agents-cli vs Ragas
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. — versus — Evaluation toolkit for your RAG and agent pipelines — faithfulness, relevance, and more.
agents-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.
| agents-cli | Ragas | |
|---|---|---|
| Stars | 5.2k | 15k |
| Forks | 542 | 1.6k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 7 days ago | 4 months ago |
| Topics | coding, evals, skills | evals, rag |
| Curated connections | 5 | 5 |
agents-cli — the curator's take
Reach for agents-cli when you're building and shipping agents ON Google Cloud / Gemini Enterprise and want one CLI for scaffold → eval → deploy, driven through a coding agent you already use. NOT the pick if you're not on Google Cloud — deploy targets and publish are GCP/Gemini-bound — or if you want a portable, vendor-neutral framework to embed in your own app; a framework like LangGraph or CrewAI fits that better.
Ragas — the curator's take
Evaluation toolkit for your RAG and agent pipelines — faithfulness, relevance, and more.