Memory Bank of Finite Capacity for LLMs based Multi-Agent Systems

Memory mechanisms are the core enabler for large language model based multi-agent systems (MAS) in downstream tasks. Existing works enhance the effectiveness of memory guided MAS through the integration and updating of memory for adaptation to constantly evolving tasks. Nevertheless, these strategies incur higher consumption of storage space, time, and token resources, which constrains the efficacy of memory mechanisms in long-term MAS operation. To solve these drawbacks, this paper proposes FiniteMem, a finite memory mechanism based on multi-dimensional memory replay for MAS. It builds a three-tier memory architecture to supply both macro and micro guidance, and designs the memory replay based on memory competition and inhibitory control, to substantially reduce resource consumption while improve the memory mechanism adaptability. Experiments on multiple publicly available datasets and MAS frameworks verify that FiniteMem achieves state-of-the-art performance, reducing storage space by 31.3%, token consumption by 26.9%, and runtime by 11.8%. FiniteMem offers a universal, high efficiency long-term memory management solution for balancing performance and resource overhead of MAS. Our code will be available at https://anonymous.4open.science/r/FiniteMem-5A3B.