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 are becoming more capable, but debugging them is still surprisingly difficult. A typical agent might execute something like:

User request ↓ LLM ↓ Search / Tool ↓ LLM ↓ Database / Tool ↓ LLM ↓ External API ↓ Final response

Now imagine the final answer is wrong. The actual mistake may have happened several steps earlier. Maybe the model selected the wrong tool. Maybe a tool returned an unexpected result. Maybe the agent state changed unexpectedly. Maybe the model made a bad decision because of information introduced earlier in the execution.

With traditional logging, you can inspect what happened. But if you want to experiment with what would have happened after changing an earlier decision, things become much harder. You often have to run the agent again from the beginning. That can mean:

• repeating model calls
• repeating tool calls
• making network requests again
• waiting for the entire workflow
• potentially triggering real-world side effects

I wanted a different approach.

The Idea: Treat an Agent Execution as History

Instead of thinking about an agent run as a stream of logs, I wanted to treat it as a historical execution that could be inspected. The workflow becomes:

Record ↓ Inspect ↓ Rewind ↓ Modify ↓ Replay ↓ Branch

The important part is that the original execution remains available. You can use it as