promptfoo vs ragas
Side-by-side comparison of two AI agent tools
promptfooopen-source
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and
ragasopen-source
Supercharge Your LLM Application Evaluations 🚀
Metrics
| promptfoo | ragas | |
|---|---|---|
| Stars | 18.6k | 13.1k |
| Star velocity /mo | 1.6k | 1.1k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 8 |
| Overall score | 0.7281076018478292 | 0.6027713470286904 |
Pros
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
- +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
- -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
- -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
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture
- •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