Ragas

Ragas is an open-source Python framework for evaluating RAG pipelines and, increasingly, broader LLM and agent applications, using metrics such as faithfulness, answer relevancy, and context precision/recall. It uses LLMs to score outputs and can generate synthetic test sets, making evaluation practical without full manual labeling. It is free and open source, and is often paired with observability tools for dashboards.

Ragas (RAG Assessment) began as a focused, research-backed way to measure RAG quality without hand-labeling every example. Its core metrics decompose a RAG system into retriever and generator quality: faithfulness (is the answer grounded in retrieved context), answer relevancy, and context precision/recall (did retrieval surface the right information). It uses LLMs to score these dimensions and can generate synthetic test sets, which makes it practical to evaluate and monitor pipelines and to wire evaluation into CI/CD. Originally released in 2023 by Exploding Gradients and described in the Ragas paper, the framework has evolved well beyond pure RAG. Recent versions (the 0.2+ line, with 0.4 in late 2025) expanded to cover general LLM applications and agentic workflows, broadening its metric set while keeping the same evaluation-first philosophy. It is Python-based, integrates with common frameworks and orchestration tools, and is widely adopted in the open-source RAG community. Ragas is primarily an open-source library rather than a large commercial platform, which makes it lightweight and free to adopt, though it means you assemble your own dashboards and infrastructure around it (or pair it with an observability tool). As with all LLM-as-a-judge evaluation, the reliability of scores depends on the judge model and configuration, so results should be validated against your own expectations.