Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language
For developers and practitioners in industrial automation, this benchmark addresses the costly and error-prone manual construction of executable visual workflows by evaluating LLMs' ability to automate this process.
Chat2Workflow is a benchmark for generating executable visual workflows from natural language, built from real-world business workflows. Experiments show that current LLMs struggle to generate correct, stable workflows, with the proposed agentic framework achieving up to 5.34% resolve rate gains, highlighting a significant gap for industrial automation.
At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully design workflows, write prompts for each step, and repeatedly revise the logic as requirements evolve-making development costly, time-consuming, and error-prone. To study whether large language models can automate this multi-round interaction process, we introduce Chat2Workflow, a benchmark for generating executable visual workflows directly from natural language, and propose a robust agentic framework to mitigate recurrent execution errors. Chat2Workflow is built from a large collection of real-world business workflows, with each instance designed so that the generated workflow can be transformed and directly deployed to practical workflow platforms such as Dify and Coze. Experimental results show that while state-of-the-art language models can often capture high-level intent, they struggle to generate correct, stable, and executable workflows, especially under complex or changing requirements. Although our agentic framework yields up to 5.34% resolve rate gains, the remaining real-world gap positions Chat2Workflow as a foundation for advancing industrial-grade automation. Code is available at https://github.com/zjunlp/Chat2Workflow.