Ragas

Ragas is an open-source evaluation framework purpose-built for LLM applications, with a strong focus on retrieval-augmented generation (RAG) pipelines. It provides a suite of automated metrics that measure faithfulness, answer relevancy, context precision, and more — enabling teams to objectively assess and improve their AI systems. Ragas integrates with popular LLM frameworks and supports both unit-test-style evaluations and continuous monitoring in production. It is widely used by AI engineers and researchers who need reliable, reproducible quality signals for their LLM-powered products.

• RAG Evaluation Metrics: Automatically score RAG pipelines on faithfulness, answer relevancy, context recall, context precision, and more using reference-free and reference-based metrics.
• LLM-as-a-Judge: Leverage LLMs to evaluate generated outputs against ground truth or without reference, reducing the need for manual annotation.
• Test Dataset Generation: Synthetically generate evaluation datasets from your documents to bootstrap testing without manual labeling.
• Integration with LLM Frameworks: Works seamlessly with LlamaIndex, LangChain, and other popular orchestration frameworks to evaluate pipelines end-to-end.
• CI/CD-Ready Evaluations: Run evaluations as part of automated pipelines to catch regressions before they reach production.
• Observability & Monitoring: Track evaluation metrics over time to monitor model and pipeline quality in production environments.
• Customizable Metrics: Define and extend custom metrics tailored to your specific use case and domain requirements.
• Open Source: Freely available on GitHub, with an active community and transparent development.

To get started, install Ragas via pip, connect it to your LLM provider, and run evaluations on your RAG pipeline outputs using the built-in metric suite or your own custom metrics.