AgentZip：利用LLM等待期压缩并行沙箱共享内存
If 1 agent task launches many sandboxes, manage their memory together
Agent架构中显存/内存管理是核心痛点，AgentZip给出了具体的压缩时机与原理，做多Agent系统的同学值得参考其工程思路。
If 1 agent task launches many sandboxes, manage their memory together
如果一个智能体任务启动了多个沙箱，应统一管理它们的内存
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.
AgentZip 表明，88.55% 的并行智能体的内存成本中，重复状态占很大比例，因此解决方案是在 LLM 等待期间压缩兄弟沙箱共享的内容。
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.
当一个任务生成多个沙箱时，它们并非真正独立。它们基于相同的模板启动，并且经常运行相似的代码、文件、库和命令。这导致大量重复的内存占用。
Standard Linux tools either compress pages separately or only merge exact matches, so they miss much of this overlap.
标准的 Linux 工具要么单独压缩页面，要么仅合并完全匹配的页面，因此无法有效利用这种重叠。
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.
AgentZip 针对共享模板或兄弟沙箱进行内存压缩，在智能体等待 LLM 响应时执行繁重的压缩工作，并在下一次工具调用需要之前恢复可能需要的页面。
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