Ragas – Open-source library for evals and testing RAG systems
Ragas is an open-source library designed for evaluating and testing RAG (Retrieval-Augmented Generation) and other LLM applications. It offers a diverse set of metrics and methods, including synthetic test data generation, to help you assess your RAG applications. Ragas was initially developed to address our own needs for evaluating RAG chatbots last year. ### Problems Ragas Can Solve: - How can you select the best components for your RAG, such as the retriever, reranker, and LLM? - How can you create a test dataset without incurring significant expenses and time? We believe there's a need…
In plain words
Ragas is an open-source library for evaluating and testing RAG (Retrieval-Augmented Generation) and other LLM applications. It provides metrics and methods to assess application performance, including synthetic test data generation, helping developers select optimal components like retrievers and rerankers without significant time or cost investment. The library targets developers building LLM applications who need standardized evaluation approaches adapted from traditional machine learning practices.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Ragas is an open-source library designed for evaluating and testing RAG (Retrieval-Augmented Generation) and other LLM applications. It offers a diverse set of metrics and methods, including synthetic test data generation, to help you assess your RAG applications. Ragas was initially developed to address our own needs for evaluating RAG chatbots last year. ### Problems Ragas Can Solve: - How can you select the best components for your RAG, such as the retriever, reranker, and LLM? - How can you create a test dataset without incurring significant expenses and time? We believe there's a need for an open-source standard for evaluating and testing LLM applications. Our vision is to establish this standard for the community. We're addressing this challenge by adapting ideas from the traditional ML lifecycle for LLM applications. ### ML Testing Evolved for LLM Applications Ragas is founded on the principles of metrics-driven development. Our goal is to develop and innovate techniques inspired by the latest research to address the challenges in evaluating and testing LLM applications. We don't think that merely building a sophisticated tracing tool will solve the evaluation and testing challenges. Instead, we aim to tackle these issues from a foundational level. To this end, we're introducing methods such as automated synthetic test data curation, metrics, and feedback utilization. These approaches are inspired by lessons learned from deploying stochastic models throughout our careers as machine learning engineers. While our current focus is on RAG pipelines, we intend to expand Ragas to test a broad spectrum of compound systems. This includes systems based on RAGs, agentic workflows, and various transformations. ### Try Ragas Experience Ragas by trying it out in Google Colab [here](https://colab.research.google.com/github/shahules786/openai-...). For more information, read our [documentation](https://docs.ragas.io/). We would love to hear feedback from the Hacker News community :)
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2024
the whole month →
- 3Y
Life & fun · 2024 · github.com
Microlaunch▲1,116Launch and get feedback on both the idea and product
Dev tools · 2024 · microlaunch.net
