Ragas is an open-source RAG evaluation framework designed for automating evaluation, monitoring, and improvement of retrieval-augmented generation systems, helping developers move from subjective checks to a systematic, quantifiable evaluation process.
Ragas evaluates in two dimensions: retrieval and generation. Core metrics include contextual accuracy, recall, and relevance, as well as the fidelity of answers. This covers the key quality points of RAG systems.
Ragas offers integration support with popular RAG frameworks such as LangChain and LlamaIndex. It can be installed via pip, and you can quickly connect it to your existing projects by following the official docs and API.
Evaluation requires a dataset that includes user questions, system-generated answers, retrieved contexts, and optional reference answers, ensuring proper alignment. See the official docs for the exact format.
The core framework of Ragas is open source and available on GitHub. The team also offers enterprise features, collaboration, and paid consulting services—contact the official site for details.
Suitable for developers, algorithm engineers, research teams, and enterprises involved in building, optimizing, or deploying RAG systems, especially where objective, repeatable evaluation of LLM performance is required.
Arize AI is a lifecycle observability and evaluation platform for large language models (LLMs) and agents. It helps AI engineering teams monitor, evaluate, and optimize model performance to ensure application reliability and business impact.
Ragie AI is a fully managed RAG-as-a-service platform for developers, designed to simplify the integration and deployment of retrieval-augmented generation technology, helping developers quickly build intelligent applications based on their own knowledge base.
