Ragas

Free plan APILinuxSelf-hosted

What Ragas is

Ragas is a free, self-hosted library for systematically evaluating large language model applications. It provides metrics for retrieval-augmented generation, agent and tool workflows, factual correctness, SQL, natural-language comparisons, and other tasks. Users can define custom metrics and tailor test-set generation, prompts, and model configuration. An experiments-first workflow lets teams evaluate application changes, inspect results, and iterate. Ragas can generate synthetic evaluation data, including test sets for RAG and agents, and describes online monitoring for application quality in production. Documented integrations include LangChain, LlamaIndex, Haystack, Griptape, Arize Phoenix, and LangSmith, among others. Evaluation providers include OpenAI, Anthropic Claude, Google Gemini, Ollama, and OpenAI-compatible models. Installation uses Python packages through pip, GitHub, or uvx. Results appear in the console and are saved as CSV in an experiments directory. Support questions go to a Discord questions chatroom.

Who it suits

Ragas suits developers and teams evaluating LLM or AI applications in self-hosted environments. It is useful when custom metrics, synthetic test data, experiments, or production monitoring are needed.

What it does well

Free plan with self-hosted deployment
Metrics cover RAG, agents, SQL, and more
Custom metrics and test-set generation
Integrates with major frameworks and tracing tools
Results saved as CSV

What to know first

Installation describes a Python package
No desktop or mobile app is listed
Support is directed to a Discord chatroom

Limits

Ragas is free and self-hosted. Its documented installation route is a Python package rather than a desktop or mobile app.

Getting started

Install Ragas with pip, from the GitHub main branch, or with uvx
Install project dependencies with pip or uv
Choose an evaluation provider such as OpenAI, Anthropic Claude, Google Gemini, Ollama, or a custom provider
Configure metrics, prompts, test-set generation, and model settings
Run an evaluation experiment
Review console results and CSV files in the experiments directory
Use the Discord #questions chatroom for support

Verdict

Ragas offers a broad evaluation toolkit for LLM applications, from synthetic test data through production monitoring. Its free, self-hosted format fits technical teams comfortable installing and configuring Python tools.

Questions

Is Ragas free?

Yes. Ragas has a free plan.

How is Ragas deployed?

Ragas is self-hosted and documented as a Python package.

Which model providers can it evaluate?

The quickstart documents OpenAI, Anthropic Claude, Google Gemini, local Ollama models, and custom or OpenAI-compatible providers.

What does Ragas integrate with?

Documented integrations include LangChain, LlamaIndex, Haystack, Griptape, Arize Phoenix, and LangSmith, among others.

Where do evaluation results go?

Results are displayed in the console and saved as CSV in the experiments directory.

How can users get support?

The product site directs support questions to its Discord #questions chatroom.

Facts from Ragas's own pages

Customization: Ragas documents options to customize metrics, test set generation, prompts, and model configuration. · docs.ragas.io, 5 Oct 2026
Custom metrics: Users can create custom metrics tailored to their use case. · docs.ragas.io, 4 Oct 2026
Enterprise: The product site invites inquiries about enterprise features and collaborations by email or a founder meeting. · ragas.io, 4 Oct 2026
Evaluation coverage: Documented metrics cover RAG, agent and tool use, factual correctness, SQL, and other tasks. · docs.ragas.io, 5 Oct 2026
Evaluation workflow: Ragas supports experiments to evaluate application changes, observe results, and iterate. · docs.ragas.io, 5 Oct 2026
Experiments: Its experiments-first approach lets users run evaluations, observe results, and iterate on application changes. · docs.ragas.io, 4 Oct 2026
Framework integrations: Documented framework integrations include LangChain, LlamaIndex, Haystack, Griptape, and others. · docs.ragas.io, 5 Oct 2026
Install: The quickstart shows installation with uvx or pip and project dependencies with uv or pip. · docs.ragas.io, 4 Oct 2026
Installation: Ragas can be installed with pip or from its GitHub main branch. · docs.ragas.io, 5 Oct 2026
Integrations: The documentation lists integrations with Arize, LangSmith, Amazon Bedrock, Google Gemini, OCI Gen AI, LangChain, LangGraph, LlamaIndex, and other frameworks. · docs.ragas.io, 4 Oct 2026
Maker: The product site identifies founders Shahul and Jithin James and gives Vibrant Labs email addresses; it does not state headquarters or founding year. · ragas.io, 4 Oct 2026
Metrics: Users can use available evaluation metrics or create custom metrics. · docs.ragas.io, 5 Oct 2026
Model providers: The quick start documents use with OpenAI, Anthropic, Google Gemini, Ollama, and OpenAI-compatible providers. · docs.ragas.io, 5 Oct 2026
Platform format: The installation instructions describe installing the Python package and do not list desktop or mobile apps. · docs.ragas.io, 5 Oct 2026
Product: Ragas is a library for systematically evaluating large language model applications. · docs.ragas.io, 4 Oct 2026
Production monitoring: The product site describes online monitoring to evaluate LLM application quality in production. · ragas.io, 4 Oct 2026
Provider choice: The quickstart documents OpenAI, Anthropic Claude, Google Gemini, local Ollama models, and custom providers for evaluation. · docs.ragas.io, 4 Oct 2026
Purpose: Ragas is a library for systematically evaluating AI and large language model applications. · docs.ragas.io, 5 Oct 2026
Results: The quickstart says evaluations display results in the console and save them as CSV in the experiments directory. · docs.ragas.io, 4 Oct 2026
Security: The opened product and documentation pages did not state security certifications or compliance details. · ragas.io, 4 Oct 2026
Support: The Ragas site directs users with questions to its Discord questions chatroom and gives an email for enterprise features and collaborations. · ragas.io, 5 Oct 2026
Synthetic data: The product site says Ragas can synthetically generate diverse evaluation data customized for user requirements. · ragas.io, 4 Oct 2026
Test data: Ragas provides tools for generating synthetic test data, including test sets for RAG and agents. · docs.ragas.io, 5 Oct 2026
Tracing integrations: Ragas documents integrations with Arize Phoenix and LangSmith for tracing evaluator LLM calls. · docs.ragas.io, 5 Oct 2026