langfuse vs litellm
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
litellmfree
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropi
Metrics
| langfuse | litellm | |
|---|---|---|
| Stars | 23.9k | 41.2k |
| Star velocity /mo | 2.0k | 3.4k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.7561428020148911 | 0.7848129952826897 |
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
- +统一API接口设计，一套代码兼容100多个不同的LLM提供商，大幅简化多模型切换和对比测试
- +内置企业级功能如成本追踪、负载均衡、安全防护栏，为生产环境提供完整的AI治理解决方案
- +既提供Python SDK又提供独立的代理服务器部署模式，适合不同规模和架构的项目需求
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
- -作为中间层抽象，可能无法完全利用某些模型提供商的独特功能和高级参数配置
- -依赖网络连接和第三方API稳定性，增加了系统的复杂度和潜在故障点
- -对于简单的单模型应用场景可能存在过度设计，增加不必要的依赖和学习成本
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
- •AI应用开发中需要对比测试多个LLM模型性能，快速切换不同提供商而无需重写代码
- •企业级AI服务需要统一的成本监控、访问控制和负载均衡管理多个模型调用
- •构建AI代理或聊天机器人时需要根据用户需求和成本考虑动态选择最适合的模型