openllmetry
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Star Growth
Overview
OpenLLMetry is an open-source observability platform specifically designed for GenAI and LLM applications, built on top of the industry-standard OpenTelemetry framework. With over 6,900 GitHub stars, it provides comprehensive monitoring and visibility into AI applications, allowing developers to track performance, debug issues, and optimize their LLM implementations. The tool's semantic conventions have been officially integrated into OpenTelemetry, making it a standardized approach to LLM observability. OpenLLMetry supports both Python and JavaScript/TypeScript ecosystems, making it accessible to a wide range of developers. As a Y Combinator-backed project, it combines enterprise-grade reliability with open-source flexibility. The platform enables teams to gain deep insights into their AI applications' behavior, token usage, latency patterns, and error rates. This visibility is crucial for production LLM applications where understanding model performance, cost optimization, and user experience are paramount. By leveraging OpenTelemetry's proven infrastructure, OpenLLMetry provides familiar tooling for DevOps teams while addressing the unique observability challenges of AI applications.
Deep Analysis
The OTel-native LLM observability standard — semantic conventions now part of official OpenTelemetry, with widest LLM provider + observability destination coverage
⚡ Capabilities
- • OpenTelemetry-native LLM observability
- • Auto-instrumentation for 15+ LLM providers
- • Vector DB call tracing
- • Framework instrumentation (LangChain, LlamaIndex, CrewAI)
- • Standard OTel semantic conventions for GenAI
- • Multi-destination trace export
- • MCP protocol tracing
🔗 Integrations
✓ Best For
- ✓ Teams needing LLM observability in existing OTel infrastructure
- ✓ Production LLM apps requiring cost/latency monitoring
- ✓ Organizations using multiple LLM providers
✗ Not Ideal For
- ✗ Hobby projects without observability needs
- ✗ Teams wanting all-in-one evaluation + observability (use Agenta/OpenLIT instead)
Languages
Deployment
Pricing Detail
⚠ Known Limitations
- ⚠ Adds some overhead to LLM calls
- ⚠ Requires OpenTelemetry knowledge for advanced config
- ⚠ Dashboard requires Traceloop cloud or self-hosted OTel stack
- ⚠ Some newer LLM providers may lag in instrumentation support
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
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
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