Large language models (LLMs) have shown promising advancements in tackling human-level tasks, wherein generating workflows for collaborative AI systems remains a critical and challenging step. To explore this frontier, we introduce ComfyFlow, a comprehensive benchmark to evaluate current LLMs’ ability to generate executable and instruction-following AIGC workflows in ComfyUI. The dataset includes 400 diverse visual generation tasks across 20 categories, supported by 10K training examples constructed from knowledge bases, which contain detailed annotations for 2,480 nodes and 3,298 workflows. We establish a systematic evaluation protocol that quantifies performance across multiple dimensions, ranging from basic format validity to multi-level hallucination rates. Our extensive evaluations show that: 1) ComfyFlow presents a substantial challenge even for top-tier proprietary LLMs such as GPT-5.1 and the Claude series; 2) Open-source models achieve new state-of-the-art results after post-training, yet struggle with long-horizon planning as the number of nodes increases; 3) Different post-training strategies offer complementary benefits in following instructions and mitigating hallucinations. By establishing both a challenging benchmark and a principled evaluation scheme, ComfyFlow lays the foundation for developing more intelligent and reliable collaborative AI systems.

AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated knowledge bases, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97% format validity rate, along with high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Qualitative analysis further highlights our model’s strength in synthesizing intricate workflows with diverse nodes, aligning with human instructions, and generalizing to newly introduced nodes, underscoring the potential of long CoT reasoning in AI art creation.