> ## Documentation Index
> Fetch the complete documentation index at: https://docs.narada.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# agentic_mouse_action

> Perform mouse actions at recorded coordinates with Operator agent fallback

The `agentic_mouse_action` method performs mouse actions at specific screen coordinates captured during a recording. If the action fails at those coordinates (e.g., due to page layout changes), it falls back to the Operator agent using a natural language query.

<Note>
  This method is primarily used by agents generated through [Imitation Learning](/documentation/imitation-learning), where click coordinates are captured during recording. For most manual automations, prefer [`agentic_selector()`](/api-reference/agentic-selector) which targets elements by CSS selectors.
</Note>

## Method Signature

```python theme={null}
async def agentic_mouse_action(
    self,
    *,
    action: AgenticMouseAction,
    recorded_click: RecordedClick,
    fallback_operator_query: str,
    resize_window: bool = True,
    timeout: int | None = 60,
) -> None
```

## Parameters

<ParamField query="action" type="AgenticMouseAction" required>
  The mouse action to perform. Supported actions:

  ```python theme={null}
  {"type": "click"}                                          # Left click
  {"type": "right_click"}                                    # Right click
  {"type": "double_click"}                                   # Double click
  {"type": "fill", "text": "hello", "press_enter": False}    # Type text at the location
  {"type": "scroll", "horizontal": 0, "vertical": 300}      # Scroll by pixel offset
  ```
</ParamField>

<ParamField query="recorded_click" type="RecordedClick" required>
  The coordinates and viewport dimensions from when the action was originally recorded.

  ```python theme={null}
  {
      "x": 450,          # X coordinate in pixels
      "y": 320,          # Y coordinate in pixels
      "viewport": {
          "width": 1920,  # Viewport width when recorded
          "height": 1080  # Viewport height when recorded
      }
  }
  ```
</ParamField>

<ParamField query="fallback_operator_query" type="str" required>
  Natural language instruction for the Operator agent to use if the coordinate-based action fails.

  ```python theme={null}
  fallback_operator_query="click the Submit button at the bottom of the form"
  ```
</ParamField>

<ParamField query="resize_window" type="bool" default="True">
  Whether to resize the browser window to match the recorded viewport dimensions before performing the action. This improves accuracy when the page layout is responsive.
</ParamField>

<ParamField query="timeout" type="int | None" default="60">
  Maximum time in seconds to wait for the operation to complete.
</ParamField>

## Return Value

This method returns `None` and completes when the action has been performed successfully.

## Example

```python theme={null}
import asyncio

from narada import Agent, BrowserEnvironment

async def main():
    env = BrowserEnvironment()
    agent = Agent(environment=env)

    try:
        await agent.go_to_url(url="https://www.google.com")

        # Click at recorded coordinates, with Operator fallback
        await agent.agentic_mouse_action(
            action={"type": "click"},
            recorded_click={
                "x": 600,
                "y": 450,
                "viewport": {"width": 1440, "height": 900}
            },
            fallback_operator_query='click the "Google Search" button'
        )

        # Type into a field at recorded coordinates
        await agent.agentic_mouse_action(
            action={"type": "fill", "text": "Narada AI", "press_enter": True},
            recorded_click={
                "x": 600,
                "y": 350,
                "viewport": {"width": 1440, "height": 900}
            },
            fallback_operator_query='type "Narada AI" in the search box and press Enter'
        )
    finally:
        await env.close()

if __name__ == "__main__":
    asyncio.run(main())
```

## When to Use This vs agentic\_selector

|                 | `agentic_mouse_action`           | `agentic_selector`                     |
| --------------- | -------------------------------- | -------------------------------------- |
| **Targets by**  | Screen coordinates               | CSS selectors / element attributes     |
| **Best for**    | Replaying recorded actions       | Programmatic element targeting         |
| **Resilience**  | Depends on page layout stability | Depends on element attribute stability |
| **Typical use** | Generated by Imitation Learning  | Written manually or in custom agents   |

<Tip>
  Both methods fall back to the Operator agent when their primary targeting strategy fails, ensuring robust automation even when pages change.
</Tip>