Ragas

Ragas is an open source framework for testing and evaluating applications built on large language models. It is especially used for retrieval augmented generation pipelines, where answer quality depends on the documents that were found.

Ragas provides metrics that score how well an application answers questions and uses its retrieved context. It can also generate synthetic test data, which helps when a team has few real examples. The framework is meant for use during development, and it can be used to watch quality after release. Teams usually install it as a Python package and add the checks to their test workflow.

• Score RAG pipelines with built-in metrics
• Generate synthetic test data for evaluations
• Run evaluation workflows during development
• Monitor quality after release
• Install with pip

The framework suits ML engineers, developers and researchers who build question answering or search features. Teams that only need a quick manual check of a few answers may not need an evaluation framework yet.

Ragas is open source under the Apache-2.0 license and can be used at no license cost. Metrics that call an LLM as a judge will use your model provider's tokens, so evaluation runs have a real cost. Scores depend on the models and prompts used for judging, so results should be compared on the same setup over time.