Everyone monitors. Nobody prescribes.

Your agents are misbehaving.
Whisker tells you how to fix them.

Whisker sits on top of your existing observability stack and turns OTel traces into plain-English prescriptions. Not dashboards. Not alerts. Fixes.

Try it on your trace →

The Problem

Your observability tool filed a report.
Now what?

Langfuse, LangSmith, AgentOps, Helicone — genuinely good tools. They show you latency, token counts, cost, and traces. They tell you what happened.

None of them tell you what to do about it. You're left staring at a flamegraph wondering why your agent burned 8,000 tokens on a 200-token lookup.

Observability without prescription is just expensive logging.

Langfuse LangSmith AgentOps Helicone + your current setup
"Observability tools show you what happened.
Whisker tells you how to fix it."

Architecture

One layer on top of what you already use.

Whisker doesn't replace your observability stack. It reads the same OTel traces and adds the layer that was always missing.

Your AI Agent
LangChain, custom stack, or anything emitting OTel spans
Your Observability Tool
Langfuse · LangSmith · AgentOps · Helicone
Whisker
Reads traces · Detects inefficiencies · Prescribes fixes
Exactly what to fix
Specific, actionable, ready to ship

Sample Output

This is what a prescription looks like.

whisker — bash
$ whisker analyze --trace agent_run_20260509.json Analyzing 12 spans across 4 LLM calls... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [1/3] CONTEXT BLOAT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Span: span_3 (gpt-4o, call #3 of 4) Obs: Input tokens grew 4.2× (1,204 → 5,089) Fix: Summarize or trim tool output before passing to the next LLM call. Raw dumps are inflating your context window — and your bill. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [2/3] INSUFFICIENT TOOL CALLS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Span: span_7 (gpt-4o, final answer) Obs: Complex query resolved with 0 tool calls Fix: Agent answered from memory alone. Add a retrieval or validation step before the final response. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [3/3] MODEL ROUTING OPPORTUNITY ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Span: span_2 (gpt-4o → 18 output tokens, $0.03/call) Obs: Short output on an expensive model Fix: Route this call to gpt-4o-mini. Estimated savings: ~85% per invocation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3 prescriptions. No dashboards. No tickets. Fix your agent.

Get Started

Try it on your trace.

Share your trace. Get prescriptions. Real findings, real fixes.

Email your trace to hello@usewhisker.dev