"Daggr allows developers to define workflows programmatically in Python while automatically generating a visual canvas that exposes intermediate states, inputs, and outputs for each step in the pipeline. Daggr simplifies applied AI development by organizing workflows as directed graphs, allowing for independent inspection and re-execution of each node. This method enhances debugging and speeds up iteration by tackling the issue of slow and unclear experimentation,"
"The library follows a code-first approach. Developers define nodes and connections directly in Python, and Daggr renders a corresponding visual interface for inspection. This contrasts with GUI-driven workflow builders, which often sacrifice version control and flexibility. With Daggr, the visual layer is derived from code rather than replacing it, allowing workflows to remain reproducible and easy to review."
Daggr provides a code-first framework for building multi-step AI workflows as directed graphs, automatically rendering a visual canvas that reveals inputs, outputs, and intermediate states for each step. The system supports independent inspection and re-execution of nodes, reducing the need to rerun entire pipelines and accelerating debugging and iteration. Daggr offers three node types—GradioNode, FnNode, and InferenceNode—to reuse Gradio apps, wrap Python functions, and integrate hosted models. Automatic state persistence saves workflow state, cached results, inputs, and layout so developers can pause, resume, and compare node implementations without losing context.
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