MultiOn

Interprets natural language instructions (e.g., 'book a flight from NYC to LA for next Friday') and autonomously executes multi-step web interactions by controlling a browser instance. Uses vision-language models to understand page layouts, identify interactive elements, and determine appropriate actions (clicks, form fills, navigation) without requiring explicit step-by-step programming or DOM selectors. Maintains context across page transitions to handle workflows spanning multiple websites and form submissions.

Analyzes rendered web page screenshots using vision-language models to identify interactive elements (buttons, forms, links, dropdowns), understand page structure and content hierarchy, and extract semantic meaning from visual layout. Generates internal representations of page state that enable the agent to reason about available actions and determine which elements to interact with to accomplish a goal, without requiring HTML parsing or DOM access.

Chains together multiple web interactions across different pages and websites while maintaining execution context (user preferences, extracted data, previous decisions). Decomposes high-level natural language goals into sequences of lower-level actions, tracks state across page transitions, and adapts subsequent actions based on results from previous steps. Implements backtracking or alternative paths when actions fail or return unexpected results.

Automatically populates web forms with structured data by understanding form field types (text inputs, dropdowns, date pickers, checkboxes) through visual analysis and filling them with appropriate values. Handles form validation, error messages, and conditional fields that appear based on previous entries. Supports mapping between natural language descriptions of data and form field semantics (e.g., understanding that 'departure date' maps to a date picker field).

Converts high-level natural language instructions into concrete browser actions (click coordinates, keyboard input, scroll commands, navigation) by reasoning about page state and user intent. Uses language models to interpret ambiguous instructions (e.g., 'click the blue button' when multiple blue buttons exist) by considering context and semantic meaning. Handles implicit actions like 'submit the form' by identifying the appropriate submit button.

Extracts structured data from multiple websites and transforms it into a unified format for comparison or further processing. Uses vision-language models to identify and extract relevant information from pages (prices, dates, descriptions, ratings), then normalizes and structures the data according to a schema. Handles variation in how different websites present similar information (e.g., different date formats, currency symbols).

Manages browser sessions and handles authentication flows (login, password entry, session cookies) to maintain access to protected websites throughout automation workflows. Stores and reuses session tokens to avoid repeated authentication. Handles common authentication patterns (username/password, email verification, OAuth redirects) by analyzing page state and responding to authentication prompts.

Detects when actions fail or produce unexpected results by analyzing page state and comparing against expected outcomes. Implements recovery strategies such as retrying failed actions, trying alternative UI paths, or requesting user clarification. Uses vision-language models to understand error messages and determine appropriate recovery actions (e.g., filling missing required fields, handling rate limiting).