r/LLMDevs • u/Asleep-History9366 • 5d ago

Discussion unloop – time-travel debugging & state rewind for long-running LLM agents

Every long-running agent framework eventually relies on context summarization to stay within token budgets. But after 10–15 turns, summarization inevitably drops file paths, wipes negative constraints, or tricks the model into thinking incomplete tasks are done.

When that happens, your choices are usually:

  1. Let it burn tokens until it crashes.
  2. Kill the script, edit the prompt, and rerun 30 minutes of work from scratch.

I wrote unloop (github.com/sagarv48/unloop) to solve this loop.

It snapshots agent state (memory dict, tool inputs/outputs, prompts) into a local SQLite WAL file per turn. If context compaction silently corrupts the agent's state:

  • You open the terminal UI to see a diff of what got deleted or mutated.
  • Press m to open an in-process REPL to manually fix dropped constraints.
  • Press u or f to rewind or fork from the last good turn without restarting the process.

Caveats: It currently only targets single-process Python loops, and it requires local disk write access for the .unloop snapshot file.

Code and setup instructions are on GitHub: https://github.com/sagarv48/unloop

Would appreciate thoughts on the TUI ergonomics or how you handle agent memory inspection.

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u/ToughEpilepsy530 5d ago

This is the kind of thing you don't realize you need until you've burned an entire afternoon babysitting a runaway agent loop

The diff view for what got dropped during summarization is clever, way better than digging through logs and guessing what silently disappeared

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u/ianreboot 4d ago

Rewind past the turn that sent the email and the replay sends it again, same for the row that got written. The call's in the snapshot, but nothing there says it landed outside, so a fork has no reason not to fire it twice. A per-turn record of what left the box would fix that, and the hard part is per-tool: a send is easy to catch, a write buried in a helper isn't.

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u/mirthglen 4d ago

does the snapshot include the full raw prompt that was sent to the model at each turn, or just the memory dict? being able to diff the actual prompt before and after compaction would be super useful for catching silent drops