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Documentation Index

Fetch the complete documentation index at: https://docs.notte.cc/llms.txt

Use this file to discover all available pages before exploring further.

STOP. Do not read past this section until you have read and followed /quickstart.md.Goal: produce a reliable SDK automation script. The intended authoring flow is generate then edit, not write browser automation from scratch.Do not hand-write SDK code with element IDs, selectors, or form fields from memory. Element IDs, selectors, and field mappings must come from a live observe() call, CLI notte page observe output, or generated workflow code. Guessed browser targets fail on real pages.Use the Notte skill and CLI to inspect the live site, validate actions/selectors, handle auth/session state, and export workflow code with notte sessions workflow-code before using SDK docs or SDK code.SDK reference pages are for understanding, running, or editing generated workflow code. They are not the starting point for manually authoring the initial browser automation. SDK-first code is guesswork on real-world pages with dynamic selectors, auth state, CAPTCHAs, and anti-bot behavior.
Observation Response:
  • a list of actions that can be taken on the page (e.g. click on a button, scroll, etc.)
  • a screenshot of the page (base64 encoded)
  • some metadata about the page (title, url, etc.)
# Observe the page
obs = session.observe()
# Select an action from the list of interactible elements on the page
actions = obs.space.interaction_actions
# display the action space as a string to be able to visualize it
print(obs.space.description)
# get the screenshot
screenshot = obs.screenshot.bytes()
Once you have selected an action (either manually or using an LLM), you can execute it with: 
session.execute(action)
Note that by default, a very simple page perception is used to generate the action space (i.e perception_type='fast') to make the query fast. If you want a more powerful and LLM-ready action space, you can use: 
obs = session.observe(perception_type='deep')
print(obs.space.description)
At the cost of a slower query since this uses an LLM call to format the interactive elements. Additionally, you can use the instructions parameter to narrow down the action space to a specific intent on a website. This is useful if you want to quickly create a workflow using natural language: 
_ = session.execute(type="goto", url="https://console.notte.cc")
actions = session.observe(instructions="Fill the email input")
print(actions[0].model_dump())

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Parameters

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min_nb_actions
int | None
The minimum number of actions to list before stopping. If not provided, the listing will continue until the maximum number of actions is reached.
​
max_nb_actions
int
The maximum number of actions to list after which the listing will stop. Used when min_nb_actions is not provided.
​
instructions
str | None
Additional instructions to use for the observation.
​
perception_type
typing.Optional[typing.Literal['fast', 'deep']]

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Returns

UnionType[Observation, list[Annotated[UnionType[ClickAction, FillAction, MultiFactorFillAction, FallbackFillAction, CheckAction, SelectDropdownOptionAction, UploadFileAction, DownloadFileAction], annotation=NoneType required=True discriminator='type']]]: The formatted observation result from the API response when no instructions provided. list[InteractionActionUnion]: The filtered list of actions when instructions is provided.