Your agent is failing. You just don't know where.

Kelet reads your production AI agent traces and signals, clusters failure patterns across thousands of sessions, and surfaces root causes with evidence — so you ship fixes instead of hypotheses. Think of it as a detective that investigates every failure automatically.

Any agent or LLM application where you own the code — agentic loops, multi-step workflows, RAG pipelines, chatbots, autonomous agents. If you built it and you ship it, Kelet can help you improve it. Two situations where Kelet is not the right fit: If you use AI tools built by others (Cursor, Claude Code, Copilot as a developer), you're a user, not a builder — Kelet isn't designed for your use case. Similarly, if you're building a skill or plugin inside an existing agentic platform, you're extending infrastructure you don't control, and Kelet can't instrument that. But if you're building your own agent using any LLM SDK or API — Claude Code SDK, OpenAI, Anthropic, anything — you own that agent, and Kelet is exactly for you.

Five minutes. pip install kelet (or npm install kelet), add two lines to your agent code, and traces start flowing. Kelet is fully OpenTelemetry-compliant — any OTEL-instrumented agent works out of the box, no infrastructure changes needed.

Signals are probabilistic hints that something went wrong in a session: a thumbs-down rating, a user editing AI output, an abandoned conversation, or a synthetic LLM-as-judge check you configure. They tell Kelet where to look in your traces — not verdicts, but clues that guide the investigation.

Those tools show you traces. Kelet reads them for you. Observability platforms are thermometers — they report symptoms. Kelet is the doctor that diagnoses root causes and generates targeted prompt patches. You no longer need to scroll 10,000 traces manually.

Kelet works like a detective. Every session leaves a trail — LLM calls, tool invocations, retrieval steps, every agent hop. Kelet uses signals as clues: a thumbs-down, an edited AI response, an abandoned conversation, a synthetic LLM-judge flag. It follows each thread through your traces, cross-references patterns across thousands of sessions, and builds a root cause hypothesis backed by evidence. Same process a senior engineer would run manually — automated, at scale, on every failure at once.

A solution. Most AI reliability products give engineers better ways to look at failures — cleaner trace viewers, smarter search, labeled clusters. You still do the investigation. Kelet does the investigation for you. You don't streamline your debugging workflow — you replace it. The output isn't data to interpret; it's a root cause and a prompt patch ready to ship.

No. Teams typically see their first real failure patterns with as few as 200+ sessions and 3+ signals configured. Not sure which signals to set up? Kelet's AI walks you through it — no guesswork, no manual configuration. And if you're starting from zero, synthetic signal presets (LLM-as-judge evaluators) generate signal from day one, before real user feedback accumulates.

Yes. Kelet handles multi-agent sessions natively. Credit assignment identifies exactly which agent in a chain caused a failure — so you know what to fix, not just that something is broken.

Yes — Kelet was architected for production scale from day one. The team behind Kelet includes ex-Kubernetes maintainers and cloud-native infrastructure veterans with 15+ years of open-source systems work. Kelet handles millions of traces, concurrent agent fleets, and high-volume production workloads. We have built infrastructure at this scale before — Kelet is built on the same foundations.

Free to start, no credit card required. Connect your first agent in 5 minutes. Usage-based pricing scales with volume for teams that need more. See kelet.ai/pricing for details.

Yes. Kelet is SOC 2 certified. All data is isolated at the database level per organization — strict row-level security, no cross-org data access, ever.

Never. We don't share your data or use it to train public models. What we do: Kelet automatically fine-tunes a private set of models for each sub-agent you connect — roughly a dozen per agent. They live in your account, trained on your traces, serving only your root-cause analysis. They're never shared. Frankly, they wouldn't be useful to anyone else anyway — they're calibrated to your specific agent, not anyone else's.

Kelet was built by a team obsessed with production AI reliability. We come from cloud-native infrastructure, Kubernetes core contributions, and LLM systems — engineers who have spent careers building and operating critical distributed systems, and building the tools others rely on to do the same. We built Kelet because we felt the pain ourselves: thousands of traces, no root cause, no fix. So we built the tool we wished existed.

Our team has spent years maintaining critical infrastructure used by thousands of engineers worldwide — including core contributions to Kubernetes and cloud-native tooling. Kelet is SOC 2 certified and designed to be a passive observer: read-only access to your traces, no changes to your system, no risk to uptime.