CoReMem: Graph-Organized Memory for Long-Horizon Multi-Agent Collaboration

Long-horizon multi-agent systems distribute planning, retrieval, tool execution, and reflection across many roles and interaction rounds. Their histories are nevertheless often stored as separate Agent conversations, summaries, or vector memories. The same event can therefore be duplicated across private stores, different Agents can act on inconsistent versions of shared state, and an outdated intermediate conclusion can continue to influence later decisions. We introduce CoReMem, a graph-organized memory framework that represents Agents, tasks, subgoals, messages, tool outputs, environment states, and deliverables as typed nodes connected by delegation, reference, dependency, conflict, and temporal edges. Before acting, each Agent retrieves a budgeted local memory subgraph conditioned on its current subtask instead of reading the complete history. When an environment state, tool result, or task plan changes, CoReMem versions the updated node, propagates stale status through dependency edges, and rereads, recomputes, or replans only the affected subgraph before restoring current state. All memory organization and consistency maintenance remain external to the frozen language models. Our evaluation protocol tests the framework on long-horizon data analysis, software construction, and evidence synthesis workflows.