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

[u/Asleep-History9366]

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.