AgentOps vs OpenLLMetry
Side-by-side comparison of two AI agent tools
Short answer
- Pick AgentOps for: python SDK for monitoring, cost tracking, benchmarking, and debugging AI agents. Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry.
From GitHub data refreshed daily.
AgentOpsopen-source
Python SDK for monitoring, cost tracking, benchmarking, and debugging AI agents
OpenLLMetryopen-source
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Metrics
| AgentOps | OpenLLMetry | |
|---|---|---|
| Stars | 5.9k | 7.5k |
| Star velocity /mo | 73.26315789473685 | 80.36842105263159 |
| Commits (90d) | 0 | 12 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | 111.1K | — |
| Overall score | 0.2650599984606837 | 0.5912367252217405 |
Pros
- +Comprehensive integration ecosystem supporting major AI frameworks like CrewAI, OpenAI Agents SDK, Langchain, and Autogen
- +Open-source under MIT license with active community development and regular updates
- +Complete observability suite covering monitoring, cost tracking, and benchmarking from prototype to production
- +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
Cons
- -Limited to Python ecosystem, which may not suit developers using other programming languages
- -Requires integration setup with each agent framework, potentially adding complexity to existing workflows
- -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
Use Cases
- •Monitoring production AI agent performance and identifying bottlenecks in agent workflows
- •Tracking and optimizing LLM usage costs across different agent frameworks and models
- •Benchmarking agent performance during development and comparing different agent implementations
- •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
FAQ
- Which is more popular, AgentOps or OpenLLMetry?
- OpenLLMetry has more GitHub stars (7,467 vs 5,870).
- Which is more actively developed, AgentOps or OpenLLMetry?
- OpenLLMetry had more commits in the last 90 days (12 vs 0).
- Should I use AgentOps or OpenLLMetry?
- 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.