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

• URL: http://arxiv.org/abs/2609.00714v1
• Date: Tue, 01 Sep 2026 04:47:30 GMT
• Title: ChatDev 2.0: A No-Code Multi-Agent Platform for Developing Everything
• Abstract summary: Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks.<n>We present ChatDev 2.0: DevAll, a no-code platform for building, executing, and inspecting heterogeneous MAS.<n>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.
• Score: 19.850023518195695
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist [107.04196084992907]
  We introduce UniVA, an omni-capable multi-agent framework for next-generation video generalists.<n>UniVA employs a Plan-and-Act dual-agent architecture that drives a highly automated and proactive workflow.<n>We also introduce UniVA-Bench, a benchmark suite of multi-step video tasks spanning understanding, editing, segmentation, and generation.
  arXiv  Detail & Related papers  (2025-11-11T17:58:13Z)
• Instruction Agent: Enhancing Agent with Expert Demonstration [12.67489098612846]
  Graphical user interface (GUI) agents have advanced rapidly but still struggle with complex tasks involving novel UI elements, long-horizon actions, and personalized trajectories.<n>In this work, we introduce Instruction Agent, a GUI agent that leverages expert demonstrations to solve such tasks, enabling completion of otherwise difficult tasks.<n>Given a single demonstration, the agent extracts step-by-step instructions and executes them by strictly following the trajectory intended by the user, which avoids making mistakes during execution.
  arXiv  Detail & Related papers  (2025-09-08T18:00:12Z)
• ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents [40.697759330690815]
  ScreenCoder is a modular multi-agent framework that decomposes the task into three interpretable stages: grounding, planning, and generation.<n>By assigning these distinct responsibilities to specialized agents, our framework achieves significantly higher robustness and fidelity than end-to-end approaches.<n>Our approach achieves state-of-the-art performance in layout accuracy, structural coherence, and code correctness.
  arXiv  Detail & Related papers  (2025-07-30T16:41:21Z)
• AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation [0.0]
  We propose a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks.<n>In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code.
  arXiv  Detail & Related papers  (2025-07-26T10:10:02Z)
• AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems [31.113305753414913]
  AUTOGEN STUDIO is a no-code developer tool for rapidly prototyping multi-agent systems. It provides an intuitive drag-and-drop UI for agent specification, interactive evaluation, and a gallery of reusable agent components.
  arXiv  Detail & Related papers  (2024-08-09T03:27:37Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [57.98933460388985]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices.<n>Our agent constructs a flexible action space that enhances adaptability across various applications.<n>Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.<n>We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation [61.68049335444254]
  Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP) With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T08:29:03Z)
• Executable Code Actions Elicit Better LLM Agents [76.95566120678787]
  This work proposes to use Python code to consolidate Large Language Model (LLM) agents' actions into a unified action space (CodeAct) integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language.
  arXiv  Detail & Related papers  (2024-02-01T21:38:58Z)
• i-Code Studio: A Configurable and Composable Framework for Integrative AI [93.74891865028867]
  We propose the i-Code Studio, a flexible and composable framework for Integrative AI. The i-Code Studio orchestrates multiple pre-trained models in a fine-tuning-free fashion to conduct complex multimodal tasks. The i-Code Studio achieves impressive results on a variety of zero-shot multimodal tasks, such as video-to-text retrieval, speech-to-speech translation, and visual question answering.
  arXiv  Detail & Related papers  (2023-05-23T06:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.