GenAgent: Build Collaborative AI Systems with Automated Workflow
  Generation -- Case Studies on ComfyUI
Authors: name: Xiangyuan Xue, name: Zeyu Lu, name: Di Huang, name: Wanli Ouyang, name: Lei Bai

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.