Which is more popular, OpenLLMetry or phoenix?

phoenix has more GitHub stars (11,689 vs 7,467).

Which is more actively developed, OpenLLMetry or phoenix?

phoenix had more commits in the last 90 days (1,198 vs 12).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

phoenix is growing faster: +415 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 phoenix for: aI Observability & Evaluation.

From GitHub data refreshed daily.

Open-source observability for your GenAI or LLM application, based on OpenTelemetry

AI Observability & Evaluation

+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

-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

•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

Which is more popular, OpenLLMetry or phoenix?: phoenix has more GitHub stars (11,689 vs 7,467).
Which is more actively developed, OpenLLMetry or phoenix?: phoenix had more commits in the last 90 days (1,198 vs 12).
Should I use OpenLLMetry or phoenix?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.