Which is more popular, agentic-radar or langwatch?

langwatch has more GitHub stars (4,908 vs 1,057).

Which is more actively developed, agentic-radar 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

agentic-radar has had no commit in 10 months; langwatch is actively maintained (1,587 commits in the last 90 days).
langwatch is growing faster: +275 GitHub stars in the last 30 days vs +19 for agentic-radar.
Pick agentic-radar for: a security scanner for your LLM agentic workflows. Pick langwatch for: the platform for LLM evaluations and AI agent testing.

From GitHub data refreshed daily.

A security scanner for your LLM agentic workflows

The platform for LLM evaluations and AI agent testing

+Specialized focus on LLM agentic workflow security vulnerabilities that traditional scanners miss
+Includes built-in visualization tools for clear security assessment reporting and analysis
+Integrates with popular frameworks like CrewAI and provides easy PyPI installation

+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

-Appears to be a relatively new tool with limited documentation visibility from the provided materials
-May require specialized knowledge of agentic systems to effectively interpret and act on scan results

-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

•Security assessment of autonomous AI agent systems before production deployment
•Compliance auditing for organizations using LLM-powered workflows in regulated industries
•Continuous security monitoring of agentic systems to detect emerging vulnerabilities

•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, agentic-radar or langwatch?: langwatch has more GitHub stars (4,908 vs 1,057).
Which is more actively developed, agentic-radar or langwatch?: langwatch had more commits in the last 90 days (1,587 vs 0).
Should I use agentic-radar or langwatch?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.