ComfyMind: Toward General-Purpose Generation via Tree-Based Planning and
  Reactive Feedback
Authors: name: Litao Guo, name: Xinli Xu, name: Luozhou Wang, name: Jiantao Lin, name: Jinsong Zhou, name: Zixin Zhang, name: Bolan Su, name: Ying-Cong Chen

With the rapid advancement of generative models, general-purpose generation
has gained increasing attention as a promising approach to unify diverse tasks
across modalities within a single system. Despite this progress, existing
open-source frameworks often remain fragile and struggle to support complex
real-world applications due to the lack of structured workflow planning and
execution-level feedback. To address these limitations, we present ComfyMind, a
collaborative AI system designed to enable robust and scalable general-purpose
generation, built on the ComfyUI platform. ComfyMind introduces two core
innovations: Semantic Workflow Interface (SWI) that abstracts low-level node
graphs into callable functional modules described in natural language, enabling
high-level composition and reducing structural errors; Search Tree Planning
mechanism with localized feedback execution, which models generation as a
hierarchical decision process and allows adaptive correction at each stage.
Together, these components improve the stability and flexibility of complex
generative workflows. We evaluate ComfyMind on three public benchmarks:
ComfyBench, GenEval, and Reason-Edit, which span generation, editing, and
reasoning tasks. Results show that ComfyMind consistently outperforms existing
open-source baselines and achieves performance comparable to GPT-Image-1.
ComfyMind paves a promising path for the development of open-source
general-purpose generative AI systems. Project page:
https://github.com/LitaoGuo/ComfyMind