Learning Who Knows What: Learned Role-Aware Agentic Memory in LLM-Based Multi-Agent Systems

Persistent memory helps LLM agents reuse experience across tasks, but experience useful to one role may be unsuitable for another. We study how agents should decide whether to use memory and whose experience to consult for the current task. We propose LoRAM, a role-aware memory framework that separates source eligibility from online source selection. A role compatibility gate identifies eligible peers, while an online contextual policy selects among bypassing memory, using local experience, consulting a single peer, or combining local and peer memories under a common token budget. Memories remain in stores owned by individual agents, and only consolidated procedures are eligible for transfer between agents. A transactive directory tracks source expertise and directed transfer outcomes to inform routing decisions. We evaluate LoRAM on sequential Research, Database, and Coding tasks from MultiAgentBench. LoRAM achieves the highest observed mean scores among the evaluated configurations across all three environments. On Research with GPT-4o-mini, it also uses 15.7% fewer tokens per task than the strongest baseline by mean score. Research ablations compare the full method with local memory, shared access, static role routing, and provenance-aware item routing. These results suggest that learning whose experience to reuse is a promising approach to memory management in teams of specialized agents.