用图结构设计可组合的大模型智能体系统，灵活且可分析。
MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems
- 用三种图抽象分离组件、流程与搜索逻辑，支持灵活复用。
- 实验表明能有效分析性能与效率的权衡关系。
- 适合需要构建复杂智能体系统的开发者和研究者。
我们提出 MyAG，一种基于图结构的框架，用于设计和分析可组合的大语言模型智能体系统。该框架将系统构建分为三类图抽象：组件图（用于代理、环境与模块）、工作流图（用于执行控制）和搜索图（用于运行时执行）。这种分离使用户能够以不同策略灵活复用相同组件。通过递归系统节点支持层级组合，并提供监控与可视化工具以检查智能体执行过程。在典型智能体应用上的实验表明，该框架支持灵活设计，并有助于分析性能-效率权衡。框架已公开，完全开源。
原文摘要 · Abstract (English)
We present MyAG, a graph-based framework for designing and analyzing composable LLM agent systems. Our framework separates agent system construction into three graph abstractions: a component graph for agents, environments, and modules; a workflow graph for execution control; and a search graph for runtime execution. This separation allows users to flexibly reuse the same components with different strategies. We further support hierarchical composition through recursive system nodes and provide monitoring and visualization tools for inspecting agent execution. Experiments on representative agent applications show that our framework supports flexible agent system design and helps analyze performance-efficiency tradeoffs. Our framework is publicly available and fully open-source.
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