OT-2 Python Protocol API Version 2

The OT-2 Python Protocol API is a simple Python framework designed to make writing automated biology lab protocols easy.

We’ve designed it in a way we hope is accessible to anyone with basic Python and wetlab skills. As a bench scientist, you should be able to code your automated protocols in a way that reads like a lab notebook.

Version 2 of the API is a new way to write Python protocols. It is more reliable, simpler, and better able to be supported. Unlike version 1, it has support for new modules like the Thermocycler. While version 1 will still recieve bug fixes, new features and improvements will land in version 2. For a guide on transitioning your protocols from version 1 to version 2 of the API, see this article on migration. For a more in-depth discussion of why version 2 of the API was developed and what is different about it compared to version 1, see this article on why we wrote API V2.

Getting Started

New to Python? Check out our Using Python For Protocols page first before continuing. To get a sense of the typical structure of our scripts, take a look at our Examples page.

To simulate protocols on your laptop, check out Simulating Your Scripts. When you’re ready to run your script on a robot, download our latest desktop app.


If you encounter problems using our products please take a look at our support docs or contact our team via intercom on our website at opentrons.com.


How it Looks

The design goal of this API is to make code readable and easy to understand. For example, below is a short set of instructions to transfer from well 'A1' to well 'B1' that even a computer could understand:

This protocol is by me; it’s called Opentrons Protocol Tutorial and is used for demonstrating the OT-2 Python Protocol API. It uses version 2.0 of this API.

Begin the protocol

Add a 96 well plate, and place it in slot ‘2’ of the robot deck

Add a 300 µL tip rack, and place it in slot ‘1’ of the robot deck

Add a single-channel 300 µL pipette to the left mount, and tell it to use that tip rack

Transfer 100 µL from the plate’s ‘A1’ well to its ‘B2’ well

If we were to rewrite this with the Python Protocol API, it would look like the following:

from opentrons import protocol_api

# metadata
metadata = {
    'protocolName': 'My Protocol',
    'author': 'Name <email@address.com>',
    'description': 'Simple protocol to get started using OT2',
    'apiLevel': '2.11'

# protocol run function. the part after the colon lets your editor know
# where to look for autocomplete suggestions
def run(protocol: protocol_api.ProtocolContext):

    # labware
    plate = protocol.load_labware('corning_96_wellplate_360ul_flat', '2')
    tiprack = protocol.load_labware('opentrons_96_tiprack_300ul', '1')

    # pipettes
    left_pipette = protocol.load_instrument(
         'p300_single', 'left', tip_racks=[tiprack])

    # commands
    left_pipette.aspirate(100, plate['A1'])
    left_pipette.dispense(100, plate['B2'])

How it’s Organized

When writing protocols using the Python Protocol API, there are generally five sections:

  1. Metadata and Version Selection

  2. Run function

  3. Labware

  4. Pipettes

  5. Commands

Metadata and Version Selection

Metadata is a dictionary of data that is read by the server and returned to client applications (such as the Opentrons App). Most metadata is not needed to run a protocol, but if present can help the Opentrons App display additional data about the protocol currently being executed. These optional (but recommended) fields are ("protocolName", "author", and "description").

The required element of the metadata is "apiLevel". This must contain a string specifying the major and minor version of the Python Protocol API that your protocol is designed for. For instance, a protocol written for version 2.0 of the Python Protocol API (only launch version of the Protocol API should have in its metadata "apiLevel": "2.0".

For more information on Python Protocol API versioning, see Versioning.

The Run Function and the Protocol Context

Protocols are structured around a function called run(protocol), defined in code like this:

from opentrons import protocol_api

metadata = {'apiLevel': '2.11'}

def run(protocol: protocol_api.ProtocolContext):

This function must be named exactly run and must take exactly one mandatory argument (its name doesn’t matter, but we recommend protocol since this argument represents the protocol that the robot will execute).

The function run is the container for the code that defines your protocol.

The object protocol is the protocol context, which represents the robot and its capabilities. It is always an instance of the opentrons.protocol_api.contexts.ProtocolContext class (though you’ll never have to instantiate one yourself - it is always passed in to run()), and it is tagged as such in the example protocol to allow most editors to give you autocomplete.

The protocol context has two responsibilities:

  1. Remember, track, and check the robot’s state

  2. Expose the functions that make the robot execute actions

The protocol context plays the same role as the robot, labware, instruments, and modules objects in past versions of the API, with one important difference: it is only one object; and because it is passed in to your protocol rather than imported, it is possible for the API to be much more rigorous about separating simulation from reality.

The key point is that there is no longer any need to import opentrons at the top of every protocol, since the robot now runs the protocol, rather than the protocol running the robot. The example protocol imports the definition of the protocol context to provide editors with autocomplete sources.


The next step is defining the labware required for your protocol. You must tell the protocol context about what should be present on the deck, and where. You tell the protocol context about labware by calling the method protocol.load_labware(name, slot) and saving the result.

The name of a labware is a string that is different for each kind of labware. You can look up labware to add to your protocol on the Opentrons Labware Library.

The slot is the labelled location on the deck in which you’ve placed the labware. The available slots are numbered from 1-11.

Our example protocol above loads

plate = protocol.load_labware('corning_96_wellplate_360ul_flat', 2)
tiprack = protocol.load_labware('opentrons_96_tiprack_300ul', 1)

These labware can be referenced later in the protocol as plate and tiprack respectively. Check out the Python docs for further clarification on using variables effectively in your code.

You can find more information about handling labware in the Labware section.


After defining labware, you define the instruments required for your protocol. You tell the protocol context about which pipettes should be attached, and which slot they should be attached to, by calling the method protocol.load_instrument(model, mount, tip_racks) and saving the result.

The model of the pipette is the kind of pipette that should be attached; the mount is either "left" or "right"; and tip_racks is a list of the objects representing tip racks that this instrument should use. Specifying tip_racks is optional, but if you don’t then you’ll have to manually specify where the instrument should pick up tips from every time you try and pick up a tip.

See Pipettes for more information on creating and working with pipettes.

Our example protocol above loads a P300 Single-channel pipette ('p300_single') in the left mount ('left'), and uses the Opentrons 300 µL tiprack we loaded previously as a source of tips (tip_racks=[tiprack]).


Once the instruments and labware required for the protocol are defined, the next step is to define the commands that make up the protocol. The most common commands are aspirate(), dispense(), pick_up_tip(), and drop_tip(). These and many others are described in the Building Block Commands and Complex Commands sections, which go into more detail about the commands and how they work. These commands typically specify which wells of which labware to interact with, using the labware you defined earlier, and are methods of the instruments you created in the pipette section. For instance, in our example protocol, you use the pipette you defined to:

  1. Pick up a tip (implicitly from the tiprack you specified in slot 1 and assigned to the pipette): pipette.pick_up_tip()

  2. Aspirate 100 µL from well A1 of the 96 well plate you specified in slot 2: pipette.aspirate(100, plate['A1'])

  3. Dispense 100 µL into well A2 of the 96 well plate you specified in slot 2: pipette.dispense(100, plate['A2'])

  4. Drop the tip (implicitly into the trash at the back right of the robot’s deck): pipette.drop_tip()

Feature Requests

Have an interesting idea or improvement for our software? Create a ticket on GitHub by following these guidelines.

Developer’s guide

Do you want to contribute to our open-source API? You can find more information on how to be involved here.