Opik vs Ragas
Side-by-side comparison of two AI agent tools
Short answer
- Ragas has had no commit in 7 months; Opik is actively maintained (1,062 commits in the last 90 days).
- Pick Opik for: debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive. Pick Ragas for: supercharge Your LLM Application Evaluations.
From GitHub data refreshed daily.
Opikopen-source
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Ragasopen-source
Supercharge Your LLM Application Evaluations 🚀
Metrics
| Opik | Ragas | |
|---|---|---|
| Stars | 22.3k | 15.9k |
| Star velocity /mo | 606 | 440.3684210526315 |
| Commits (90d) | 1.1k | 0 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 1.9M | 998.4K |
| Overall score | 0.8395960884989896 | 0.3480794663399632 |
Pros
- +提供端到端的 AI 应用可观测性，包括详细的链路追踪和性能监控，帮助开发者快速定位问题
- +支持自动化评估和优化，能够自动改进提示词和工具配置，降低手动调优的工作量
- +完全开源且拥有活跃社区支持，提供灵活的部署选项和定制化能力
- +提供客观的LLM应用评估指标，结合智能LLM评估和传统指标，确保评估结果的准确性和可靠性
- +自动生成综合测试数据集功能，覆盖广泛应用场景，解决测试数据不足的问题
- +与LangChain等主流框架深度集成，支持生产环境反馈循环，便于持续优化
Cons
- -作为相对较新的工具，可能在某些企业级功能和集成方面还需要进一步完善
- -学习曲线可能较陡，需要开发者具备一定的 AI 应用开发和监控经验
- -主要依赖Python生态系统，对其他编程语言的支持有限
- -作为相对新兴的工具，社区生态和最佳实践仍在发展中
- -LLM基础评估可能增加计算成本和延迟
Use Cases
- •RAG 聊天机器人的性能监控和优化，追踪检索质量和回答准确性
- •代码助手应用的链路分析，监控代码生成质量和响应时间
- •复杂智能体工作流的调试和评估，跟踪多步骤推理过程的执行效果
- •RAG系统性能评估：评估检索质量、答案准确性和相关性指标
- •聊天机器人质量监控：自动评估对话质量、一致性和用户满意度
- •LLM应用A/B测试：对比不同模型版本或提示策略的性能差异
FAQ
- Which is more popular, Opik or Ragas?
- Opik has more GitHub stars (22,349 vs 15,913).
- Which is more actively developed, Opik or Ragas?
- Opik had more commits in the last 90 days (1,062 vs 0).
- Should I use Opik or Ragas?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.