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 Trace: agent_run_20260509.json LLM calls found: 4 ============================================================ Context Bloat Detected ============================================================ LLM call #3 received 4.2x more input tokens than call #2. Before: 1,204 tokens → After: 5,089 tokens Likely cause: A tool result was injected into the prompt unfiltered. Fix: Summarize or trim tool output before passing it to the next LLM call. ============================================================ Model Routing Opportunity Detected ============================================================ LLM call #1 used gpt-4o but only produced 18 output tokens. Likely cause: A simple routing task was sent to an expensive model. Fix: Route this call to gpt-4o-mini. Reserve gpt-4o for complex reasoning. ============================================================ [AI] Task Abandonment Detected ============================================================ Agent gave up after search returned no results and told the user to look it up themselves instead of retrying. Fix: Add a retry loop: if search returns no results, reformulate the query and try again before responding to the user. ============================================================ Found 3 prescriptions

Contact

Questions or feedback?

We'd love to hear from you.

hello@usewhisker.dev