explodinggradients

Ragas

Evaluation toolkit for your RAG and agent pipelines — faithfulness, relevance, and more.

★ 14,686⑂ 1,535● Python⚖ Apache-2.0updated 4 months ago

README.md

Ragas provides metrics and tooling to evaluate retrieval-augmented generation and agent pipelines, so you can measure faithfulness, answer relevance and context quality.

pip install ragas

Continue your stack

What teams reach for next — and why each earns a place beside Ragas.

Pairs well with

LlamaIndex

Data framework for connecting custom data sources to LLMs — ingestion, indexing, retrieval.

Why it fitsTurns 'looks right' into a number — score LlamaIndex pipelines before you ship.

AutoGen

Multi-agent conversation framework for building LLM applications with cooperating agents.

Why it fitsScore the outputs of your multi-agent conversations for faithfulness and relevance.

Alternative to

agents-cli

CLI + agent-skills layer that turns your coding assistant into a Google Cloud agent-lifecycle expert: scaffold ADK projects, run and evaluate them, then deploy and publish to Gemini Enterprise.

Why it fitsagents-cli ships a full agent-eval suite — trace generation, metric grading, LLM-as-judge, failure-mode clustering, prompt optimization — which overlaps Ragas's job. Difference: Ragas is eval-only and framework-agnostic, while agents-cli's eval is one stage of a GCP-bound scaffold/deploy pipeline.