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Kelet

Continuously diagnoses why LLM apps and agents fail in production and generates the prompt patch

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freemiumStarter is $0 forever: 500 sessions/month, 15-day data retention, full root-cause analysis, prompt patch generation, OTEL and Langfuse integration, and community support. Startup is listed at $400/month but is free during early access — 5,000 sessions included per month with pay-per-session above that, 30-day retention and email support. Enterprise is custom with unlimited sessions, custom retention and integrations, SSO/SAML, an SLA and dedicated support. Human signals and feedback collection are free forever on every tier. No credit card required to start; the vendor states 30 days' notice before pricing changes and grandfathering for early users.View full pricing →

About Kelet

Kelet is root-cause analysis for LLM applications and AI agents in production. Observability tools of the tracing generation are good at showing you that a run failed and what the spans were; Kelet's premise is that the expensive part is the next step — reading a haystack of traces to work out why the agent went wrong and what to change. It ingests from the stack teams already have, with OpenTelemetry and Langfuse integrations plus support for OpenAI, Anthropic, LangChain, CrewAI, Mastra, Agno, Strands, Pydantic AI, Google ADK, the Vercel AI SDK, PostHog and Mixpanel, then continuously diagnoses failures and generates a prompt patch rather than a dashboard. Human signals and feedback collection are free on every tier, which is a deliberate choice: the labels that make failure classification useful are exactly the thing most vendors put behind the paywall, so gating them would break the product for the teams most likely to adopt it. Billing is per session, where a session is one unit of agent work — a conversation, a task run or a pipeline execution — and the free tier covers 500 a month with 15-day retention. The startup tier is $400/month but free during early access with 30 days' notice before pricing changes and grandfathering for early users, so the honest read is that its real economics are not yet proven in the wild.

Key Features

Automatic root-cause analysis on production agent failures
Generates a prompt patch rather than only surfacing traces
OpenTelemetry and Langfuse ingestion
Integrations across LangChain, CrewAI, Mastra, Agno, Strands and Pydantic AI
Human signals and feedback collection free on every tier
Per-session billing tied to a unit of agent work

Kelet Pros & Cons

Pros

  • +Full RCA on the free tier rather than a teaser
  • +Feedback collection unpaywalled, which is where trace labelling usually dies
  • +Session-based billing maps to work done, not tokens burned

⚠️ Cons

  • $400/month startup tier is steep once early access ends
  • 500 sessions on free is a few days of real production traffic
  • Early-access pricing means the eventual bill is unproven

Tags

observabilityagentsroot-cause-analysisopentelemetryllm-opsdebugging
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