OpenAI Evals vs Ragas
Side-by-side comparison of two AI agent tools
Short answer
- Ragas has had no commit in 7 months; OpenAI Evals is actively maintained.
- Ragas is growing faster: +440 GitHub stars in the last 30 days vs +230 for OpenAI Evals.
- Pick OpenAI Evals for: evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks. Pick Ragas for: supercharge Your LLM Application Evaluations.
From GitHub data refreshed daily.
OpenAI Evalsfree
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Ragasopen-source
Supercharge Your LLM Application Evaluations 🚀
Metrics
| OpenAI Evals | Ragas | |
|---|---|---|
| Stars | 19.5k | 15.9k |
| Star velocity /mo | 230.21052631578948 | 440.3684210526315 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 376 | — |
| Overall score | 0.31275929417567333 | 0.3480794663399632 |
Pros
- +提供完整的LLM评估框架，包含丰富的预置基准测试注册表
- +支持自定义评估开发，可针对特定业务场景和用例进行定制
- +现在可直接在OpenAI Dashboard中运行，也支持本地部署，使用灵活
- +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
- -需要OpenAI API密钥和相关费用，运行评估可能产生不小的成本
- -使用Git-LFS存储评估数据，增加了初始设置的复杂性
- -主要针对OpenAI模型优化，对其他LLM供应商的支持可能有限
- -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
- •测试不同OpenAI模型版本对特定业务工作流程的影响和性能差异
- •为领域特定的LLM应用构建自定义基准测试和评估指标
- •使用企业私有数据创建内部评估套件，而不暴露敏感信息
- •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, OpenAI Evals or Ragas?
- OpenAI Evals has more GitHub stars (19,548 vs 15,913).
- Which is more actively developed, OpenAI Evals or Ragas?
- OpenAI Evals had more commits in the last 90 days (0 vs 0).
- Should I use OpenAI Evals or Ragas?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.