AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

2025-08-22

AI Summary

To address challenges in tool-driven agent development—including limited flexibility, poor debuggability, high security risks, and weak scalability—this paper proposes DevAgent, a systematic framework built upon an asynchronous runtime and ReAct-based behavioral modeling to enable seamless collaboration between LLMs and MCPs (Model-Controller-Plugin). DevAgent integrates a visual studio featuring execution tracing and interactive debugging, a lightweight security sandbox, and a pluggable modular architecture. These components collectively enhance development traceability and human-AI collaboration efficiency, while supporting long-horizon task monitoring and dynamic environmental adaptation. Experimental evaluation demonstrates that DevAgent outperforms existing approaches in task completion rate, deployment efficiency, and module reuse rate. It thus provides an end-to-end development infrastructure for building scalable, adaptive, and secure-controllable intelligent agents.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agentic applications and provide unified interfaces and extensible modules, enabling developers to easily leverage the latest progress, such as new models and MCPs. Furthermore, we ground agent behaviors in the ReAct paradigm and offer advanced agent-level infrastructure based on a systematic asynchronous design, which enriches both human-agent and agent-agent interaction patterns while improving execution efficiency. Building on this foundation, we integrate several built-in agents tailored to specific practical scenarios. AgentScope also includes robust engineering support for developer-friendly experiences. We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace. In addition, AgentScope offers a runtime sandbox to ensure safe agent execution and facilitates rapid deployment in production environments. With these enhancements, AgentScope provides a practical foundation for building scalable, adaptive, and effective agentic applications.

Problem

• Building flexible tool-based agent-environment interactions for applications
• Providing unified interfaces and extensible modules for developers
• Enhancing execution efficiency and interaction patterns in agent systems

Innovation

• Unified interfaces and extensible modules
• ReAct paradigm with asynchronous design
• Scalable evaluation and runtime sandbox