Galago - Open Source Scheduler

Hey lab automation community! :waving_hand:

We’re excited to announce something we’ve been working on for quite some time - Galago, a modern open-source lab automation software designed for real-world laboratory environments.

What Makes This Different

Unlike expensive commercial solutions that can cost hundreds of thousands (or more) and require lengthy integration cycles, Galago was built from the ground up to be:

  • Open source - Apache 2.0 license, no vendor lock-in
  • Beta-tested - Tested by several biotech companies outside of Science Corp.
  • Battle-tested - Over 5,000 successful automation runs in production
  • Minimal setup required - Get running quickly without complex configuration
  • Multi-device integration - Unified interface for diverse lab equipment
  • Extensive driver collection - Over 20 instrument drivers.
  • Highlights:
    • Modern stack: Next.js, FastAPI, SQLAlchemy, and gRPC
    • No scripting required for basic workflows
    • Built-in code editor supporting Python, JavaScript, and C# with database access libraries
    • Drag-and-drop form builder for rapid UI creation
    • Real-time variables accessible in scripts and tool inputs for read/update operations
    • Full protocol logic: conditionals, loops, error handling, etc.

Why We’re Open Sourcing This

Frankly, we’re tired of seeing promising biotech companies (especially startups) held back by prohibitively expensive automation solutions. The current landscape often means:

  • Massive upfront costs that drain precious funding
  • Long integration timelines with third-party vendors who may not understand your specific needs
  • Inflexible systems that can’t adapt as your research evolves
  • Vendor lock-in that limits future options

What began as a solution for our own lab at Science Corporation is now ready to empower labs around the world. We believe powerful automation tools should be within reach for every lab, regardless of resources.

Getting Started

Galago consists of two main components:

Tools Manager: Python-based interface managing tool servers

  • Multiple deployment options including Windows installer or just
    • pip install galago-tools

Web App: Complete interface with database, scheduler, and execution engine

  • Docker-based deployment for any OS

Ready to try it? Check out the documentation and installation guide at galago.bio. Pip installer and Docker images are public now - the repositories will be made public in a couple of weeks.

Questions or want to collaborate? Drop a comment below or reach out directly.

Let’s make lab automation accessible to everyone.

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Congrats!

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congrats!

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Thank you @luisvillaautomata

Thanks @rickwierenga . Inspired by the impact PLR has had! Hope we can work together to democratize lab automation!

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Congratulations!

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@evwolfson good luck! Let us know if you run into issues or if there are any specific drivers you need. We have several drivers in dev that we could prioritize based on need.

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we should collaborate on integrating PLR drivers into Galago!

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We’ve actually added both PLR and PyHamilton as tools available in Galago. Just updated the documentation with more info on each tool. Here’s a link to PLR and PyHamilton. Let us know what you think!

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Downloaded and will play around with it!

I had to specify the platform in the docker compose.yml to emulate in a AMD64 Docker container as I am on a Silicon Mac and got I some errors when composing out of the box, specifically: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested. No big deal, easy to emulate, more of a FYI here

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Hi @Brad , thank you for the feedback. Just made the image multi platform, feel free to do a force rebuild. Keep us updated on how the test goes!

Looks great. Looking forward to seeing the code when it is released.

I think something like this is very important for not only the reasons you mentioned but also for standardizing driver development to avoid wasted effort. Having a scheduler out there that has been tested in production and relased into the wild could be a game changer like e.g. blender.

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