Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents or sub-workflows and edges encode dependencies and message passing. However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources. To overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS. It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components, skill support, multimodal message handling, and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT-GAMMA/MASFactory, licensed under Apache-2.0) and video demonstration (https://youtu.be/ANynzVfY32k) are publicly available.

翻译：基于大型语言模型（LLM）的多智能体系统（MAS）日益广泛用于通过角色分工与协作来扩展问题求解能力。MAS工作流可天然建模为有向计算图，其中节点执行智能体或子工作流，边编码依赖关系和消息传递。然而，当前框架中实现复杂图工作流仍需大量人工操作，复用性有限，且难以集成异构外部上下文源。为克服这些局限，我们提出MASFactory——一种面向图中心的基于LLM的MAS编排框架。该框架引入Vibe Graphing（一种人机协作方法），将自然语言意图编译为可编辑的工作流规范，进而转化为可执行图。此外，框架提供可复用组件、技能支持、多模态消息处理、可插拔上下文集成，以及用于拓扑预览、运行时追踪和人机协作的可视化工具。我们在七个公开基准上评估了MASFactory，验证了代表性MAS方法的复现一致性及Vibe Graphing的有效性。我们的代码（https://github.com/BUPT-GAMMA/MASFactory，基于Apache-2.0许可）和视频演示（https://youtu.be/ANynzVfY32k）均已公开。