--- title: "Wire It, Run It, Deploy It: AI Workflows in Gradio" url: https://daily.dev/posts/wire-it-run-it-deploy-it-ai-workflows-in-gradio-gqngjc5a8 source_url: https://huggingface.co/blog/gradio-workflow-guide type: article source: "Hugging Face" published: 2026-08-25T03:19:30.215Z updated: 2026-08-25T18:55:30.414Z tags: ["huggingface", "gradio"] reading_time: 5 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Wire It, Run It, Deploy It: AI Workflows in Gradio **[Hugging Face](https://daily.dev/sources/huggingface)** · 5 min read · 0 upvotes · 0 comments ## Summary gr.Workflow is a new Gradio feature that lets developers build AI pipelines as a graph of typed nodes on a drag-and-drop canvas, where every node is runnable and every intermediate result visible. The same graph automatically becomes a REST API and can be deployed to Hugging Face Spaces with one command. Example workflows shown include an image editor powered by Qwen-Image-Edit, a media studio chaining FLUX image generation, background removal, text-to-speech, and an LLM title generator, a parallel fan-out image generation demo, a Hugging Face dataset profiler, and a GPU-based video animation node using ZeroGPU and Diffusers. Each output node becomes a named REST endpoint callable via the Gradio client or plain curl. Building a workflow is as simple as wrapping Python functions with gr.Workflow(bind=[...]).launch(). ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is gr.Workflow in Gradio and what does it do? gr.Workflow is a Gradio feature that lets developers describe an AI pipeline as a graph of typed nodes, with Gradio rendering a drag-and-drop canvas where every node is runnable and every intermediate result visible. The same graph automatically becomes a REST API and can be deployed to Hugging Face Spaces with a single command, without extra API setup work. _Building multi-step AI pipelines gets easier to track when you follow releases like gr.Workflow on daily.dev._ ### How do I expose a Gradio workflow's outputs as REST API endpoints? Each output node in a gr.Workflow graph automatically becomes a named REST endpoint based on its label, with no extra work required. You can call these endpoints with the gradio_client Python library using Client(space_name).predict(..., api_name="/endpoint_name"), or reach them directly over plain HTTP with curl against the /gradio_api/call/ path. _Developers wiring up model APIs can follow Gradio's evolving tooling on daily.dev._ ### How can I run a GPU-based model inside a Gradio Space node instead of using an external inference API? An fn node in gr.Workflow is just Python, so it can run a model directly inside the Space on a GPU rather than calling an external service. Decorating the bound function with @spaces.GPU lets ZeroGPU allocate a GPU for that call, run the model, and release it afterward, with gr.Workflow itself needing no knowledge of the GPU setup. _Teams choosing between hosted inference and in-Space GPU execution track these tooling changes on daily.dev._ ## Similar posts on daily.dev - [Daggr Introduced as an Open-Source Python Library for Inspectable AI Workflows](https://daily.dev/posts/daggr-introduced-as-an-open-source-python-library-for-inspectable-ai-workflows-w5uxajhhs) · InfoQ · 4 upvotes · 0 comments - [How to Build Your AI Demos with Gradio](https://daily.dev/posts/how-to-build-your-ai-demos-with-gradio-vztvjccuk) · freeCodeCamp · 8 upvotes · 0 comments --- Tags: [#huggingface](https://daily.dev/tags/huggingface), [#gradio](https://daily.dev/tags/gradio) [View this post on daily.dev](https://daily.dev/posts/wire-it-run-it-deploy-it-ai-workflows-in-gradio-gqngjc5a8)