MemIndex: Agentic Event-based Distributed Memory Management for Multi-agent Systems

Interactive applications are latency-sensitive systems that enable dynamic responses to user inputs in domains such as robotics, industrial automation, and autonomous control. These applications require efficient application protocols for communication, with the pub/sub model being one of the most promising approaches. However, existing pub/sub systems are architecturally constrained, particularly by limited memory capacity and inefficiencies in dynamic environments. Addressing these challenges requires effective distributed memory management, yet this aspect has received limited attention in existing research. This paper addresses the gap by proposing MemIndex , an adaptive and autonomous distributed memory-management framework with an intent-indexed bipartite graph architecture. It is designed for an LM-based multi-agent pub/sub systems, enabling agents to autonomously negotiate memory operations in real time through dynamic index spaces for efficient reasoning. We evaluate our proposed MemIndex using diverse models against two baselines. Experimental results show MemIndex outperforms both baselines across storage, retrieval, update, and deletion operations, achieving average reductions of about 34% and 56% in elapsed time, 57% and 75% in CPU utilization, 23% and 76% in memory usage. Scalability tests further demonstrate that MemIndex maintains low end-to-end delay as submissions and agents grow, confirming that its negotiation-driven offloading enables efficient distributed memory management in interactive applications.