Convo's state restoration is based on a "checkpoint snapshot" mechanism and is a three-step process:
- saved state: Called at a critical point in the code
save_checkpoint(run_id)The system keeps the current agent's context, tool call stack, and memory state intact. - View Records: In the "Checkpoints" module of the dashboard, you can view all saved status nodes by timeline, and support adding comments to mark important nodes.
- resumption: By
restore_checkpoint(run_id)or interface operation, any history node can be selected for re-running
This feature offers two major advantages over traditional debugging methods:
- accurate reproduction: 100% can restore the system state at the time of the problem, to avoid the "can't be reproduced" problem caused by environmental differences.
- Time Travel Debugging: Developers can jump test between checkpoints to quickly locate the specific point at which an anomaly first appears.
It is especially suitable for handling complex multi-round dialog scenarios, such as the air ticket booking process that involves multiple tool invocations.
This answer comes from the articleConvo: Logging and Debugging Tools for Building Intelligent AI AgentsThe
May not be reproduced without permission:AI productivity tools " How does Convo's state recovery function work? What are the advantages in commissioning?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil