title: Very Large-Scale Multi-Agent Simulation in AgentScope
summary: 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.
paper_authors._id: 66a31f39a74b20aad777e7e7
paper_authors.name: Xuchen Pan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-26T08:30:22.586Z
paper_authors._id: 64101b4ca92fedb0e8559b91
paper_authors.avatarUrl: /avatars/c7453a5641a94d04a75de9b7d4272bc2.svg
paper_authors.fullname: Xuchen Pan
paper_authors.type: user
paper_authors.user: panxuchen
paper_authors._id: 66a31f39a74b20aad777e7e8
paper_authors.name: Dawei Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-26T08:30:28.568Z
paper_authors._id: 642e79a83a486fec83426a49
paper_authors.avatarUrl: /avatars/a003d27177cee6234348008263933cfd.svg
paper_authors.fullname: dawei gao
paper_authors.type: user
paper_authors.user: DavdGao
paper_authors._id: 66a31f39a74b20aad777e7e9
paper_authors.name: Yuexiang Xie
paper_authors._id: 66a31f39a74b20aad777e7ea
paper_authors.name: Zhewei Wei
paper_authors._id: 66a31f39a74b20aad777e7eb
paper_authors.name: Yaliang Li
paper_authors._id: 66a31f39a74b20aad777e7ec
paper_authors.name: Bolin Ding
paper_authors._id: 66a31f39a74b20aad777e7ed
paper_authors.name: Ji-Rong Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-26T08:32:01.623Z
paper_authors._id: 64b8c89052b7353d8c6a1013
paper_authors.avatarUrl: /avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
paper_authors.fullname: Ji-Rong Wen
paper_authors.type: user
paper_authors.user: jrwen
paper_authors._id: 66a31f39a74b20aad777e7ee
paper_authors.name: Jingren Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-26T08:32:09.560Z
paper_authors._id: 602f88f5e8149a962412a667
paper_authors.avatarUrl: /avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Jingren
paper_title: Very Large-Scale Multi-Agent Simulation in AgentScope
paper_summary: 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.
paper_ai_keywords: actor-based distributed mechanism parallel execution centralized workflow orchestration inter-agent interactions agent-environment interactions automatic background generation pipeline web-based interface