ragas

What it is and what it does

Ragas is an evaluation framework designed to measure and improve Large Language Model applications through objective, data-driven assessment. It provides both LLM-based metrics (like AspectCritic for semantic evaluation) and traditional metrics, alongside automated test dataset generation to cover diverse scenarios. The framework integrates tightly with LangChain and OpenAI, making it natural to embed into existing LLM pipelines.

The package addresses the core problem of subjective, time-consuming LLM evaluation by offering repeatable, quantifiable metrics and the ability to generate test cases from production data. It's built on a foundation of 19 runtime dependencies—including numpy, datasets, pydantic, langchain, and openai—which means it expects a fairly complete LLM development environment. The framework is actively maintained and widely used (top 5000 PyPI packages by downloads), though it carries two known security vulnerabilities that warrant review before deployment.

Use it for:

• Evaluate RAG system accuracy and relevance by scoring retrieval quality and answer correctness against ground truth.
• Generate synthetic test datasets covering edge cases and diverse input scenarios for LLM applications without manual labeling.
• Measure summary quality, factual accuracy, and semantic coherence of model outputs using LLM-based metrics.
• Build feedback loops from production LLM outputs to identify and fix systematic failures in real-time.
• Benchmark and compare different LLM configurations or prompt variations using consistent, objective metrics.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Ragas provides objective metrics, test data generation, and evaluation workflows for Large Language Model applications, integrating with frameworks like LangChain and OpenAI.

Yes, with conditions. Ragas is actively maintained, permissively licensed, and well-suited for teams building production LLM applications who need objective evaluation beyond manual review. The low install friction and integration with LangChain make it straightforward to adopt. However, verify the two known vulnerabilities (GHSA-95ww-475f-pr4f, PYSEC-2026-3046) for your threat model, and be aware that the 19 runtime dependencies will add significant size to your environment. Best for projects where LLM evaluation is a core requirement, not a lightweight add-on.

Install

ragas on PyPI

pip install ragas

uv add ragas

poetry add ragas

Installing ragas

Before you install

Low install friction with a pure-Python wheel. Actively maintained with recent commits and 15313 GitHub stars. However, it carries 19 runtime dependencies including heavy packages like langchain, openai, and datasets, which may increase your environment footprint.

License in practice

Apache License 2.0 is permissive—you can use, modify, and distribute ragas freely in commercial and private projects without copyleft obligations, though you must include license and copyright notices.

Quickstart

pip install ragas

from ragas.metrics.collections import AspectCritic
from ragas.llms import llm_factory

llm = llm_factory("gpt-4o")
metric = AspectCritic(
    name="summary_accuracy",
    definition="Verify if the summary is accurate.",
    llm=llm
)
score = await metric.ascore(
    user_input="text to evaluate",
    response="model response"
)

Requires OPENAI_API_KEY environment variable set and an active OpenAI account for LLM-based metrics.

Verify before relying

• Whether all 19 runtime dependencies are required for basic evaluation workflows or if many are optional.
• Details on the two known vulnerabilities (GHSA-95ww-475f-pr4f, PYSEC-2026-3046) and their impact on evaluation workloads.
• Performance characteristics and latency of metric computation at scale.

Package facts

Evidence: ragas-0.4.3-py3-none-any.whl

Tags

More Artificial Intelligence packages

Further reading