AgentScope: A Flexible yet Robust Multi-Agent Platform

随着大型语言模型（LLMs）的快速发展，多智能体应用取得了显著进展。然而，协调智能体合作和LLMs的不稳定表现所带来的复杂性，给开发鲁棒且高效的多智能体应用带来了显著挑战。为了解决这些挑战，我们提出了AgentScope，这是一个以消息交换为核心通信机制的面向开发人员的多智能体平台。AgentScope提供了丰富的语法工具、内置的智能体和服务功能、应用演示和实用监控的用户友好界面、零代码编程工作站以及自动提示调整机制，这些显著降低了开发和部署的门槛。为了实现鲁棒和灵活的多智能体应用，AgentScope提供了内置和可定制的容错机制。同时，它还配备了系统级支持，用于管理和利用多模态数据、工具和外部知识。此外，我们设计了一个基于Actor的分布式框架，使本地和分布式部署之间的转换变得容易，并自动进行并行优化，无需额外的工作量。有了这些功能，AgentScope赋予开发人员建立完全发挥智能体潜力的应用的能力。我们已经在https://github.com/modelscope/agentscope上发布了AgentScope，并希望AgentScope能在这个快速发展的领域中得到更广泛的参与和创新。

AgentScope: A Developer-Centric Multi-Agent Platform with Message Exchange

AgentScope is a platform for developing and deploying multi-agent applications with message exchange as the core communication mechanism. It provides a zero-code programming workstation, abundant syntactic tools, built-in agents and service functions, and automatic prompt tuning mechanism to lower the barriers to development and deployment. It also has built-in and customizable fault tolerance mechanisms, system-level support for managing and utilizing multi-modal data, tools, and external knowledge, and an actor-based distribution framework for easy conversion between local and distributed deployments and automatic parallel optimization without extra effort.

AgentScope empowers developers to build applications that fully realize the potential of intelligent agents. The platform is released on Github and invites wider participation and innovation in the field. The experiments in the paper demonstrate the effectiveness and efficiency of AgentScope in various scenarios. The platform also supports both built-in and customizable fault tolerance mechanisms, making it more robust and flexible. Additionally, the actor-based distribution framework enables easy conversion between local and distributed deployments and automatic parallel optimization without extra effort.

Recent related research includes Multi-Agent Reinforcement Learning (MARL), which focuses on developing agents that can learn and cooperate with each other in a multi-agent environment. Other related works include multi-agent platforms such as OpenAI Gym, Microsoft Project Malmo, and Multi-Agent Particle Environment (MPE), which provide environments for developing and testing multi-agent algorithms.