OpenLLMetry vs Opik
Side-by-side comparison of two AI agent tools
Short answer
- Opik is growing faster: +606 GitHub stars in the last 30 days vs +80 for OpenLLMetry.
- Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry. Pick Opik for: debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive.
From GitHub data refreshed daily.
OpenLLMetryopen-source
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Opikopen-source
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Metrics
| OpenLLMetry | Opik | |
|---|---|---|
| Stars | 7.5k | 22.3k |
| Star velocity /mo | 80.36842105263159 | 606 |
| Commits (90d) | 12 | 1.1k |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | — | 1.9M |
| Overall score | 0.5912367252217405 | 0.8395960884989896 |
Pros
- +Built on OpenTelemetry standard with official semantic conventions integration, ensuring compatibility with existing observability infrastructure
- +Open-source with strong community support (6,900+ GitHub stars) and active development backed by Y Combinator
- +Multi-language support covering both Python and JavaScript/TypeScript ecosystems for broad developer adoption
- +提供端到端的 AI 应用可观测性，包括详细的链路追踪和性能监控，帮助开发者快速定位问题
- +支持自动化评估和优化，能够自动改进提示词和工具配置，降低手动调优的工作量
- +完全开源且拥有活跃社区支持，提供灵活的部署选项和定制化能力
Cons
- -Requires familiarity with OpenTelemetry concepts and infrastructure setup, which may have a learning curve for teams new to observability
- -As a specialized tool for LLM observability, it may be overkill for simple AI applications or proof-of-concepts
- -作为相对较新的工具，可能在某些企业级功能和集成方面还需要进一步完善
- -学习曲线可能较陡，需要开发者具备一定的 AI 应用开发和监控经验
Use Cases
- •Production LLM application monitoring to track performance metrics, token usage, and error rates across different models and providers
- •Debugging complex GenAI workflows by tracing requests through multiple AI services and identifying bottlenecks or failures
- •Cost optimization and performance analysis of AI applications to understand usage patterns and optimize model selection
- •RAG 聊天机器人的性能监控和优化，追踪检索质量和回答准确性
- •代码助手应用的链路分析，监控代码生成质量和响应时间
- •复杂智能体工作流的调试和评估，跟踪多步骤推理过程的执行效果
FAQ
- Which is more popular, OpenLLMetry or Opik?
- Opik has more GitHub stars (22,349 vs 7,467).
- Which is more actively developed, OpenLLMetry or Opik?
- Opik had more commits in the last 90 days (1,062 vs 12).
- Should I use OpenLLMetry or Opik?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.