AgentZip: memory compression for parallel agent sandboxes cuts memory up to 8.7x
rohanpaul_ai · x · 2026-09-19
AgentZip shows much of the memory cost of parallel agent sandboxes — 88.55% of it duplicated state from shared templates and similar trajectories — can be reclaimed by compressing against sibling sandboxes during LLM waiting periods, with restore-time prefetching. Across LLM training and inference workloads it reduces sandbox-owned memory by up to 8.7x vs 2.1x for the Linux baseline.
Related event: AgentZip Paper Compresses High-Fanout Agent Sandbox Memory by 8.7x(2 posts)→
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