ragas
Evaluation framework for RAG and LLM applications
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires OPENAI_API_KEY environment variable set and an active OpenAI account for LLM-based metrics.
- Low install friction with a pure-Python wheel.
- Actively maintained with recent commits and 15313 GitHub stars.
License · maintenance · safety
permissive license (permissive) — 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.
last release 2026-01-13 (213 days) · last repo commit 2026-02-24 · 15,313 stars
2 known vulnerabilities (OSV.dev, 2026-08-14) · 1,640,682 downloads/mo, #3,696 on PyPI
Alternatives
Verify before relying
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"
)- 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.
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 on it.
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
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.
Requires OPENAI_API_KEY environment variable set and an active OpenAI account for LLM-based metrics.
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"
)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
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 19 packagesnumpydatasetstiktokenpydanticnest-asyncioappdirsdiskcachetyperrichopenaitqdminstructorpillownetworkxscikit-networklangchainlangchain-corelangchain-communitylangchain_openai |
| Maintenance | Actively maintained 213 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,640,682 / month, #3,696 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | 2 GHSA-95ww-475f-pr4f, PYSEC-2026-3046 |
Evidence: ragas-0.4.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “llm evaluation metrics”
- ragasRagas provides objective metrics, test data generation, and…
- deepevalDeepEval is an LLM evaluation framework that runs unit tests on…
- opikOpik is an open-source LLM observability and evaluation platform that…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also evidently · trulens · deepeval · cleanlab-tlm · openevals · trulens-core · autoevals · inspect-evals · opik · rubric