vibrantlabsai/ragas

Ragas is a Python toolkit for testing and measuring the quality of applications built on large language models. If you have built something that uses an AI model to answer questions, summarize text, or retrieve information, Ragas gives you a structured way to score how well it is working. The core idea is to move evaluation away from manual, subjective review and toward repeatable, data-driven scoring. Ragas provides a set of pre-built metrics that can assess things like whether a summary is accurate or whether a generated answer is grounded in the source material. You can also define your own custom scoring criteria by writing a prompt that describes what you want to check, and Ragas will apply that check to your outputs automatically. One practical problem the library addresses is the cold-start problem for testing: many teams want to run evaluations but do not have a ready-made set of test cases. Ragas includes a test data generation feature that can create a range of scenarios from your existing content, so you can start evaluating without building a test set by hand. Ragas is installed via pip and works alongside common AI orchestration frameworks. It collects anonymized usage data by default, which you can opt out of by setting an environment variable. The project is open source under the Apache 2.0 license and maintained by VibrantLabs, who also offer paid consulting for teams needing help scaling their evaluation workflows. The quickstart command provides template projects for common evaluation scenarios like RAG (retrieval-augmented generation) systems, with additional templates for agent evaluation and prompt testing listed as coming soon.