Ragas

What is Ragas?

Ragas is a library for measuring how well an LLM application performs, built for developers who are tired of judging outputs by eye. Its workflow is organized around experiments: you change a prompt, model or retriever, run an evaluation, read the results and repeat. Scores come from LLM-driven metrics, and you can either pick from a library grouped by task, including agents and tool use, SQL, natural-language comparison and general-purpose checks, or write your own with a simple decorator. A test data generator builds evaluation sets for RAG pipelines, with options for personas, single-hop and multi-hop queries, non-English corpora and seeding from production data. The docs also describe dataset management, result tracking, a command line interface, tracing through observability tools, and integrations with LangChain and LlamaIndex. Ragas is open source and the documentation lists no paid plans. The team advertises office hours and an email contact for companies that want help setting up evals.

Use cases

• Evaluate a RAG pipeline with LLM metrics
• Generate synthetic test sets for retrieval
• Benchmark an agent or tool-use workflow
• Create custom evaluation metrics
• Compare prompt or model changes in experiments
• Trace evaluations with observability tools
• Check SQL generation quality

Who it's for

• LLM application developers
• ML engineers
• AI researchers
• QA teams for AI products

Why use Ragas

• Repeatable experiments replace informal spot checks, so you can tell whether a change to your prompt or retriever helped.
• Decorator-based custom metrics let you score the qualities that matter for your own application, not only generic ones.
• Synthetic test set generation saves hand-writing questions when you have documents but no labeled data.
• Integrations with LangChain, LlamaIndex and observability tools fit it into an existing stack.
• Being open source, it costs nothing to adopt, and you control which judge model runs the metrics.

Ragas features

• Experiments-first evaluation workflow
• Library of LLM-driven metrics
• Custom metrics through simple decorators
• Metrics for agents, tool use, SQL and natural-language comparison
• Test data generation with personas and multi-hop queries
• Test data generation from non-English corpora
• Dataset management and result tracking
• Customizable models, timeouts and retries
• Prompt and language adaptation for metrics
• Command line interface

Ragas integrations

• LangChain
• LlamaIndex

How to get started with Ragas

1. Install the library and follow the quickstart, which the docs say takes about five minutes.
2. Choose built-in metrics or write a custom metric with a decorator, and configure the model that judges outputs.
3. Build a dataset by hand or generate a test set from your documents, optionally with personas.
4. Run an experiment against your application and record the metric scores for each example.
5. Change the application, rerun the experiment and compare results to see what improved.

Ragas limitations

• Metrics that use an LLM judge depend on the judge model you configure
• It is a developer library, so setup requires writing Python code
• The documentation lists no hosted service or paid plans

Ragas pricing

Ragas is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Ragas alternatives

The main Ragas alternatives listed on Debuly are TruLens, Promptfoo, Arize Phoenix, Braintrust, Arize AX and Agent Development Kit (ADK). The table compares pricing and platforms; order is A–Z.

1. 

   Agent Development Kit (ADK)

   Google's open-source, code-first Python framework for building, testing and deploying AI agents and multi-agent workflows.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit Agent Development Kit (ADK) (official site)
2. 

   Arize AX

   Observability and evaluation platform for AI agents and LLM apps, with tracing, issue detection and experiments on a free plan.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit Arize AX (official site)
3. 

   Arize Phoenix

   Open-source tool for tracing, evaluating and experimenting on LLM apps and agents, free to self-host or run locally.

   Developer platformsAutomation and agents
   Open sourceVisit Arize Phoenix (official site)
4. 

   Braintrust

   AI observability and evaluation platform for tracing LLM apps and agents, scoring outputs and catching regressions before release.

   Developer platformsCoding
   FreemiumVisit Braintrust (official site)
5. 

   Promptfoo

   Open-source LLM evaluation and red-teaming tool that finds prompt injections, jailbreaks and data leaks in AI apps and agents.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Promptfoo (official site)
6. 

   TruLens

   Open-source, OpenTelemetry-native Python library for tracing AI agents and RAG apps and scoring each step with LLM judges.

   Developer platformsCodingAutomation and agents
   Open sourceVisit TruLens (official site)

Ragas FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from docs.ragas.io · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
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