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Ragas

by Vibrant Labs (formerly Exploding Gradients) · Library · also known as RAGAS, ragas (PyPI)

Open-source Python library for evaluating LLM and RAG applications with LLM-driven and custom metrics, experiments, dataset management and result tracking

Apache-2.0 licenseOpen sourceFreeLocalSelf-hostable
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Link verified Nov 2, 2026

Overview

Ragas is an Apache-2.0 Python library for systematic evaluation of LLM applications, originally focused on retrieval-augmented generation and now framed around an experiments workflow: change the application, run an evaluation, review results and iterate. It ships a library of LLM-driven metrics (faithfulness, context precision and recall and answer relevancy are among the best known from its RAG origins) and lets you define custom metrics with simple decorators; built-in dataset management and result tracking keep runs comparable. Integrations cover LangChain and LlamaIndex among other frameworks, and the GitHub repository moved from the explodinggradients organization to vibrantlabsai, reflecting the maintaining company's rename to Vibrant Labs.

Ragas suits teams who want a code-first evaluation harness inside Python rather than a hosted platform; it is commonly paired with a tracing backend such as Langfuse. Because most metrics use an LLM as judge, evaluation incurs model costs and inherits judge biases, and metric definitions have changed between major versions, so results should be compared within one version.

Editorial note on uncertainty. The company rename from Exploding Gradients to Vibrant Labs is inferred from the repository redirect and the docs footer; no announcement page was located. Specific metric names are from the project's documented history rather than the current landing page.

Capabilities

  • Library of LLM-driven evaluation metrics for RAG and agents
  • Custom metrics via decorators
  • Experiments workflow with dataset management and result tracking
  • Integrations with LangChain, LlamaIndex and other frameworks
  • Test-set generation for RAG evaluation

Typical workflows

  1. Score a RAG pipeline on faithfulness and context recall over a golden set
  2. Compare two retriever configurations in an experiment
  3. Log Ragas scores to a tracing platform

Strengths

  • Code-first and framework-agnostic
  • Apache-2.0 license
  • Well-known RAG metric definitions

Limitations

  • LLM-as-judge costs and biases
  • Metric semantics changed across versions
  • Hosted companion product is still emerging

Prerequisites

  • Python 3.9+
  • An LLM provider for judge metrics

Who it is for

  • Developers evaluating RAG systems
  • Researchers benchmarking retrieval pipelines

Alternatives & comparison

  • Braintrust

    Hosted observability and evaluation platform for AI agents with tracing, experiments, pattern detection, a CLI, MCP access and an AI assistant (Loop) that analyzes traces and builds evals

    MixedFreemium
  • DSPy

    Python framework for programming language-model pipelines with typed signatures and modules, then optimizing prompts and weights automatically against a metric

    Open sourceOpen source
  • Langfuse

    Open-source LLM observability and evaluation platform: tracing, LLM-as-judge and human evals, prompt management, datasets and cost dashboards, self-hostable or as Langfuse Cloud

    Open sourceOpen source
  • LangSmith

    LangChain's hosted platform for tracing, evaluating and deploying LLM applications and agents, with prompt tooling, an LLM gateway and cloud, hybrid or self-hosted options

    ProprietaryFreemium
  • LlamaIndex

    Open-source Python framework for retrieval-augmented agents over your data, with connectors, indexes, query engines and event-driven workflows, alongside the commercial LlamaParse document platform

    Open sourceOpen source
  • promptfoo

    Open-source, local-first CLI and library for evaluating prompts, models, RAG pipelines and agents, with automated red teaming and CI integration

    MixedOpen source
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Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • Official documentation
    Ragas documentation

    https://docs.ragas.io/en/stable

    Supports: capabilities, integrations, vendor

    Links to Vibrant Labs as the maintaining company

    checked Nov 1, 2026
  • Official site
    Ragas

    https://www.ragas.io/

    Supports: name, summary

    checked Nov 1, 2026
  • Repository
    https://github.com/vibrantlabsai/ragas

    Supports: license_spdx, languages

    github.com/explodinggradients/ragas redirects here; repository metadata reports Apache-2.0

    checked Nov 1, 2026
Confidence
Medium
Reviewed
Nov 1, 2026
Links
Broken · Nov 2, 2026

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