Invoking functions

Once deployed as a process, a Python function runs as an Orchestrator job. You can invoke it from several surfaces.

From a Maestro Service Task

In a Maestro process, add a Service Task and bind it to the deployed function:

1. Select the package and entry point.
2. Map process variables to the function's Input fields.
3. Map the function's Output fields back to process variables.

The input and output schemas exported from your Input/Output models drive the binding UI, so typed fields appear directly in the mapping panel.

From a Run Job activity in Studio

In Studio or Studio Web, add the Run Job activity and point it at the deployed function. The same exported input/output schema drives the argument mapping, so typed fields appear directly in the activity's input/output panels.

From the Orchestrator API

Start the function as a job:

POST /orchestrator_/odata/Jobs/UiPath.Server.Configuration.OData.StartJobs

Pass the package (release) and the input arguments in the request body. The job runs the function and returns its output through the standard job result.

From a job trigger

Because a deployed function is an Orchestrator process, it can be started by any job trigger — scheduled, queue-based, or event/webhook-based.

Observability

Every invocation runs as a job and is logged in Orchestrator job history with caller identity and timestamps. When invoked from Maestro, the run also emits trace spans — see Tracing and observability.

Next steps

• About Functions — review where functions fit in the platform.