Ragas

Spec sheet

Purpose

Ragas is a library for systematically evaluating AI and large language model applications.Read 5 Oct 2026 · docs.ragas.io

Product

Ragas is a library for systematically evaluating large language model applications.Read 4 Oct 2026 · docs.ragas.io

Integrations

The documentation lists integrations with Arize, LangSmith, Amazon Bedrock, Google Gemini, OCI Gen AI, LangChain, LangGraph, LlamaIndex, and other frameworks.Read 4 Oct 2026 · docs.ragas.io

Security

The opened product and documentation pages did not state security certifications or compliance details.Read 4 Oct 2026 · ragas.io

Support

The Ragas site directs users with questions to its Discord questions chatroom and gives an email for enterprise features and collaborations.Read 5 Oct 2026 · ragas.io

Maker

The product site identifies founders Shahul and Jithin James and gives Vibrant Labs email addresses; it does not state headquarters or founding year.Read 4 Oct 2026 · ragas.io

Custom metrics

Users can create custom metrics tailored to their use case.Read 4 Oct 2026 · docs.ragas.io

Customization

Ragas documents options to customize metrics, test set generation, prompts, and model configuration.Read 5 Oct 2026 · docs.ragas.io

Enterprise

The product site invites inquiries about enterprise features and collaborations by email or a founder meeting.Read 4 Oct 2026 · ragas.io

Evaluation coverage

Documented metrics cover RAG, agent and tool use, factual correctness, SQL, and other tasks.Read 5 Oct 2026 · docs.ragas.io

Evaluation workflow

Ragas supports experiments to evaluate application changes, observe results, and iterate.Read 5 Oct 2026 · docs.ragas.io

Experiments

Its experiments-first approach lets users run evaluations, observe results, and iterate on application changes.Read 4 Oct 2026 · docs.ragas.io

Framework integrations

Documented framework integrations include LangChain, LlamaIndex, Haystack, Griptape, and others.Read 5 Oct 2026 · docs.ragas.io

Install

The quickstart shows installation with uvx or pip and project dependencies with uv or pip.Read 4 Oct 2026 · docs.ragas.io

Installation

Ragas can be installed with pip or from its GitHub main branch.Read 5 Oct 2026 · docs.ragas.io

Metrics

Users can use available evaluation metrics or create custom metrics.Read 5 Oct 2026 · docs.ragas.io

Model providers

The quick start documents use with OpenAI, Anthropic, Google Gemini, Ollama, and OpenAI-compatible providers.Read 5 Oct 2026 · docs.ragas.io

Platform format

The installation instructions describe installing the Python package and do not list desktop or mobile apps.Read 5 Oct 2026 · docs.ragas.io

Production monitoring

The product site describes online monitoring to evaluate LLM application quality in production.Read 4 Oct 2026 · ragas.io

Provider choice

The quickstart documents OpenAI, Anthropic Claude, Google Gemini, local Ollama models, and custom providers for evaluation.Read 4 Oct 2026 · docs.ragas.io

Results

The quickstart says evaluations display results in the console and save them as CSV in the experiments directory.Read 4 Oct 2026 · docs.ragas.io

Synthetic data

The product site says Ragas can synthetically generate diverse evaluation data customized for user requirements.Read 4 Oct 2026 · ragas.io

Test data

Ragas provides tools for generating synthetic test data, including test sets for RAG and agents.Read 5 Oct 2026 · docs.ragas.io

Tracing integrations

Ragas documents integrations with Arize Phoenix and LangSmith for tracing evaluator LLM calls.Read 5 Oct 2026 · docs.ragas.io

Where Specifiction places it

• LLM Evaluation Tools#23 of 297.2
• Llm Developer Tools#22 of 267.1
• AI LLM Evaluation Tools#26 of 307.2
• LLM Observability Tools#25 of 267.1