{"name":"Ragas: Supercharge Your LLM Application Evaluations","description":"Ragas is an ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. It offers objective metrics, intelligent test generation, and data-driven insights to move beyond subjective assessments. This framework helps developers build feedback loops and continuously improve their LLM applications.","github":"https://github.com/vibrantlabsai/ragas","url":"https://osrepos.com/repo/vibrantlabsai-ragas","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/vibrantlabsai-ragas","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/vibrantlabsai-ragas.md","json":"https://osrepos.com/repo/vibrantlabsai-ragas.json","topics":["evaluation","llm","llmops","Python","AI","Machine Learning","LLM Evaluation","Open Source"],"keywords":["evaluation","llm","llmops","Python","AI","Machine Learning","LLM Evaluation","Open Source"],"stars":null,"summary":"Ragas is an ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. It offers objective metrics, intelligent test generation, and data-driven insights to move beyond subjective assessments. This framework helps developers build feedback loops and continuously improve their LLM applications.","content":"## Introduction\n\nRagas is an open-source framework designed to help you evaluate and optimize your Large Language Model (LLM) applications. It provides objective metrics, intelligent test generation, and data-driven insights, moving beyond subjective assessments. With Ragas, you can streamline your evaluation workflows and build robust, high-performing LLM applications.\n\n## Installation\n\nTo get started with Ragas, you can install it directly from PyPI:\n\nbash\npip install ragas\n\n\nAlternatively, you can install it from the source:\n\nbash\npip install git+https://github.com/vibrantlabsai/ragas\n\n\n## Examples\n\nRagas offers a quickstart command to set up example projects:\n\nbash\n# List available templates\nragas quickstart\n\n# Create a RAG evaluation project\nragas quickstart rag_eval\n\n# Specify where you want to create it.\nragas quickstart rag_eval -o ./my-project\n\n\nYou can also evaluate your LLM application using pre-built metrics. Here's an example using `DiscreteMetric` to assess summary accuracy:\n\npython\nimport asyncio\nfrom openai import AsyncOpenAI\nfrom ragas.metrics import DiscreteMetric\nfrom ragas.llms import llm_factory\n\n# Setup your LLM\nclient = AsyncOpenAI()\nllm = llm_factory(\"gpt-4o\", client=client)\n\n# Create a custom aspect evaluator\nmetric = DiscreteMetric(\n    name=\"summary_accuracy\",\n    allowed_values=[\"accurate\", \"inaccurate\"],\n    prompt=\"\"\"Evaluate if the summary is accurate and captures key information.\\n\\nResponse: {response}\\n\\nAnswer with only 'accurate' or 'inaccurate'.\"\"\"\n)\n\n# Score your application's output\nasync def main():\n    score = await metric.ascore(\n        llm=llm,\n        response=\"The summary of the text is...\"\n    )\n    print(f\"Score: {score.value}\")  # 'accurate' or 'inaccurate'\n    print(f\"Reason: {score.reason}\")\n\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n\n\nMake sure your `OPENAI_API_KEY` environment variable is set. For more details, refer to the [Quickstart Guide](https://docs.ragas.io/en/latest/getstarted/quickstart){:target=\"_blank\"}.\n\n## Why Use Ragas?\n\nRagas stands out as a crucial tool for LLM development due to several key features:\n\n*   **Objective Metrics:** It allows you to evaluate LLM applications with precision using both LLM-based and traditional metrics, moving away from subjective assessments.\n*   **Test Data Generation:** Ragas can automatically create comprehensive test datasets, covering a wide range of scenarios, even when you don't have a test dataset ready.\n*   **Seamless Integrations:** It works flawlessly with popular LLM frameworks like LangChain and major observability tools, fitting easily into your existing workflows.\n*   **Build Feedback Loops:** Leverage production data to continually improve your LLM applications, ensuring continuous optimization and better performance.\n\n## Links\n\nExplore Ragas further through these official resources:\n\n*   [Documentation](https://docs.ragas.io/){:target=\"_blank\"}\n*   [Join Discord Community](https://discord.gg/5djav8GGNZ){:target=\"_blank\"}\n*   [Ragas Blog](https://blog.ragas.io/){:target=\"_blank\"}\n*   [Newsletter](https://newsletter.ragas.io/){:target=\"_blank\"}\n*   [Careers](https://www.ragas.io/careers){:target=\"_blank\"}","metrics":{"detailViews":1,"githubClicks":0},"dates":{"published":null,"modified":"2026-08-09T15:54:23.000Z"}}