Introducing Daggr: Chain apps programmatically, inspect visually
Introducing Daggr: Chain apps programmatically, inspect visually Introducing Daggr: Chain apps programmatically, inspect visually Published January 29, 2026 Update on GitHub Upvote 107 merve merve yuvraj sharma ysharma Abubakar Abid abidlabs hysts hysts Pedro Cuenca pcuenq 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!
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- Introducing Daggr: Chain apps programmatically, inspect visually Published January 29, 2026 Update on GitHub Upvote 107 merve merve yuvraj sharma ysharma Abubakar Abid abidlabs hysts hysts Pedro Cuenca pcuenq 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.