Suff Syed
2024
AUTOGEN STUDIO: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Victor Dibia
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Jingya Chen
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Gagan Bansal
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Suff Syed
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Adam Fourney
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Erkang Zhu
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Chi Wang
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Saleema Amershi
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
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
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Co-authors
- Victor Dibia 1
- Jingya Chen 1
- Gagan Bansal 1
- Adam Fourney 1
- Erkang Zhu 1
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