Hugging Face's Gradio framework has introduced gr.Workflow, a visual pipeline builder that lets developers construct complex AI applications without leaving the browser. The new feature presents multi-step AI processes as drag-and-drop node graphs, where each step can be a Python function, a model from Hugging Face Inference Providers, or another Gradio Space. Developers can run and debug each step independently, inspect intermediate results in real-time, and watch data flow through the pipeline.
Every workflow automatically converts into a REST API with no additional configuration required, with each output becoming its own callable endpoint. Developers can invoke workflows from Python using the Gradio client or via direct HTTP calls. The feature supports parallel execution patterns, allowing a single input to fan out across multiple models simultaneously, and can be deployed to Hugging Face Spaces with a single command. Gradio also supports running custom GPU-accelerated models inside workflows through ZeroGPU, eliminating the need to rely solely on remote model providers.
Key Points
gr.Workflow visualizes AI pipelines as interactive graphs with runnable nodes and visible intermediate results
Automatically generates REST APIs for each workflow output without extra configuration
Supports parallel execution patterns, model inference via Hugging Face, and custom GPU-accelerated functions
Enables one-click deployment to Hugging Face Spaces with built-in debugging and testing interface