MemCo: Memory-Centric Collaboration for Generalizing LLM Agents to Unseen Environments

Large language model (LLM) agents interact with environments through sequential observation, action, and feedback. While memory enables agents to reuse past experience, existing approaches often construct and maintain memory for individual agents independently, making it costly to collect sufficient trajectories and limiting generalization to unseen environments. Sharing experience accumulated by multiple agents interacting with different environments can reduce repeated exploration. In this work, we ask: how can shared experience be organized into useful memory and effectively used to support another agent’s current decision? We propose MemCo, a memory-centric collaboration framework for sharing and reusing agent experience. MemCo maintains complementary local and global memory spaces, preserving environment-specific experience locally while reusable records abstracted from local trajectories are aggregated across agents and promoted to global memory based on accumulated evidence. During online interaction, MemCo retrieves and composes memories from both levels based on the agent’s current state and decision phase, grounding shared experience in the current environment to support the next decision. Extensive studies on ALFWorld, PDDL, FEVER, and ScienceWorld show that MemCo improves task success under evaluation across seen and unseen environments, as well as reduces redundant exploration compared with the state-of-the-art methods. Our code is available at project.