ChatDev 2.0: A No-Code Multi-Agent Platform for Developing Everything

arXiv:2609.00714 · cs.AI, cs.CL, cs.MA · Submitted 2026-09-01 · Read on arXiv

cs.AI, cs.CL, cs.MA

Submitted: 2026-09-01

Updated: 2026-09-01

Comments: Accepted at EMNLP 2026 Demo Track

Code: https://github.com/OpenBMB/ChatDev

Project page: https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/graph-flow.html

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: 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

Terminology

Abstract

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.

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