• Introducing Daggr: Chain apps programmatically, inspect visually TL;DR: Daggr is a new, open-source Python library for building AI workflows that connect Gradio apps, ML models, and custom functions. • It automatically generates a visual canvas where you can inspect intermediate outputs, rerun individual steps, and manage state for complex pipelines, all in a few lines of Python code! • Table of Contents - Background - Getting Started - Sharing Your Workflows - End-to-End Example with Different Nodes - Next Steps Background If you’ve built AI applications that combine multiple models or processing steps, you know the pain: chaining API calls, debugging pipelines, and losing track of intermediate results. • When something goes wrong in step 5 of a 10-step workflow, you often have to re-run everything just to see what happened. • Most developers either build fragile scripts that are hard to debug or turn to heavy orchestration platforms designed for production pipelines-not rapid experimentation. • We’ve been working on Daggr to solve problems we kept running into when building AI demos and workflows: Visualize your code flow: Unlike node-based GUI editors, where you drag and connect nodes visually, Daggr takes a code-first approach.
Article Summaries:
- Daggr is a new open‑source Python library that lets developers build AI workflows by writing code while automatically generating a visual canvas for inspection. Created by the Gradio team, Daggr connects Gradio Spaces, ML models, and custom functions into a single pipeline. The visual interface shows intermediate outputs, allows individual step reruns, and supports “backup nodes” for resilient workflows. It also persists state, inputs, and canvas layout, enabling multiple workspaces (“sheets”) within one app. Installation is simple (
pip install daggr), and the library demonstrates integration with public Gradio Spaces through a concise example that chains image generation and background removal.
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