title: GenAgent: Build Collaborative AI Systems with Automated Workflow
    Generation -- Case Studies on ComfyUI
summary: 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.
paper_authors._id: 66d83ad80d88366001ccf370
paper_authors.name: Xiangyuan Xue
paper_authors._id: 66d83ad80d88366001ccf371
paper_authors.name: Zeyu Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T12:00:46.104Z
paper_authors._id: 635626a8ec32331b227f407b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/635626a8ec32331b227f407b/KRkAEN3eXN_mYzzJQ_8dO.jpeg
paper_authors.fullname: LuZeyu
paper_authors.type: user
paper_authors.user: whlzy
paper_authors._id: 66d83ad80d88366001ccf372
paper_authors.name: Di Huang
paper_authors._id: 66d83ad80d88366001ccf373
paper_authors.name: Wanli Ouyang
paper_authors._id: 66d83ad80d88366001ccf374
paper_authors.name: Lei Bai
paper_title: GenAgent: Build Collaborative AI Systems with Automated Workflow
    Generation -- Case Studies on ComfyUI
paper_summary: 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.
paper_ai_keywords: LLM-based framework collaborative AI systems workflows data sources pipelines GenAgent code collaborative agents OpenComfy run-level evaluations task-level evaluations