Ragas is an evaluation framework and platform for measuring the quality of RAG systems, agents, and LLM applications.

Who should use Ragas?

It is best for developers and AI teams building retrieval systems, agents, document assistants, and other LLM applications that need measurable quality.

What does Ragas evaluate?

Ragas can evaluate dimensions such as retrieval quality, context relevance, answer faithfulness, and other application-specific AI quality signals.

Is Ragas open source?

Yes. Ragas provides an open-source evaluation framework, with hosted or managed capabilities available separately.

Can Ragas test RAG pipelines?

Yes. Ragas was designed around RAG evaluation and can help teams measure retrieval and generation behavior across datasets.

What should teams watch for?

Use representative datasets, validate metrics against human judgment, and combine Ragas with broader observability and production monitoring.

Ragas

Evaluation framework and platform for RAG, agents, and LLM applications.

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What is Ragas?

TL;DR

Ragas is an evaluation framework and platform for testing RAG systems, agents, and LLM applications, measuring retrieval quality, faithfulness, relevance, and other production AI quality signals across datasets.

Evaluate RAG pipelines and agent outputs with research-backed metrics
Use open-source evaluation framework for local workflows
Create datasets, score outputs, and catch regressions

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About

Ragas is an evaluation framework and platform for RAG systems, agents, and LLM applications. It helps developers measure retrieval quality, generation quality, answer faithfulness, context relevance, and other dimensions that matter when AI systems use external knowledge. The product is useful for founders and technical teams building document assistants, knowledge base chatbots, search products, research tools, support copilots, legal or financial analysis workflows, and agent systems that depend on retrieval. RAG systems can look impressive in demos but fail when retrieval is weak, context is irrelevant, or generated answers are not faithful to source documents. Ragas gives teams a structured way to run evaluations, build datasets, compare pipeline versions, and detect regressions. It can be used locally as an open-source library and through hosted workflows depending on team needs. For FutureStack, Ragas belongs in Developer Tools because RAG evaluation is one of the most important practical problems for AI builders. It is best for developers and founders who need to prove that their RAG systems and agents are accurate, grounded, and improving over time.

Use Cases

RAG Evaluation

Measure retrieval and generation quality across document question-answering and knowledge assistant workflows.

Agent Evaluation

Evaluate agent responses and multi-step behavior against datasets, expected outcomes, and quality criteria.

Regression Testing

Run repeatable evaluations after model, prompt, retrieval, or application changes to catch quality regressions.

Production AI QA

Build quality gates and evaluation workflows that help teams ship more reliable AI applications.

Key Features

Evaluate RAG pipelines and agent outputs with research-backed metrics
Use open-source evaluation framework for local workflows
Create datasets, score outputs, and catch regressions
Useful for CI/CD, QA, and production AI quality checks

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