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Human-in-the-loop steps let a Python agent pause at a controlled point, show a small card in the Narada browser UI, and continue after a person responds. Use them when an automation needs an explicit approval decision or values that should come from a human during the run.

User Approval

Ask the user to approve or reject before the workflow continues. The step returns True for the approve button and False for the reject button.

Prompt for User Input

Ask the user to fill one or more workflow variables. The step returns a dictionary keyed by variable name.

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When to Use These Steps

Use user_approval for checkpoints where the automation already has enough context, but should not continue without explicit consent. Examples include sending an email, submitting a form, approving a purchase, or making a destructive update. Use prompt_for_user_input when the automation needs structured values before it can continue. Examples include asking for a date range, choosing an enum option, collecting a number, or asking for a small table of rows.
These steps require a Narada browser context that can show the human interaction UI.

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User Approval

Call window.user_approval() when the workflow needs a yes/no decision before continuing. You provide the prompt text and the labels for the approve and reject buttons. The reject button is a normal response and returns False. A cancellation or aborted interaction raises UserAbortedError, so handle that separately from an explicit rejection. See the user_approval API reference for the full signature and example.

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Prompt for User Input

Call window.prompt_for_user_input() when the workflow needs structured values from the user. You provide a list of variable definitions, and the method returns a dictionary containing the values the user submitted. Supported variable types are string, number, boolean, enum, dataTable, object, and array. Optional variables may be omitted from the returned dictionary if the user leaves them blank. For dataTable values, Narada serializes the table as: 
{
  "columnNames": ["Name", "Email"],
  "rows": [
    ["Ada Lovelace", "ada@example.com"],
    ["Grace Hopper", "grace@example.com"]
  ]
}
See the prompt_for_user_input API reference for the full signature, variable schema, and example.

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Best Practices

1

Use stable step IDs

Set step_id to a stable, descriptive value. This makes traces and debugging easier when the same agent has multiple human interaction steps.
2

Keep approval prompts specific

Approval text should describe the exact action that will happen next. Avoid vague prompts like “Continue?” when the next step sends data, submits a form, or changes an external system.
3

Mark required inputs carefully

Use required=True only when the workflow cannot continue without the value. Optional values can be omitted from the returned dictionary.
4

Handle cancellation separately

Treat cancellation as control flow. Catch UserAbortedError and exit or branch cleanly instead of reporting it as an unexpected failure.

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API Reference