Ragas

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.