Welcome to UniteLabs

UniteLabs connects lab instruments to one common interface. Each instrument is paired with a connector that exposes its functions as typed actions that you can list, inspect, and call: from Python, over the REST API, or in the platform's web interface, with no vendor-specific code on your side. Workflows written in Python coordinate several instruments, execute on the platform, and record every run with its inputs, logs, and results.

Calling a connected instrument from Python:

import asyncio

from unitelabs.sdk import AsyncApiClient
from unitelabs.liquid_handling.hamilton import MicrolabSTAR


async def main():
    # Connect by the name the instrument carries in your workspace
    # AsyncApiClient() reads BASE_URL, AUTH_URL, and the client credentials from .env
    hamilton = MicrolabSTAR(name="Microlab STAR", client=AsyncApiClient())
    await hamilton.initialize()

    # tip_rack and plate come from your deck layout
    await hamilton.pipettes.pick_up_tips_from(channels=[0], rack=tip_rack)
    await hamilton.pipettes.aspirate(plate["A1"], channels=[0], volume=[50])
    await hamilton.pipettes.dispense(plate["B1"], channels=[0], volume=[50])


asyncio.run(main())

The same pattern works for any connected instrument, a thermocycler, a plate reader, a balance, a liquid handler. The use cases show where it leads, from a single device to workflows deployed across several workcells.

The parts of the system

These parts sit in three places:

• In the lab: the connectors and GroundControl, on the computer wired to your instruments.
• On your machine: the scripts and workflows you write in your own editor, and from which you deploy workflows.
• In the cloud: your tenant, which the SDK, the REST API, and the web interface all talk to.

The lab always connects outward with one encrypted connection from the lab computer to your tenant, so no inbound ports need to be opened. Without that connection a connector still works, but only from inside the lab network. See How it works and Network requirements.

Set up your lab in four steps

Your workspace, called a tenant, and the credentials for it come from UniteLabs. Get access lists the five things you receive, where each one goes, and what to have ready on your side.

1. Install GroundControl on the lab computer wired to your instruments.
2. Add the instrument in GroundControl and pick its connector: browse the UniteLabs registry and download the one for your instrument, and GroundControl fetches the build matching your operating system. The UniteLabs Hub shows publicly what exists. One configuration covers both how the connector reaches the instrument and how it reaches your tenant.
3. Prepare your development machine, then install the SDK. The registry credentials fetch the packages, the client credentials let your code authenticate.
4. Run your first script: Calling a connector discovers what your instrument exposes and calls it.

The documentation, section by section

The four middle sections follow the life of your lab code: connect instruments, control them, turn scripts into workflows, work with the data.

Integrate: connect your instruments

Start with What is a connector?, or go straight to Connect a device. This section covers what a connector is, the ways to run one, and how to call it from GroundControl, the web interface, or Python, including listing a connector's modules and actions, reading properties, and subscribing to sensor values that stream while an instrument runs.

Operate: control instruments from Python

Start with Your first protocol, which builds the deck layout the snippet above leaves out and runs a full transfer. The Operate overview lists the rest: labware definitions, deck layout, pipetting, tip handling, labware transport, and guides for Hamilton STAR and Vantage, Agilent Bravo, and Tecan Fluent.

Automate: turn scripts into workflows

Start with What is a workflow? or Your first workflow. This section grows a script like the one above into a workflow that the workflow engine executes and tracks for you: structuring a process so it can resume from a safe point instead of starting over, pausing it where an operator confirms or fills in a form, and deploying the same Python file you ran on your machine to the platform. Every execution becomes a run you can follow live and look up later, with its inputs, logs, and results.

Observe: work with your data

Start with the Data overview. Raw files land in object storage, processed results in a queryable data warehouse, credentials in the built-in secrets store. Query results with standard database tools or build pipelines that process data as it arrives.

The remaining sections

• Get Started, this section: How it works, the use cases, why lab methods belong in code, and the setup guides behind the four steps above.
• Reference: the REST API and the changelog of every UniteLabs package.
• CDK: building your own connector for an instrument that has none yet.

Resources and help

The UniteLabs Hub lists the connectors that already exist, so check there before building one. For technical problems and for anything wrong in these docs, write to support@unitelabs.io; for access, credentials, and commercial questions, talk to your UniteLabs contact or info@unitelabs.io. Support lists what each channel covers.