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TracePilot AI is a debugging layer for AI agents. When your agent fails in production, traditional logs tell you it happened — TracePilot tells you exactly why, at which step, with what inputs. You wrap your OpenAI calls with TracePilot, and every decision your agent makes is captured as a structured span you can inspect, fork, and replay from the dashboard.

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The problem TracePilot solves

AI agents are hard to debug. Your agent calls GPT-4o 47 times, spends $3.20, and returns garbage. Your logs say "agent finished". You have no idea what happened inside. Traditional observability tools tell you that something broke. TracePilot lets you go back in time, change the input at the exact step that failed, and re-run from there — without redeploying anything.

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How it works

You wrap your existing OpenAI calls with TracePilot. That’s it. TracePilot doesn’t replace your stack — it wraps it.
Every LLM call and tool invocation is captured as a structured span. Spans are linked into an execution tree that mirrors your agent’s logic. When something goes wrong, you open the dashboard, find the failing span, and hit Fork & Rerun. Edit the prompt right there. See the new output instantly.

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Key features

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Works with your existing stack

TracePilot wraps your calls — it doesn’t replace them. There’s no lock-in and no SDK sprawl. It’s compatible with the OpenAI SDK directly, and works alongside LangChain, CrewAI, AutoGen, and any custom orchestration pattern that uses async calls.

Quick Start

Install the SDK and send your first trace in under 5 minutes.

Authentication

Get your API key and configure the SDK for your environment.