{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Ragas provides objective metrics, test data generation, and evaluation workflows for Large Language Model applications, integrating with frameworks like LangChain and OpenAI.","skillfed_tags":["llm-evaluation","rag-testing","quality-assurance"],"use_cases":["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."],"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.\n\nThe 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\u2014including numpy, datasets, pydantic, langchain, and openai\u2014which 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.","worth_installing":"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."},"id":"ragas","links":{"html":"https://skillfed.io/packages/ragas","md":"https://skillfed.io/packages/ragas.md","pypi":"https://pypi.org/project/ragas/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-13","license_spdx":null,"license_treatment":"permissive","name":"ragas","python_support":"supports_current","summary":"Evaluation framework for RAG and LLM applications"},"popularity":{"monthly_downloads":1640682,"position":3696,"tier":"top_5000"},"security":{"n_vulnerabilities":2},"version":"0.4.3"}