LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
This work addresses the challenge of efficient memory design in multi-agent systems for workflow automation, offering a practical framework and research tool, though it is incremental in building on existing multi-agent and memory-augmented approaches.
The paper tackles the problem of enhancing multi-agent LLM systems for workflow automation by introducing LEGOMem, a modular procedural memory framework that decomposes past task trajectories into reusable units, resulting in improved task decomposition and execution accuracy on the OfficeBench benchmark, with smaller models narrowing the performance gap with stronger agents.
We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory units and flexibly allocates them across orchestrators and task agents to support planning and execution. To explore the design space of memory in multi-agent systems, we use LEGOMem as a lens and conduct a systematic study of procedural memory in multi-agent systems, examining where memory should be placed, how it should be retrieved, and which agents benefit most. Experiments on the OfficeBench benchmark show that orchestrator memory is critical for effective task decomposition and delegation, while fine-grained agent memory improves execution accuracy. We find that even teams composed of smaller language models can benefit substantially from procedural memory, narrowing the performance gap with stronger agents by leveraging prior execution traces for more accurate planning and tool use. These results position LEGOMem as both a practical framework for memory-augmented agent systems and a research tool for understanding memory design in multi-agent workflow automation.