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Ragas

Open Source

Evaluation framework for RAG pipelines with metrics for faithfulness, relevance, and recall.

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Pricing

Free / Open source

Type

Automation

Languages

Python

// VERDICT

Reach for Ragas when you're testing a RAG pipeline and want purpose-built metrics for retrieval quality and answer faithfulness. Skip it when you're not doing RAG, or you want a hosted platform or general-purpose eval framework.

Best for

A focused framework for evaluating retrieval-augmented generation (RAG) - metrics for context precision/recall, faithfulness and answer relevance that test both the retrieval and generation halves.

Avoid when

You aren't building RAG, you want a broad eval+observability platform, or no-code evaluation.

CI/CD fit

Python library · CI eval gates · GitHub Actions

Languages

Python

Team fit

RAG app teams · Dev/QA testing retrieval · Teams measuring faithfulness

Setup

Easy

Maintenance

Low

Learning

Intermediate

Licence

Free / Open source

// BEST FOR

  • Measuring RAG retrieval quality (context precision/recall)
  • Testing answer faithfulness to retrieved context
  • Catching invented (unfaithful) answers
  • Evaluating both retrieval and generation separately
  • Open-source and code-first
  • Building a RAG eval dataset for CI

// AVOID WHEN

  • You're not building a RAG system
  • You want a broad eval+observability platform
  • No-code evaluation is required
  • General-purpose (non-RAG) eval is the need (DeepEval)
  • You need a managed UI/datasets
  • Manual human eval is your only method

// QUICK START

pip install ragas
# evaluate over {question, contexts, answer, ground_truth}
# metrics: faithfulness, answer_relevancy, context_precision/recall

// ALTERNATIVES TO CONSIDER

ToolChoose it when
DeepEvalYou want general LLM evals with a pytest-like API.
TruLensYou want feedback-function evaluation with tracing.
LangSmithYou want hosted RAG evals plus tracing.

// FEATURES

  • Faithfulness, answer relevance, and context precision metrics
  • Synthetic test set generation from source documents
  • Reference-free metrics that do not require ground truth
  • Integrations with LangChain, LlamaIndex, and Haystack
  • CI-friendly evaluation runs over RAG outputs

// PROS

  • Research-backed metrics tailored specifically for RAG
  • Synthetic data generator removes a major bottleneck
  • Plug-in hooks for the popular RAG frameworks
  • Active development with frequent metric additions

// CONS

  • Metric runs require an LLM judge — adds cost and latency
  • Quality of evaluation depends heavily on judge model choice
  • Younger project — APIs still evolving

// EXAMPLE QA WORKFLOW

  1. Install Ragas (pip)

  2. Assemble a RAG eval dataset (Q / contexts / answer)

  3. Run retrieval + faithfulness + relevance metrics

  4. Review where retrieval vs generation fails

  5. Gate CI on faithfulness/relevance thresholds

  6. Grow the dataset from real RAG failures

// RELATED QA.CODES RESOURCES

Cheat sheets