EvoOrch: Evolving Orchestration for Dynamic Multi-Agent Systems

Large language model-based multi-agent systems (MASs) solve complex, long-horizon tasks by decomposing them into subtasks and assigning these subtasks to specialized sub-agents. However, existing systems often rely on predefined or ad hoc orchestration structures, limiting their adaptability at both the intra-task and cross-task levels: 1) within a task, they struggle to dynamically reorganize sub-agent structures and correct execution failures in a timely manner, and 2) across tasks, they make limited use of previous trajectories to improve future orchestration. To address these limitations, we propose **EvoOrch**, a multi-agent orchestration framework that supports both intra-task and cross-task evolution. EvoOrch consists of a persistent Meta Agent and dynamically instantiated sub-agents. At the intra-task level, EvoOrch performs structural evolution by dynamically constructing and extending task-specific sub-agent structures, and configuration evolution by revising ineffective active sub-agents during execution. At the cross-task level, EvoOrch follows a two-stage pipeline that first hierarchically organizes completed multi-agent trajectories into structured trajectories, and then performs trajectory-driven analysis to distill reusable orchestration and verification insights, which are stored in an experience base and retrieved to guide future tasks without parameter updates. Experiments on diverse settings show that EvoOrch surpasses the state-of-the-art automated orchestration method by 7.9 points on average, while achieving a better Pareto trade-off between performance and cost.