AgentScope: A Flexible yet Robust Multi-Agent Platform
Dawei Gao†, Zitao Li†, Xuchen Pan∗, Weirui Kuang∗, Zhijian Ma∗, Bingchen Qian∗, Fei Wei∗, Wenhao Zhang∗, Yuexiang Xie∗, Daoyuan Chen∗, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li‡, Bolin Ding‡, Jingren Zhou Alibaba Group
Abstract
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
中文速览
大规模语言模型（LLM）驱动的多智能体系统（multi-agent system）在软件工程、社会仿真等领域潜力巨大，但实际开发中面临三大难题：多个智能体的协作流程难以管理、LLM输出不稳定导致错误级联蔓延、分布式部署对开发者的系统编程要求极高。AgentScope 以"消息传递"为核心通信机制，提供了一套面向开发者的多智能体平台，内置丰富的语法工具、故障容错机制、多模态数据与外部知识支持，还附带零代码拖拽式编程工作台和自动提示词（prompt）调优功能。在分布式部署上，平台采用基于Actor模型的框架，让本地代码无需改动即可平滑迁移到分布式环境并自动并行优化。实验与应用案例表明，AgentScope 能显著降低多智能体应用的开发门槛，同时保证系统的健壮性与运行效率，对推动该领域更广泛的研究与工程实践具有重要价值。
原文 arXiv:2402.14034;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2402.14034v2