It uses a dual-stream architecture with a semantic stream and a runtime stream backed by a persistent Python environment. It introduces stateful runtime management that injects, manipulates, and retrieves complex Python objects across turns. CaveAgent achieves a 28.4% reduction in total token consumption and 59% reduction on data-intensive tasks.
It extends the Agent Skills open standard with a runtime-integrated skill management system. Multiple agents can share a single runtime, perceiving changes instantly through direct object reference.
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