Kelet

Kelet helps developers find and fix the failures in AI agents and LLM apps that do not throw an obvious error but still produce wrong or unhelpful results. It works by analyzing execution traces and user feedback from your live system, forming hypotheses about why individual runs went wrong, and then clustering similar failures across many sessions. That clustering is the key step: instead of chasing one-off bugs, Kelet surfaces the systematic root causes behind recurring problems and pairs them with suggested solutions so a team can act quickly. Because it runs continuously in production rather than only during pre-release testing, it keeps monitoring behavior as real users interact with the app. The tool is aimed at developers and AI engineers responsible for keeping agents and LLM-powered features reliable once they are shipped. To get value from it, you need to instrument your agents so traces and feedback flow into Kelet, and its clustering is most powerful when there is enough volume for patterns to emerge, so very low-traffic apps will see fewer grouped insights. For teams operating agents at meaningful scale, it turns scattered, silent failures into a prioritized list of causes and fixes.

AI engineers use Kelet to detect and diagnose silent failures in production LLM apps and agents, clustering failure patterns into root causes with suggested fixes.