AUTOGEN STUDIO: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Victor Dibia, Jingya Chen, Gagan Bansal, Suff Syed, Adam Fourney, Erkang Zhu, Chi Wang, Saleema Amershi
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
BibTeX
@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://aclanthology.org/2024.emnlp-demo.8/",
doi = "10.18653/v1/2024.emnlp-demo.8",
pages = "72--79"
}