Ragas

What it does

Ragas is a library for evaluating Large Language Model (LLM) applications. It takes in evaluation data such as questions, retrieved contexts, and ground-truth answers, and produces metrics describing how well the application performs. It can also generate synthetic evaluation datasets tailored to your requirements and monitor quality for applications running in production.

People use Ragas to move from informal "vibe checks" to a repeatable experiment loop: make a change, run evaluations, observe results, and iterate. Its distinguishing features are an experiments-first workflow, metrics you can define with simple decorators or choose from a built-in library, and built-in dataset management and result tracking. Its documentation describes integration with frameworks such as LangChain and LlamaIndex.

Ragas suits developers and teams building RAG or other LLM applications who want systematic evaluation. Installation is via pip (pip install ragas) and the documentation provides a quickstart. Metrics are LLM-driven, so evaluations depend on an LLM, and enterprise features require contacting the team directly.

What it can do

• Automatic metrics to measure performance and robustness of LLM applications
• Synthetic generation of diverse evaluation data customised to requirements
• Online monitoring to evaluate quality of LLM applications in production
• Custom metrics created with simple decorators or a library of available metrics
• Built-in dataset management and result tracking
• Experiments-first workflow for evaluating changes consistently

Works with

• LangChain
• LlamaIndex

Before you choose

• Metrics are LLM-driven, so evaluation results depend on the LLM used.
• Enterprise features and collaborations are not self-serve; the site directs users to email the team or talk to the founders.
• Support questions are directed to the Discord community, not a formal support channel described in the pages.

Listed under

• Coding
• Code review and testing
• Developer tools
• LLM evaluation
• Monitoring
• Open source
• RAG evaluation
• Synthetic data

Get started

Run pip install ragas, then follow the quickstart guide.

1. Install the package with pip install ragas.
2. Follow the quickstart guide to run your first evaluation.
3. Define or select metrics and prepare an evaluation dataset for your application.

No public pricing was confirmed. Enterprise features require contacting the founders by email; their cost was not stated.

We couldn’t find published prices for Ragas.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Ragas ↗docs.ragas.io
• Ragas ↗www.ragas.io
• Ragas ↗docs.ragas.io

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ragas.io: product, pricing and policy pages

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Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.