  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.
