluate RAG pipeline quality programmatically, generate synthetic test sets from your corpus, track metric changes across pipeline versions. Works with any LLM (via LangChain or direct OpenAI API) for the judge model. Confidence is docs-derived.: Auth & Access Control

Docs-backed

Evaluation cost: each metric requires multiple LLM calls per sample (e.g., faithfulness: 2 LLM calls/sample). 100-sample evaluation ≈ 200–600 LLM API calls — budget accordingly. LLM judge variance: scores vary slightly between evaluation runs (LLM non-determinism); average across runs for stable metrics. Context length: long contexts may exceed judge LLM context window — chunk contexts or use a model with larger context. Ragas uses asyncio for parallel metric computation — configurable concurrency. Missing fields: evaluation fails with clear KeyError if required columns (question, answer, contexts) are missing from dataset.

keel-expansion Mar 26, 2026

Ragas provides the measurement layer for RAG quality — define a test dataset (question, ground truth, context, answer), run the evaluation suite, and get quantitative scores for faithfulness (does the answer use only retrieved context?), answer relevancy (does the answer address the question?), context precision (is retrieved context actually relevant?), and context recall (does retrieved context cover the answer?). For agents: evaluate RAG pipeline quality programmatically, generate synthetic test sets from your corpus, track metric changes across pipeline versions. Works with any LLM (via LangChain or direct OpenAI API) for the judge model. Confidence is docs-derived.: Error Handling & Operational Reliability

Docs-backed

docs.ragas.io covers metric definitions, evaluation guide, synthetic test set generation, integration with LangChain/LlamaIndex/Langfuse/W&B, and contributing guide. Getting started: pip install ragas — first RAG evaluation in under 10 minutes (requires OpenAI API key for judge). GitHub: explodinggradients/ragas (8k+ stars). Community via Discord (active). Comprehensive metric documentation with mathematical definitions. One of the most widely used RAG evaluation frameworks.

keel-expansion Mar 26, 2026

Ragas provides the measurement layer for RAG quality — define a test dataset (question, ground truth, context, answer), run the evaluation suite, and get quantitative scores for faithfulness (does the answer use only retrieved context?), answer relevancy (does the answer address the question?), context precision (is retrieved context actually relevant?), and context recall (does retrieved context cover the answer?). For agents: evaluate RAG pipeline quality programmatically, generate synthetic test sets from your corpus, track metric changes across pipeline versions. Works with any LLM (via LangChain or direct OpenAI API) for the judge model. Confidence is docs-derived.: Documentation & Developer Experience

Docs-backed

0.56

keel-expansion Mar 26, 2026

Use in your agent

mcp

→ get_score ("ragas")

● Ragas 8.2 L4 Native

exec: 8.4 · access: 7.9

Trust shortcuts

This score is documentation-derived. Treat it as a docs-based evaluation of API design, auth, error handling, and documentation quality.

Read how the score works, how disputes are handled, and how Rhumb scored itself before launch.

Methodology → Trust process → Current self-assessment → Dispute this score →

Overall tier

L4 Native

8.2 / 10.0

Alternatives

No alternatives captured yet.

Dispute this score →