We’ve just launched the PyLabRobot Cookbook (PR #726) - a curated collection of modular, interoperable code examples (“recipes”) designed to teach both humans and AI how to automate, one recipe at a time.
While shared collections like the Opentrons Protocol Library have focused on full workflows within a single ecosystem, the PyLabRobot Cookbook extends that idea to cross-instrument and cross-concept examples. Each recipe is concise, focused, and reusable, showing practical ways to build and structure real automation logic that can be applied across different contexts and devices.
The Cookbook grew out of a clear need for more practical examples. Over the past year, one of the most common requests from the community has been for complete, ready-to-run code that bridges the gap between documentation and full automation workflows. At the same time, as AI systems begin to assist with automation development, they rely on large, diverse collections of example code to learn from.
This kind of open, interoperable example library is only possible because of PyLabRobot’s architecture — a flexible, extensible framework that unifies devices from different manufacturers under a single, transparent interface.
By making automation behavior explicit and composable, PyLabRobot provides the foundation that allows both human developers and AI systems to explore, understand, and extend real automation logic. The Cookbook builds directly on this foundation, helping shape the next generation of AI-powered lab automation.
The first recipe — “Moving plate onto Alpaqua magnet using CORE grippers” — demonstrates plate handling with the Hamilton STAR, combining gripper control, resource movement, position checks, and PLR-unique automatic correction of plate placement onto a magnet adapter. It serves as a foundational example for future, multi-OEM workflows.
Over time, the Cookbook will grow into a 3-tier structure:
Tutorials — introductory, machine-specific examples.
Cookbook Recipes — functional snippets combining advanced concepts.
Protocol Library (future) — complete end-to-end automation workflows.
The goal is to make automation discoverable, teachable, and trainable — for both human developers building protocols and AI systems learning from them.
If you have ideas for new Recipes or want to contribute, please see the Contributor Guide:
https://docs.pylabrobot.org/contributor_guide/adding-coookbook-recipes.html
You can explore the Cookbook here:
https://docs.pylabrobot.org/cookbook/index.html
Let’s cook up the next generation of automation together.
