The problem

I kept finding out my AI agents broke from user complaints — not from my own monitoring.

An agent would run 47 times overnight. Three failed. Costs spiked 3×. I found out the next morning from a user ticket.

Every existing tool was either enterprise-grade overkill (Datadog, New Relic) or required complex setup (Langfuse, Helicone). Nothing was built for a solo dev who just wants to know when something goes wrong.

So I built Farol.

One decorator. That's it.

from farol import trace

@trace ( agent_name = " my-agent " , farol_key = " frl_... " ) def my_agent ( task , run = None ): run [ " topic " ] = task # your agent code here

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That's all you need to get started. Farol tracks everything automatically.

What you get

Cost anomaly detection — Farol learns what's normal for each agent and alerts you when a run costs 3× more than usual.…