Rohan Paul

@rohanpaul_ai

If 1 agent task launches many sandboxes, manage their memory together AgentZip shows that much of the memory cost by 88.55% of parallel agents is duplicated state, so the fix is to compress what sibling sandboxes share during LLM waiting time. When 1 task spawns many sandboxes, they are not truly independent. They start from the same template and often run similar code, files, libraries, and commands. That leaves a lot of repeated memory. Standard Linux tools either compress pages separately or only merge exact matches, so they miss much of this overlap. AgentZip compresses memory against the shared template or sibling sandboxes, does the heavy work while the agent waits for the LLM, and restores likely-needed pages before the next tool call needs them.
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