Which is more popular, Langfuse or langwatch?

Langfuse has more GitHub stars (35,329 vs 4,908).

Which is more actively developed, Langfuse or langwatch?

Langfuse had more commits in the last 90 days (2,013 vs 1,587).

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

Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +275 for langwatch.
Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick langwatch for: the platform for LLM evaluations and AI agent testing.

From GitHub data refreshed daily.

Langfuseopen-source

Open-source LLM engineering platform for observability, evaluation, prompt and dataset management

The platform for LLM evaluations and AI agent testing

+Open source with MIT license allowing full customization and transparency, plus active community support
+Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
+Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK

+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

-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

•Production LLM application monitoring to track performance, costs, and identify issues in real-time
•Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
•LLM evaluation and testing to measure model performance across different datasets and use cases

•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, Langfuse or langwatch?: Langfuse has more GitHub stars (35,329 vs 4,908).
Which is more actively developed, Langfuse or langwatch?: Langfuse had more commits in the last 90 days (2,013 vs 1,587).
Should I use Langfuse or langwatch?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.