evals vs langfuse
Side-by-side comparison of two AI agent tools
evalsfree
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
langfuseopen-source
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
Metrics
| evals | langfuse | |
|---|---|---|
| Stars | 18.1k | 24.1k |
| Star velocity /mo | 112.5 | 1.6k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.45232807025620575 | 0.7946422085456898 |
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
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
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
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
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
- •Production LLM application monitoring to track performance, costs, and identify issues in real-time
- •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- •LLM evaluation and testing to measure model performance across different datasets and use cases