Open Screen

Captures both the raw DOM structure and a rendered screenshot of the page. By feeding these snapshots into multimodal LLMs, the agent gains spatial awareness of UI elements, allowing it to interact with buttons and inputs based on their visual position rather than fragile CSS selectors that break during site updates.

Translates high-level user intent into precise browser actions like clicks, scrolls, and text input. Instead of writing complex automation scripts, developers define goals in plain English, and the system uses the LLM to reason about the necessary steps to achieve the desired outcome on the target webpage.

Automatically manages asynchronous page loads and dynamic content updates. The system continuously monitors the DOM for changes, ensuring the agent waits for elements to render before attempting interaction. This significantly reduces 'element not found' errors common in traditional automation tools when dealing with heavy JavaScript frameworks like React or Vue.

Built on top of high-performance headless browser protocols, it ensures minimal resource overhead. By running in a headless state, it maintains a small memory footprint, allowing developers to scale multiple concurrent agent instances on standard cloud infrastructure without the need for a full GUI environment.

Implements a recursive loop where the agent evaluates the result of every action. If an action fails or leads to an unexpected state, the system provides the error context back to the LLM, allowing it to self-correct and attempt an alternative path, which is critical for robust, autonomous web navigation.