Very Large-Scale Multi-Agent Simulation in AgentScope
Authors: name: Xuchen Pan, name: Dawei Gao, name: Yuexiang Xie, name: Zhewei Wei, name: Yaliang Li, name: Bolin Ding, name: Ji-Rong Wen, name: Jingren Zhou

Recent advances in large language models (LLMs) have opened new avenues for
applying multi-agent systems in very large-scale simulations. However, there
remain several challenges when conducting multi-agent simulations with existing
platforms, such as limited scalability and low efficiency, unsatisfied agent
diversity, and effort-intensive management processes. To address these
challenges, we develop several new features and components for AgentScope, a
user-friendly multi-agent platform, enhancing its convenience and flexibility
for supporting very large-scale multi-agent simulations. Specifically, we
propose an actor-based distributed mechanism as the underlying technological
infrastructure towards great scalability and high efficiency, and provide
flexible environment support for simulating various real-world scenarios, which
enables parallel execution of multiple agents, centralized workflow
orchestration, and both inter-agent and agent-environment interactions among
agents. Moreover, we integrate an easy-to-use configurable tool and an
automatic background generation pipeline in AgentScope, simplifying the process
of creating agents with diverse yet detailed background settings. Last but not
least, we provide a web-based interface for conveniently monitoring and
managing a large number of agents that might deploy across multiple devices. We
conduct a comprehensive simulation to demonstrate the effectiveness of the
proposed enhancements in AgentScope, and provide detailed observations and
discussions to highlight the great potential of applying multi-agent systems in
large-scale simulations. The source code is released on GitHub at
https://github.com/modelscope/agentscope to inspire further research and
development in large-scale multi-agent simulations.