Params are Airflow's concept of providing runtime configuration to tasks when a dag gets triggered manually. Params are configured while defining the dag & tasks, that can be altered while doing a manual trigger. The ability to update params while triggering a DAG depends on the flag core.dag_run_conf_overrides_params, so if that flag is False, params would behave like constants.

To use them, one can use the Param class for complex trigger-time validations or simply use primitive types, which won't be doing any such validations.

from airflow import DAG
from airflow.models.param import Param

with DAG(
  params: {
    'int_param': Param(10, type='integer', minimum=0, maximum=20),          # a int param with default value
    'str_param': Param(type='string', minLength=2, maxLength=4),            # a mandatory str param
    'dummy_param': Param(type=['null', 'number', 'string'])                 # a param which can be None as well
    'old_param': 'old_way_of_passing',                                      # i.e. no data or type validations
    'simple_param': Param('im_just_like_old_param'),                        # i.e. no data or type validations
    'email_param': Param(

Param make use of json-schema to define the properties and doing the validation, so one can use the full json-schema specifications mentioned at to define the construct of a Param objects.

Also, it worthwhile to note that if you have any DAG which uses a mandatory param value, i.e. a Param object with no default value or null as an allowed type, that DAG schedule has to be None. However, if such Param has been defined at task level, Airflow has no way to restrict that & the task would be failing at the execution time.


As of now, for security reasons, one can not use Param objects derived out of custom classes. We are planning to have a registration system for custom Param classes, just like we've for Operator ExtraLinks.

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