Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes
Saving computer resources by knowing when AI agents actually need backups
Most checkpoints of AI agent sandboxes are wasted because existing systems either skip important OS-level side effects or save state after every single action. Crab cuts checkpoint overhead by 87% by intelligently deciding which agent turns actually produce recoverable state—and achieves perfect recovery where naive chat-only approaches fail.
AI agents running in sandboxed containers need frequent backups for fault tolerance and experimentation, but constant checkpointing tanks performance and costs. Crab lets companies run more agents on shared hardware at lower cost while maintaining the ability to recover from failures or rollback bad decisions—turning a system bottleneck into a nonissue.