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Pricing sourced from RAGAS · Last verified March 2026
RAGAS is best used to evaluate retrieval-augmented generation systems, AI workflows, and tool-using agents. The documentation includes tutorials for evaluating a prompt, a simple RAG system, an AI workflow, and an AI agent. It is especially relevant when a team needs to inspect retrieval quality, groundedness, response relevance, tool-call accuracy, or agent goal completion before shipping changes.
The RAGAS documentation lists several RAG-focused metrics, including Context Precision, Context Recall, Context Entities Recall, Noise Sensitivity, Response Relevancy, and Faithfulness. It also includes Nvidia-related metrics such as Answer Accuracy, Context Relevance, and Response Groundedness. This gives teams separate ways to evaluate whether the right context was retrieved, whether the answer used that context properly, and whether the final response addressed the user request.
RAGAS is not limited to classic RAG pipelines. The documentation includes sections for agent and tool-use cases, with metrics such as Topic Adherence, Tool Call Accuracy, Tool Call F1, and Agent Goal Accuracy. It also includes a guide for evaluating a text-to-SQL agent, which makes it useful for teams building more complex AI workflows that call tools or generate structured actions.
The scraped documentation lists integrations across observability platforms, LLM providers, and frameworks. Observability integrations include Arize and LangSmith, while provider guidance includes Amazon Bedrock, Google Gemini, OCI Gen AI, and Vertex AI models. Framework integrations listed in the docs include AG-UI, Griptape, Haystack, LangChain, LangGraph, LlamaIndex, LlamaIndex Agents, LlamaStack, R2R, and Swarm.
Compared to broader evaluation tools in our directory, RAGAS is more focused on RAG, retrieval quality, generated-answer faithfulness, and tool-use evaluation. Promptfoo may be a better fit for lightweight prompt regression testing, Braintrust for hosted experiment management, LangSmith for LangChain-native tracing and debugging, and DeepEval for broader LLM evaluation workflows. Choose RAGAS when the core problem is measuring whether retrieval, context usage, and grounded generation are working correctly.
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