Welcome to PyLabRobot’s documentation!

PyLabRobot is a hardware agnostic, pure Python SDK for liquid handling robots and accessories.

• GitHub repository: PyLabRobot/pylabrobot

• Community: https://discuss.pylabrobot.org

• Paper: https://www.cell.com/device/fulltext/S2666-9986(23)00170-9

Note

PyLabRobot is different from PyHamilton. While both packages are created by the same lab and both provide a Python interfaces to Hamilton robots, PyLabRobot aims to provide a universal interface to many different robots runnable on many different computers, where PyHamilton is a Windows only interface to Hamilton’s VENUS.

Used by

Documentation

User Guide

• User guide
  • Installation
  • Getting started with liquid handling on a Hamilton STAR(let)
  • Using the Visualizer
  • Using trackers
  • Writing robot agnostic methods
  • Hamilton STAR
  • Plate Readers
  • Cytation 5
  • Pumps
  • Scales
  • Temperature controllers (heaters and coolers)
  • Tilting
  • Heater Shakers
  • Fans
  • Configuring PLR

Development

• Contributor guide
  • Contributing to PyLabRobot
  • How to Open Source
  • Contributing a new type of machine to PLR
  • Adding support for a new machine of an existing type

Resource Library

• Resource Library
  • Resources Introduction
  • Defining custom resources
  • Resource subclasses
  • Library

API documentation

• API
  • Subpackages

Citing

If you use PyLabRobot in your research, please cite the following paper:

@article{WIERENGA2023100111,
  title = {PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories},
  journal = {Device},
  volume = {1},
  number = {4},
  pages = {100111},
  year = {2023},
  issn = {2666-9986},
  doi = {https://doi.org/10.1016/j.device.2023.100111},
  url = {https://www.sciencedirect.com/science/article/pii/S2666998623001709},
  author = {Rick P. Wierenga and Stefan M. Golas and Wilson Ho and Connor W. Coley and Kevin M. Esvelt},
  keywords = {laboratory automation, open source, standardization, liquid-handling robots},
}

Wierenga, R., Golas, S., Ho, W., Coley, C., & Esvelt, K. (2023). PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories. Device. https://doi.org/10.1016/j.device.2023.100111

Cited by:

• Tom, Gary, et al. “Self-driving laboratories for chemistry and materials science.” Chemical Reviews (2024).

• Anhel, Ana-Mariya, Lorea Alejaldre, and Ángel Goñi-Moreno. “The Laboratory Automation Protocol (LAP) Format and Repository: a platform for enhancing workflow efficiency in synthetic biology.” ACS synthetic biology 12.12 (2023): 3514-3520.

• Bultelle, Matthieu, Alexis Casas, and Richard Kitney. “Engineering biology and automation–Replicability as a design principle.” Engineering Biology (2024).

• Pleiss, Jürgen. “FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science.” ACS Catalysis 14.4 (2024): 2709-2718.

• Gopal, Anjali, et al. “Will releasing the weights of large language models grant widespread access to pandemic agents?.” arXiv preprint arXiv:2310.18233 (2023).

• Padhy, Shakti P., and Sergei V. Kalinin. “Domain hyper-languages bring robots together and enable the machine learning community.” Device 1.4 (2023).

• Beaucage, Peter A., Duncan R. Sutherland, and Tyler B. Martin. “Automation and Machine Learning for Accelerated Polymer Characterization and Development: Past, Potential, and a Path Forward.” Macromolecules (2024).

• Bultelle, Matthieu, Alexis Casas, and Richard Kitney. “Construction of a Calibration Curve for Lycopene on a Liquid-Handling Platform─ Wider Lessons for the Development of Automated Dilution Protocols.” ACS Synthetic Biology (2024).

• Hysmith, Holland, et al. “The future of self-driving laboratories: from human in the loop interactive AI to gamification.” Digital Discovery 3.4 (2024): 621-636.

• Casas, Alexis, Matthieu Bultelle, and Richard Kitney. “An engineering biology approach to automated workflow and biodesign.” (2024).

• Jiang, Shuo, et al. “ProtoCode: Leveraging Large Language Models for Automated Generation of Machine-Readable Protocols from Scientific Publications.” arXiv preprint arXiv:2312.06241 (2023).

• Jiang, Shuo, et al. “ProtoCode: Leveraging large language models (LLMs) for automated generation of machine-readable PCR protocols from scientific publications.” SLAS technology 29.3 (2024): 100134.

• Thieme, Anton, et al. “Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment.” SLAS technology (2024): 100180.

• Daniel, Čech. Adaptace algoritmů pro navigaci robota na základě apriorních informací. BS thesis. České vysoké učení technické v Praze. Vypočetní a informační centrum., 2024.