@inproceedings{dibia-etal-2024-autogen,
    title = "{AUTOGEN} {STUDIO}: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems",
    author = "Dibia, Victor  and
      Chen, Jingya  and
      Bansal, Gagan  and
      Syed, Suff  and
      Fourney, Adam  and
      Zhu, Erkang  and
      Wang, Chi  and
      Amershi, Saleema",
    editor = "Hernandez Farias, Delia Irazu  and
      Hope, Tom  and
      Li, Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2024.emnlp-demo.8/",
    doi = "10.18653/v1/2024.emnlp-demo.8",
    pages = "72--79",
    abstract = "Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous do- mains. However, specifying their parameters (such as models, tools, and orchestration mechanisms etc,.) and debugging them remains challenging for most developers. To address this challenge, we present AUTOGEN STUDIO, a no-code developer tool for rapidly prototyping, debugging, and evaluating multi-agent work- flows built upon the AUTOGEN framework. AUTOGEN STUDIO offers a web interface and a Python API for representing LLM-enabled agents using a declarative (JSON-based) specification. It provides an intuitive drag-and-drop UI for agent workflow specification, interactive evaluation and debugging of workflows, and a gallery of reusable agent components. We highlight four design principles for no-code multi-agent developer tools and contribute an open-source implementation. https://github.com/microsoft/autogen/tree/autogenstudio/samples/apps/autogen-studio"
}