AgentScope: A Flexible yet Robust Multi-Agent Platform

随着大型语言模型（LLMs）的迅速发展，多智能体应用程序取得了显著进展。然而，协调智能体合作和LLMs的不稳定性等复杂性问题，给开发鲁棒高效的多智能体应用程序带来了显著挑战。为了应对这些挑战，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。我们的通信机制配备了丰富的语法工具、内置资源和用户友好的交互，显著降低了开发和理解的门槛。为了实现鲁棒和灵活的多智能体应用程序，AgentScope提供了内置和可定制的容错机制，并配备了多模态数据生成、存储和传输的系统级支持。此外，我们设计了一个基于演员的分布式框架，可以轻松地在本地和分布式部署之间进行转换，并实现自动并行优化，无需额外努力。有了这些特性，AgentScope赋予开发人员构建充分发挥智能体潜力的应用程序的能力。我们已经在https://github.com/modelscope/agentscope发布了AgentScope，并希望AgentScope能在这个快速发展的领域吸引更广泛的参与和创新。

AgentScope: A Developer-Centric Multi-Agent Platform with Message Exchange as Core Communication Mechanism

AgentScope is a multi-agent platform that addresses the challenges of coordinating agents' cooperation and LLMs' erratic performance by providing built-in and customizable fault tolerance mechanisms, system-level supports for multi-modal data generation, storage and transmission, and an actor-based distribution framework that enables easy conversion between local and distributed deployments and automatic parallel optimization.

AgentScope significantly reduces the barriers to both development and understanding of multi-agent applications by providing abundant syntactic tools, built-in resources, and user-friendly interactions. The platform has been released as open source on GitHub, and its features include message exchange as the core communication mechanism, built-in and customizable fault tolerance mechanisms, system-level supports for multi-modal data generation, storage and transmission, and an actor-based distribution framework that enables easy conversion between local and distributed deployments and automatic parallel optimization.

Other recent related research in this field includes 'Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms' by Shangtong Zhang, 'A Survey of Multi-Agent Systems: State of the Art and Research Challenges' by Sebastian Rodriguez and Ana Garcia-Serrano, and 'Multi-Agent Systems: A Survey' by Yoav Shoham and Kevin Leyton-Brown.