With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these challenges, we propose AgentScope, a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment. Towards robust and flexible multi-agent application, AgentScope provides both built-in and customizable fault tolerance mechanisms. At the same time, it is also armed with system-level support for managing and utilizing multi-modal data, tools, and external knowledge. Additionally, we design an actor-based distribution framework, enabling easy conversion between local and distributed deployments and automatic parallel optimization without extra effort. With these features, AgentScope empowers developers to build applications that fully realize the potential of intelligent agents. We have released AgentScope at https://github.com/modelscope/agentscope, and hope AgentScope invites wider participation and innovation in this fast-moving field.

翻译：随着大语言模型（LLM）的快速发展，多智能体应用取得了显著进展。然而，协调智能体合作的复杂性以及LLM性能的不稳定性，给开发稳健高效的多智能体应用带来了显著挑战。为解决这些问题，我们提出AgentScope——一种以消息交换为核心通信机制的开发者中心化多智能体平台。丰富的语法工具、内置智能体与服务功能、友好的应用演示与监控界面、零代码编程工作站以及自动提示调优机制，显著降低了开发与部署的门槛。面向稳健灵活的多智能体应用，AgentScope提供了内置及可定制的容错机制，同时配备系统级支持以管理和利用多模态数据、工具及外部知识。此外，我们设计了一种基于Actor的分布式框架，支持本地与分布式部署间的便捷转换，且无需额外成本即可实现自动化并行优化。凭借这些特性，AgentScope使开发者能够构建充分发挥智能体潜能的应用。我们已在https://github.com/modelscope/agentscope 发布AgentScope，期望其能推动这一快速演进领域更广泛的参与与创新。