LLM observability & evaluation · Ragas

Ragas

Open-source Python framework of reference-free metrics purpose-built for evaluating retrieval-augmented generation (RAG) pipelines.

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Ragas is an open-source Python evaluation framework focused specifically on RAG systems, where output quality depends on both retrieval and generation. It provides metrics such as faithfulness, answer relevancy, context precision, and context recall that can run reference-free (without hand-labeled ground-truth answers), along with tooling for synthetically generating evaluation test sets and integrations with LangChain and LlamaIndex. It runs entirely as a library inside a user's own process rather than as a hosted API, so there is no per-call charge or vendor lock-in from Ragas itself; teams typically pair it with a tracing/observability tool (such as Langfuse) to store and visualize the resulting scores over time. The project does not publish a separate commercial pricing page; the vendor mentions enterprise collaborations available by direct contact but does not detail terms.

At a glance

Vendor Ragas
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Teams evaluating RAG pipelines specifically, who want reference-free metrics without hand-labeled ground truth.

Pricing

Free, open-source Python library with no usage limits; no separate hosted product or published pricing.

Pricing has not been verified yet — see the vendor's site.

Features

  • Reference-free RAG evaluation metrics (faithfulness, answer relevancy, context precision/recall)
  • Synthetic test-set generation for evaluation datasets
  • LangChain and LlamaIndex integrations
  • Component-level and end-to-end pipeline scoring
  • Runs in-process as a Python library, no hosted API required

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

DeepEval Open-source Python evaluation framework with 30+ research-backed metrics, with an optional paid platform (Confident AI) for CI/CD and hosted reporting. Arize Phoenix Open-source, local-first tracing and evaluation library for LLM and agent applications, with an optional path to Arize's commercial platform. Patronus AI Evaluation-focused platform offering pre-built and custom evaluator models to score LLM outputs for accuracy, safety, and quality.

Ragas in the index now

Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns. Retrieval-augmented generation (RAG) An architecture that retrieves relevant documents at query time and feeds them to a language model so its answers are grounded in sources. Vendor lock-in The difficulty and cost of switching away from a vendor once an organization is deeply dependent on that vendor's proprietary systems.

Related guides

How to choose an LLM observability tool Pick an LLM tool by whether you need tracing, scoring or a gateway first — most teams eventually need all three, rarely from one vendor. How to evaluate LLM applications Offline evals, LLM-as-judge, human review and production monitoring, aimed at the whole pipeline rather than the model alone.