An open platform that shows every model call: traces, cost, quality scores and prompt versions. Use it in the cloud or self-host it.
All repositories
Evals & observabilityvibrantlabsai
ragas
A metrics library for evaluating RAG and other LLM apps: it checks how well an answer relies on retrieved sources. Can also generate test sets.
Who it is for
For those building RAG who want to measure quality rather than eyeball it.
How to start
- Install:
pip install ragas. - Create a ready-made project:
ragas quickstart rag_eval -o ./my-project. - Connect your model and compute metrics on your app's answers.
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
+322Sep 6 — Oct 5
Author's description
Supercharge Your LLM Application Evaluations 🚀
More in «Evals & observability»
A long-standing ML platform that now covers LLM apps too: tracing, evaluation, a prompt registry and a model gateway. Plugs into most frameworks.
A command-line tool for testing prompts and agents: describe cases in a config and compare models. Also does red teaming and vulnerability scanning. Plugs into CI.
A platform for debugging and evaluating LLM apps: traces, automated quality checks and dashboards. Supports RAG and agent chains, and can be self-hosted.
OpenAI's framework for evaluating models plus a registry of ready-made benchmarks. A historically important project that shows how eval templates are structured.
A pytest-style framework for testing LLM apps: ready-made metrics for RAG, agents and chatbots, plus trace-based checks. Results can be pushed to a cloud.