A Zero-Code Multi-Agent Platform for Developing Everything

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【📚 Developers | 👥 Contributors｜⭐️ ChatDev 1.0 (Legacy)】

ChatDev has evolved from a specialized software development multi-agent system into a comprehensive multi-agent orchestration platform.

• ChatDev 2.0 (DevAll) is a Zero-Code Multi-Agent Platform for "Developing Everything". It empowers users to rapidly build and execute customized multi-agent systems through simple configuration. No coding is required—users can define agents, workflows, and tasks to orchestrate complex scenarios such as data visualization, 3D generation, and deep research.
• ChatDev 1.0 (Legacy) operates as a Virtual Software Company. It utilizes various intelligent agents (e.g., CEO, CTO, Programmer) participating in specialized functional seminars to automate the entire software development life cycle—including designing, coding, testing, and documenting. It serves as the foundational paradigm for communicative agent collaboration.

• Jan 07, 2026: 🚀 We are excited to announce the official release of ChatDev 2.0 (DevAll)! This version introduces a zero-code multi-agent orchestration platform. The classic ChatDev (v1.x) has been moved to the chatdev1.0 branch for maintenance. More details about ChatDev 2.0 can be found on our official post.

• OS: macOS / Linux / WSL / Windows
• Python: 3.12+
• Node.js: 18+
• Package Manager: uv

1. Backend Dependencies (Python managed by uv):

   uv sync

2. Frontend Dependencies (Vite + Vue 3):

   cd frontend && npm install

• Environment Variables:

  cp .env.example .env

• Model Keys: Set API_KEY and BASE_URL in .env for your LLM provider.
• YAML placeholders: Use ${VAR}（e.g., ${API_KEY}）in configuration files to reference these variables.

Start both Backend and Frontent:

make dev

Then access the Web Console at http://localhost:5173.

1. Start Backend:

   # Run from the project root
   uv run python server_main.py --port 6400 --reload

   --reload watches the server's Python source folders only; agent-generated files under WareHouse/ no longer trigger restarts. Pass --reload-dir or --reload-exclude (repeatable) to customise.

2. Start Frontend:

   cd frontend
   VITE_API_BASE_URL=http://localhost:6400 npm run dev

   Then access the Web Console at http://localhost:5173.

   💡 Tip: If the frontend fails to connect to the backend, the default port 6400 may already be occupied. Please switch both services to an available port, for example:

   • Backend: start with --port 6401
   • Frontend: set VITE_API_BASE_URL=http://localhost:6401

• Help command:

  make help

• Sync YAML workflows to frontend:

  make sync

  Uploads all workflow files from yaml_instance/ to the database.

• Validate all YAML workflows:

  make validate-yamls

  Checks all YAML files for syntax and schema errors.

OpenClaw can integrate with ChatDev by invoking existing agent teams or dynamically creating new agent teams within ChatDev. To get started:

1. Start the ChatDev 2.0 backend.

2. Install the required skills for your OpenClaw instance:

   clawdhub install chatdev

3. Ask your OpenClaw to create a ChatDev workflow. For example:

• Automated information collection and content publishing

  Create a ChatDev workflow to automatically collect trending information, generate a Xiaohongshu post, and publish it.
  

• Multi-agent geopolitical simulation

  Create a ChatDev workflow with multiple agents to simulate possible future developments of the Middle East situation.
  

Alternatively, you can run the entire application using Docker Compose. This method simplifies dependency management and provides a consistent environment.

1. Prerequisites:

   • Docker and Docker Compose installed.
   • Ensure you have a .env file in the project root for your API keys.

2. Build and Run:

   # From the project root
   docker compose up --build

3. Access:

   • Backend: http://localhost:6400
   • Frontend: http://localhost:5173

The services will automatically restart if they crash, and local file changes will be reflected inside the containers for live development.

The DevAll interface provides a seamless experience for both construction and execution

• Tutorial: Comprehensive step-by-step guides and documentation integrated directly into the platform to help you get started quickly.

• Workflow: A visual canvas to design your multi-agent systems. Configure node parameters, define context flows, and orchestrate complex agent interactions with drag-and-drop ease.

• Launch: Initiate workflows, monitor real-time logs, inspect intermediate artifacts, and provide human-in-the-loop feedback.

For automation and batch processing, use our lightweight Python SDK to execute workflows programmatically and retrieve results directly.

from runtime.sdk import run_workflow

# Execute a workflow and get the final node message
result = run_workflow(
    yaml_file="yaml_instance/demo.yaml",
    task_prompt="Summarize the attached document in one sentence.",
    attachments=["/path/to/document.pdf"],
    variables={"API_KEY": "sk-xxxx"} # Override .env variables if needed
)

if result.final_message:
    print(f"Output: {result.final_message.text_content()}")

We have released the ChatDev Python SDK (PyPI package chatdev), so you can also run YAML workflow and multi-agent tasks directly in Python. For installation and version details, see PyPI: chatdev 0.1.0.

For secondary development and extensions, please proceed with this section.

Extend DevAll with new nodes, providers, and tools. The project is organized into a modular structure:

• Core Systems: server/ hosts the FastAPI backend, while runtime/ manages agent abstraction and tool execution.
• Orchestration: workflow/ handles the multi-agent logic, driven by configurations in entity/.
• Frontend: frontend/ contains the Vue 3 Web Console.
• Extensibility: functions/ is the place for custom Python tools.

Relevant reference documentation:

• Getting Started: Start Guide
• Core Modules: Workflow Authoring, Memory, and Tooling

We provide robust, out-of-the-box templates for common scenarios. All runnable workflow configs are located in yaml_instance/.

• Demos: Files named demo_*.yaml showcase specific features or modules.
• Implementations: Files named directly (e.g., ChatDev_v1.yaml) are full in-house or recreated workflows. As follows:

Category	Workflow	Case
📈 Data Visualization	data_visualization_basic.yaml data_visualization_enhanced.yaml	Prompt: "Create 4–6 high-quality PNG charts for my large real-estate transactions dataset."
🛠️ 3D Generation (Requires Blender & blender-mcp)	blender_3d_builder_simple.yaml blender_3d_builder_hub.yaml blender_scientific_illustration.yaml	Prompt: "Please build a Christmas tree."
🎮 Game Dev	GameDev_v1.yaml ChatDev_v1.yaml	Prompt: "Please help me design and develop a Tank Battle game."
📚 Deep Research	deep_research_v1.yaml	Prompt: "Research about recent advances in the field of LLM-based agent RL"
🎓 Teach Video	teach_video.yaml (Please run command uv add manim before running this workflow)	Prompt: "讲一下什么是凸优化"

For those implementations, you can use the Launch tab to execute them.

1. Select: Choose a workflow in the Launch tab.
2. Upload: Upload necessary files (e.g., .csv for data analysis) if required.
3. Prompt: Enter your request (e.g., "Visualize the sales trends" or "Design a snake game").

We welcome contributions from the community! Whether you're fixing bugs, adding new workflow templates, or sharing high-quality cases/artifacts produced by DevAll, your help is much appreciated. Feel free to contribute by submitting Issues or Pull Requests.

By contributing to DevAll, you'll be recognized in our Contributors list below. Check out our Developer Guide to get started!

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@article{chatdev,
    title = {ChatDev: Communicative Agents for Software Development},
    author = {Chen Qian and Wei Liu and Hongzhang Liu and Nuo Chen and Yufan Dang and Jiahao Li and Cheng Yang and Weize Chen and Yusheng Su and Xin Cong and Juyuan Xu and Dahai Li and Zhiyuan Liu and Maosong Sun},
    journal = {arXiv preprint arXiv:2307.07924},
    url = {https://arxiv.org/abs/2307.07924},
    year = {2023}
}

@article{colearning,
    title = {Experiential Co-Learning of Software-Developing Agents},
    author = {Chen Qian and Yufan Dang and Jiahao Li and Wei Liu and Zihao Xie and Yifei Wang and Weize Chen and Cheng Yang and Xin Cong and Xiaoyin Che and Zhiyuan Liu and Maosong Sun},
    journal = {arXiv preprint arXiv:2312.17025},
    url = {https://arxiv.org/abs/2312.17025},
    year = {2023}
}

@article{macnet,
    title={Scaling Large-Language-Model-based Multi-Agent Collaboration},
    author={Chen Qian and Zihao Xie and Yifei Wang and Wei Liu and Yufan Dang and Zhuoyun Du and Weize Chen and Cheng Yang and Zhiyuan Liu and Maosong Sun}
    journal={arXiv preprint arXiv:2406.07155},
    url = {https://arxiv.org/abs/2406.07155},
    year={2024}
}

@article{iagents,
    title={Autonomous Agents for Collaborative Task under Information Asymmetry},
    author={Wei Liu and Chenxi Wang and Yifei Wang and Zihao Xie and Rennai Qiu and Yufan Dnag and Zhuoyun Du and Weize Chen and Cheng Yang and Chen Qian},
    journal={arXiv preprint arXiv:2406.14928},
    url = {https://arxiv.org/abs/2406.14928},
    year={2024}
}

@article{puppeteer,
      title={Multi-Agent Collaboration via Evolving Orchestration}, 
      author={Yufan Dang and Chen Qian and Xueheng Luo and Jingru Fan and Zihao Xie and Ruijie Shi and Weize Chen and Cheng Yang and Xiaoyin Che and Ye Tian and Xuantang Xiong and Lei Han and Zhiyuan Liu and Maosong Sun},
      journal={arXiv preprint arXiv:2505.19591},
      url={https://arxiv.org/abs/2505.19591},
      year={2025}
}

If you have any questions, feedback, or would like to get in touch, please feel free to reach out to us via email at qianc62@gmail.com