Every step youragent |
Tracify shows what your AI agent did, why it failed, what it cost, and what to fix next. Trace every step, tool call, retry, and alert across production AI workflows.
Install the SDK. Run your agent. Watch spans appear live.
Agents fail silently.
You have no idea why.
When something breaks, you're left digging through logs, guessing what happened, and trying to reconstruct the run step by step.
NO_VISIBILITY —
Your agent calls 12 tools and 6 LLMs in a single run. Which step cost $40? Which one failed? Right now, you have no way to know.
NO_DEBUGGING —
When something goes wrong, you stare at raw logs and try to reconstruct what happened. A failed run can take hours to diagnose.
NO_COST CONTROL —
Runaway loops. Infinite context windows. Retries. Your LLM bill arrives and you have no idea what ran up the cost.
Catch the next one.
One decorator turns the next run into a trace.
No config files. No framework lock-in. No infrastructure to wire.
Every run becomes inspectable.
Tracify turns one agent run into a trace, cost map, retry trail, and failure record.
Built for agent builders and operators.
Debug multi-step agents without reading raw logs
Install the SDK, send spans, inspect the trace, copy payloads, and see exactly which model or tool call failed.
Explain reliability and cost before customers ask
Track cost over time, model usage, failed runs, and expensive traces so production agents do not become a black box.
Show clients what their workflows did in production
Label projects by client, collect proof of failures and fixes, and print reports that stakeholders can understand.
Operate shared agents with access control and alerts
Give product, engineering, and operations one view of runs, costs, Slack alerts, settings, and project ownership.
Catch failures before users escalate them
Watch failed runs, cost spikes, stalls, retries, and alert status from the same dashboard used for trace triage.
Agents that browse, summarize, and synthesize information
They call multiple tools, retry queries, and generate inconsistent outputs. You don’t know which step failed or why the answer changed.
Agents handling user conversations in production
Context grows, responses drift, and failures are unpredictable. When something breaks, you need the exact trace of what the agent saw.
Agents executing multi-step workflows
Dozens of steps, retries, and edge cases. A single failure breaks the chain, and you have no visibility into where it happened.
Agents calling APIs and external tools
They loop, retry, and escalate costs silently. Your API bill increases, but you don’t know what caused it.
Start with traces. Scale into operations.
Beta pricing is intentionally honest: use the working observability loop now, then upgrade when your agents need shared reporting, alerts, and operational controls.
Runtime controls, evals, self-hosting, email alerts, and PDF export are roadmap items, not current beta promises.