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GeneralTier 1 source5 min readTue, 25 Aug 2026

Gradio workflows turn AI pipelines into visual, deployable APIs

Hugging Face Blog

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Originally published as “Wire It, Run It, Deploy It: AI Workflows in Gradio

Highlights

  • Build multi-step AI pipelines as drag-and-drop node graphs
  • See every intermediate result instantly, no print-debugging
  • Deploy to Spaces and get REST endpoints automatically
  • Chain Hugging Face models, Spaces, and custom GPU code in one canvas
  • Call any output as an API endpoint from Python or curl

Gradio's new gr.Workflow feature turns multi-step AI pipelines into visual, drag-and-drop interfaces. Instead of wiring steps together in Python code and debugging with print statements, you build a graph of typed nodes—each runnable, each showing intermediate results instantly. The same workflow automatically becomes a REST API and deploys to Hugging Face Spaces in one command. Examples include image editing, parallel image generation, dataset profiling, and chaining models across Inference Providers and existing Spaces. Nodes can run your own GPU code, call Hugging Face models, invoke other Gradio Spaces, or execute custom Python functions. Every output node becomes its own API endpoint, callable from Python or curl without extra setup.

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