Ragas
Open-source Python framework that evaluates RAG pipelines across metrics like faithfulness, answer relevancy, context precision, and context recall using LLM-as-judge and statistical methods.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
No external data transmission for the core library; evaluation data stays local; LLM judge calls send prompts to the configured LLM provider.
⚡ Reliability
Best When
You have a RAG pipeline and need rigorous, reproducible metric scores across faithfulness and retrieval quality dimensions using open-source tooling with no per-call cost.
Avoid When
You need a fully managed cloud evaluation platform with CI/CD integrations and visual dashboards out of the box.
Use Cases
- • Benchmark a new retrieval strategy against an existing one to measure whether context precision improved
- • Run nightly CI evaluations of a RAG chatbot to catch regressions in faithfulness before deployment
- • Score generated answers against ground truth in a test dataset to quantify RAG pipeline quality
- • Evaluate multiple LLM models on the same RAG task to choose the best cost/quality tradeoff
- • Generate synthetic test datasets from documents to bootstrap evaluation when no human labels exist
Not For
- • Real-time inference or serving — Ragas is an offline evaluation library, not a production API
- • Evaluating non-RAG tasks like pure code generation or creative writing without retrieval components
- • Teams needing a hosted evaluation platform with dashboards — use Confident AI (deepeval) for that
Interface
Authentication
No auth required for the library itself; an LLM provider API key (e.g., OpenAI) is needed for LLM-as-judge metrics.
Pricing
Apache-2.0 open source. LLM judge calls (e.g., OpenAI) are the primary cost driver and are billed by the LLM provider.
Agent Metadata
Known Gotchas
- ⚠ NaN metric scores are returned silently when the LLM judge fails to parse its output — always check for NaN before using scores
- ⚠ Ragas requires a specific dataset schema (question, answer, contexts, ground_truth) — mismatched column names cause silent metric skips
- ⚠ LLM judge cost scales linearly with dataset size and number of metrics — large evaluations can be expensive
- ⚠ async_evaluate is faster for large datasets but requires careful event loop management in notebooks vs. scripts
- ⚠ Default metrics assume OpenAI models; using local or non-OpenAI models as judges requires custom LLM wrapper configuration
Alternatives
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Scores are editorial opinions as of 2026-03-07.