Cooperative Scheduling and Hierarchical Memory Model for Multi-Agent Systems

Large Language Models (LLMs) are leading a technological revolution. This gives agents based on LLMs renewed vitality, and multi-agent collaboration is showing the potential to foster new forms of intelligence. However, current multi-agent systems face two major challenges: the first is the issue of resource coordination and scheduling within multi-agent systems, and the second is the limitation of the context window in large models, which hinders the practical application of agents in long-term conversational scenarios, urgently requiring improvements in memory capabilities. To address these challenges, we propose a collaborative scheduling strategy and hierarchical memory model for LLM-based multi-agent systems inspired by operating systems. First, we design a time-sharing scheduling strategy, analogous to process scheduling in operating systems, which divides the resource usage cycle into finer-grained single-step workflows, allocating independent resource windows to different agents to reduce resource contention and conflicts. Second, we introduce a hierarchical memory model based on the multi-level cache architecture of operating systems, segmenting the agents' memory into core memory, main memory, and vague memory areas, thereby significantly improving memory retention and retrieval efficiency in LLM - based agents when handling complex tasks. Experimental results demonstrate that our proposed method achieves efficient resource allocation in multi-agent systems while significantly enhancing the memory capabilities of agents and overall system performance.