Kelet: Root cause analysis agent for debugging AI applications

What it does

Kelet is a debugging tool for AI agents and large language model applications in production. It automatically analyzes traces and signals—user feedback, edits, clicks, sentiment scores, and LLM-as-a-judge outputs—to identify failure patterns across hundreds or thousands of sessions. Rather than requiring engineers to manually scroll through traces, Kelet clusters failures, extracts root causes with supporting evidence, and generates prompt patches ready to deploy. It also measures before-and-after reliability when patches are applied to real sessions.

Who it is for

Kelet targets teams building and maintaining AI agents and LLM applications they own and control. This includes agents built with frameworks like LangChain, LangGraph, CrewAI, PydanticAI, Mastra, and others, as well as applications calling OpenAI, Anthropic, or Gemini APIs directly. The tool works with agentic loops, multi-step workflows, RAG pipelines, chatbots, and autonomous agents. It is not designed for teams using third-party AI tools built by others.

Pricing

The site does not show prices.

How it stands out

Kelet frames its value against a common workflow: agent failure, manual trace inspection, guessed fix, repeat. The founders claim this process consumes roughly 30 percent of engineering time. The product automates investigation across production sessions rather than adding another dashboard. According to the homepage, the median time from trace ingestion to generated prompt patch is 14.3 minutes. In a pilot cohort, 73 percent of teams discovered failures they had not previously noticed. The tool integrates with existing infrastructure—OpenTelemetry, Langfuse, Mixpanel, PostHog, and the major LLM APIs—so setup requires no infrastructure replacement.

What a founder should check

A founder considering this space should verify three things. First, confirm whether the core pain—manual root cause analysis in production AI systems—is widespread enough and time-consuming enough to justify a dedicated tool, or whether existing observability platforms are absorbing this use case. Second, understand the switching cost: teams may have invested heavily in custom logging and tracing around their agents, making integration friction a barrier even with quick onboarding. Third, examine the moat. The product's advantage rests on pattern-matching and RCA automation across traces; check whether this capability remains defensible as observability platforms add native AI debugging, or whether the logic behind root cause identification and patch generation is difficult to replicate.

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