daggr

name	daggr
description	Build DAG-based AI pipelines connecting Gradio Spaces, HuggingFace models, and Python functions into visual workflows. Use when asked to create a workflow, build a pipeline, connect AI models, chain Gradio Spaces, create a daggr app, build multi-step AI applications, or orchestrate ML models. Triggers on: "build a workflow", "create a pipeline", "connect models", "daggr", "chain Spaces", "AI pipeline".
license	MIT
metadata	{"author":"gradio-app","version":"1.1"}

daggr

Build visual DAG pipelines connecting Gradio Spaces, HF Inference Providers, and Python functions.

Full docs: https://raw.githubusercontent.com/gradio-app/daggr/refs/heads/main/README.md

Quick Start

from daggr import GradioNode, FnNode, InferenceNode, Graph, ItemList
import gradio as gr

graph = Graph(name="My Workflow", nodes=[node1, node2, ...])
graph.launch()  # Starts web server with visual DAG UI

Node Types

GradioNode - Gradio Spaces

node = GradioNode(
    space_or_url="owner/space-name",
    api_name="/endpoint",
    inputs={
        : gr.Textbox(label=),   
        : other_node.output_port,       
        : ,             
    },
    postprocess= *returns: returns[],   
    outputs={: gr.Image(label=)},
)


img = GradioNode(, api_name=,
    inputs={: gr.Textbox(), : },
    postprocess= imgs, *_: imgs[][],
    outputs={: gr.Image()})