AgentScope 1.0 - A multi-agent development framework open-sourced by Alibaba Tongyi

What is AgentScope 1.0?

AgentScope 1.0 is an open-source multi-agent development framework from Alibaba Tongyi. Through a three-layer technical architecture—the AgentScope core framework, AgentScope Runtime, and AgentScope Studio—it provides full lifecycle support from development to deployment. The AgentScope core framework uses a modular design, comprising four main components: messaging, models, memory, and tools, supporting the efficient construction of agent applications based on large language models. AgentScope Runtime provides a secure and reliable runtime and deployment environment, while AgentScope Studio provides visual development and monitoring tools. Key features of AgentScope 1.0 include real-time intervention control, intelligent context management, and efficient tool invocation, making agent application development simpler, operation more secure, and monitoring more transparent.

Main features of AgentScope 1.0

• Real-time intervention controlIt supports secure interruption, real-time interruption, and flexible customization of interrupt handling logic to ensure the controllability and flexibility of task execution.
• Intelligent Context ManagementBy optimizing short-term memory and managing long-term memory across sessions, the problem of "amnesia" and "reset" of intelligent agents can be effectively solved.
• Efficient tool callingIt provides functions for registering, managing, and executing tools, supports parallel invocation and dynamic control, and improves operating efficiency.
• Safe and reliable operating environment: Build a security tool sandbox based on container technology to ensure that agents run in an isolated environment, while providing flexible deployment and monitoring support.
• Visual Development and MonitoringBased on state management and visual tracking, it provides real-time monitoring and agent evaluation functions to help developers quickly optimize agent performance.

Technical principles of AgentScope 1.0

• Core FrameworkComposed of four main components—message, model, memory, and tools—it achieves high decoupling and flexible expansion. Combining reasoning and action, it allows agents to generate responses through iterative reasoning and action cycles after receiving user queries. Based on asynchronous design, it enables flexible and robust real-time intervention control, supporting parallel tool invocation and dynamic tool configuration.
• runtime environmentBased on container technology, it builds a system-level secure and isolated tool execution environment, supporting various functional scenarios. It provides a complete deployment solution, supporting multiple protocols, flexible deployment, and framework independence, ensuring application stability and reliability.
• Development and monitoring toolsAgentScope Studio provides real-time monitoring and agent evaluation capabilities, supporting multi-granularity and multi-dimensional analysis of operational trajectories and evaluation results.

Project address for AgentScope 1.0

• Project official websiteSee: https://doc.agentscope.io/index.html
• GitHub repositoryhttps://github.com/agentscope-ai/agentscope
• arXiv technical paper: https://arxiv.org/pdf/2508.16279

Application scenarios of AgentScope 1.0

• User Assistant DialogueBuild intelligent assistants to help users answer questions and perform tasks, such as querying information and scheduling appointments.
• Multi-agent collaborationIt enables collaboration among multiple intelligent agents to complete complex tasks, such as team project management and multi-role dialogue.
• In-depth research and report generationIt automatically collects and analyzes information from multiple sources to generate detailed analysis reports, suitable for academic research, market analysis, and more.
• Browser automationThe intelligent agent operates the browser to complete tasks such as web browsing, information extraction, and form submission.
• Complex Task Planning and ExecutionComplex tasks are broken down into multiple sub-tasks, and multiple intelligent agents are dynamically scheduled to work together to complete them, such as project planning and data analysis.