发表机构
Tsinghua University; Shanghai Jiao Tong University; Shanghai Innovation Institute; Fudan University; Peking University; Massachusetts Institute of Technology（清华大学; 上海交通大学; 上海创新研究院; 复旦大学; 北京大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ChatDev 2.0推出无代码多智能体平台DevAll，结合声明式可执行图抽象与可视化界面，复现先进MAS并获竞争力性能，为LLM-based MAS提供通用开发方案。
AI 中文摘要
基于大语言模型（LLM）的多智能体系统（MAS）在解决复杂任务方面展现出强大潜力，但其开发存在权衡：代码框架表达力强但工程密集，而无代码构建器简化了创作却将智能体交互限制在构建者定义的工作流中。我们提出ChatDev 2.0：DevAll（下称DevAll），这是一个用于构建、执行和检查异构MAS的无代码平台，兼具高表达力和易用性。在表达力方面，DevAll将声明式可执行图抽象与感知循环的执行引擎相结合，使得异构智能体以及动态和循环交互可在单一框架内表示和执行。在易用性方面，集成的可视化界面让用户无需编写任何代码即可完成MAS的创作、运行、监控和检查，包括人在回路的步骤。实验表明，DevAll在三个代表性任务中复现了最先进的MAS，性能具有竞争力，且无需特定任务的编排代码，凸显了其作为基于LLM的MAS通用平台的有效性。DevAll可在该https URL获取。
英文摘要
Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks, yet their development forces a tradeoff: code frameworks are expressive but engineering-intensive, while no-code builders simplify authoring but constrain agent interactions to author-defined workflows. We present ChatDev 2.0: DevAll (hereafter DevAll), a no-code platform for building, executing, and inspecting heterogeneous MAS that delivers both high expressiveness and ease of use. In terms of expressiveness, DevAll pairs a declarative executable graph abstraction with a cycle-aware execution engine, so that heterogeneous agents and dynamic and cyclic interactions can be represented and executed within a single framework. For ease of use, an integrated visual interface lets users author, run, monitor, and inspect MAS, including human-in-the-loop steps, entirely without writing code. Experiments demonstrate that DevAll reproduces state-of-the-art MAS across three representative tasks at competitive performance and without task-specific orchestration code, highlighting its effectiveness as a general-purpose platform for LLM-based MAS. DevAll is available at https://github.com/OpenBMB/ChatDev.
CommentsAccepted at EMNLP 2026 Demo Track
