Ragas
used in
0
stacks
Open-source Python framework for evaluating RAG pipelines with model-based, largely reference-free metrics.
Originated from the 2023 "RAGAS: Automated Evaluation of Retrieval Augmented Generation" paper. Core metrics include faithfulness, answer relevancy, context precision, and context recall, scored by an LLM judge rather than human-annotated ground truth. Also supports synthetic test set generation and integrates with frameworks such as LangChain and LlamaIndex and observability tools like Langfuse.
Strengths
- Reference-free metrics reduce the need for human-labeled ground truth
- Research-grounded RAG metrics: faithfulness, relevancy, context precision/recall
- Synthetic test set generation for bootstrapping evaluation datasets
- Integrations with LangChain, LlamaIndex, and tracing platforms
Tradeoffs
- LLM-judged scores can be inconsistent across runs and judge models, and add token cost
- Scoped primarily to RAG; weaker coverage for agentic or general LLM app evaluation
- Absolute metric values are hard to interpret without a baseline