Loading tool details…
Complete pricing guide for Ragas. Compare all plans, analyze costs, and find the perfect tier for your needs.
Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Ragas is worth it →
Pricing sourced from Ragas · Confirm current pricing and terms with the vendor.
A check mark means listed in that plan’s record. A dash means not listed, rather than confirmed unavailable. Verify current vendor terms.
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.
AI builders and operators use Ragas to streamline their workflow.
Try Ragas Now →Open-source CLI and library for testing, evaluating, and red-teaming LLM prompts, models, and RAG pipelines — runs locally on your machine or in CI.
Compare Pricing →Braintrust review 2026: unified evals, prompt workbench, observability, and AI proxy used by OpenAI, Stripe, Notion — features, pricing, pros, cons.
Compare Pricing →LangSmith helps developers trace, evaluate, test, and monitor applications built with language models and agent workflows.
Compare Pricing →DeepEval is the open-source, Pytest-style LLM evaluation framework from Confident AI. Ship it in your test suite the same way you ship pytest — it runs 50+ research-backed metrics (hallucination, tool-use correctness, contextual precision/recall, conversational quality) offline in dev and inline in CI/CD, then optionally streams the results to Confident AI's paid cloud for team dashboards and prompt versioning.
Compare Pricing →