langfuse vs openlit
Side-by-side comparison of two AI agent tools
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
openlitopen-source
Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers,
Metrics
| langfuse | openlit | |
|---|---|---|
| Stars | 24.1k | 2.3k |
| Star velocity /mo | 1.6k | 30 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.7946422085456898 | 0.6589614982537508 |
Pros
- +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
- +OpenTelemetry 原生支持，厂商中立，可与现有可观测性工具无缝集成
- +一行代码集成，提供从 LLM 到 GPU 的全栈监控能力
- +功能丰富的一体化平台，包含监控、评估、提示词管理、实验场地等完整工具链
Cons
- -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
- -作为综合性平台，对于简单用例可能过于复杂
- -开源项目需要自行部署和维护基础设施
Use Cases
- •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
- •LLM 应用的性能监控和成本跟踪
- •多 LLM 提供商的实验和对比测试
- •AI 开发工作流的统一管理和版本控制