  In this paper, we aim to evaluate multi-agent systems against complex
dependencies, including spatial, causal, and temporal constraints. First, we
construct a new benchmark, named VillagerBench, within the Minecraft
environment.VillagerBench comprises diverse tasks crafted to test various
aspects of multi-agent collaboration, from workload distribution to dynamic
adaptation and synchronized task execution. Second, we introduce a Directed
Acyclic Graph Multi-Agent Framework VillagerAgent to resolve complex
inter-agent dependencies and enhance collaborative efficiency. This solution
incorporates a task decomposer that creates a directed acyclic graph (DAG) for
structured task management, an agent controller for task distribution, and a
state manager for tracking environmental and agent data. Our empirical
evaluation on VillagerBench demonstrates that VillagerAgent outperforms the
existing AgentVerse model, reducing hallucinations and improving task
decomposition efficacy. The results underscore VillagerAgent's potential in
advancing multi-agent collaboration, offering a scalable and generalizable
solution in dynamic environments. The source code is open-source on GitHub
(https://github.com/cnsdqd-dyb/VillagerAgent).
