GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI

• URL: http://arxiv.org/abs/2409.01392v1
• Date: Mon, 2 Sep 2024 17:44:10 GMT
• Title: GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI
• Authors: Xiangyuan Xue, Zeyu Lu, Di Huang, Wanli Ouyang, Lei Bai,
• Abstract summary: This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
• Score: 64.57616646552869
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Much previous AI research has focused on developing monolithic models to maximize their intelligence and capability, with the primary goal of enhancing performance on specific tasks. In contrast, this paper explores an alternative approach: collaborative AI systems that use workflows to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex workflows, offering greater flexibility and scalability compared to monolithic models. The core innovation of GenAgent lies in representing workflows with code, alongside constructing workflows with collaborative agents in a step-by-step manner. We implement GenAgent on the ComfyUI platform and propose a new benchmark, OpenComfy. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations, showing its capability to generate complex workflows with superior effectiveness and stability.

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