Which is more popular, langwatch or OpenLLMetry?

OpenLLMetry has more GitHub stars (7,467 vs 4,908).

Which is more actively developed, langwatch or OpenLLMetry?

langwatch had more commits in the last 90 days (1,587 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

langwatch is growing faster: +275 GitHub stars in the last 30 days vs +80 for OpenLLMetry.
Pick langwatch for: the platform for LLM evaluations and AI agent testing. Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry.

From GitHub data refreshed daily.

The platform for LLM evaluations and AI agent testing

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

+End-to-end agent simulation capabilities that test against full stack including tools, state, and user interactions with detailed failure analysis
+Open standards approach with OpenTelemetry/OTLP support ensuring no vendor lock-in and framework-agnostic compatibility
+Integrated workflow combining tracing, evaluation, prompt optimization, and monitoring in a single platform eliminating tool sprawl

+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

-As a specialized platform, may require learning curve and setup time for teams new to LLM evaluation workflows
-Self-hosting option available but may require infrastructure management for teams preferring on-premises deployment

-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

•Regression testing of AI agents before production deployment using realistic scenario simulations to identify breaking points
•Production monitoring and observability of LLM-powered applications with detailed tracing and performance evaluation
•Collaborative prompt engineering and optimization with domain expert annotations and version control integration

•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, langwatch or OpenLLMetry?: OpenLLMetry has more GitHub stars (7,467 vs 4,908).
Which is more actively developed, langwatch or OpenLLMetry?: langwatch had more commits in the last 90 days (1,587 vs 12).
Should I use langwatch or OpenLLMetry?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.