How I Built a Time-Travel Debugger for AI Agents
How I Built a Time-Travel Debugger for AI Agents What if debugging an AI agent worked more like debugging normal code? Pause execution. Inspect what happened. Go back to an earlier state. Change something. Then continue from there. That idea led me to build an open-source time-travel debugger for AI agents . GitHub: https://github.com/UjwalBagalkoti/ai-time-travel-debugger The Problem AI agents…
The article discusses the development of an open-source time-travel debugger for AI agents, aiming to simplify the debugging process by treating AI agent execution as a historical event that can be inspected, rewound, modified, and replayed. The debugger is built around a pipeline involving a Python AgentTracer SDK, a FastAPI backend for trace storage and retrieval, a PostgreSQL or SQLite database, and a React Flow frontend for visualization.
This architecture allows for independent development of each component and enables experimentation with different agent frameworks. The debugger's interface presents an execution graph, timeline scrubber, step inspector, and execution metrics, making it easier for developers to identify and correct mistakes in AI agent workflows.
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