Which is more popular, AgentOps or langwatch?

AgentOps has more GitHub stars (5,870 vs 4,908).

Which is more actively developed, AgentOps or langwatch?

langwatch had more commits in the last 90 days (1,587 vs 0).

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 +73 for AgentOps.
Pick AgentOps for: python SDK for monitoring, cost tracking, benchmarking, and debugging AI agents. Pick langwatch for: the platform for LLM evaluations and AI agent testing.

From GitHub data refreshed daily.

AgentOpsopen-source

Python SDK for monitoring, cost tracking, benchmarking, and debugging AI agents

The platform for LLM evaluations and AI agent testing

+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

+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

-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

-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

•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

•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

Which is more popular, AgentOps or langwatch?: AgentOps has more GitHub stars (5,870 vs 4,908).
Which is more actively developed, AgentOps or langwatch?: langwatch had more commits in the last 90 days (1,587 vs 0).
Should I use AgentOps or langwatch?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.