Agent Browser

• Automating web interactions — You need an AI agent to navigate pages, click buttons, fill forms, or perform multi-step workflows on real websites.
• Extracting structured data — You want to snapshot a page's accessibility tree or DOM to pull text, attributes, or element counts from dynamic, JavaScript-rendered content.
• Filling forms programmatically — You're submitting login forms, search queries, or multi-field forms as part of an automated pipeline.
• Testing web UIs — You need lightweight, CLI-driven browser testing with screenshots, PDF export, or video recording of interactions.

• Static HTML scraping — If the target page is plain HTML with no JavaScript, a lighter HTTP client or HTML parser will be faster and simpler.
• High-concurrency crawling at scale — Agent Browser is optimized for sequential agent workflows, not distributed web crawling across thousands of URLs simultaneously.

Run agent-browser snapshot -i to get a list of all interactive elements with stable @ref identifiers (e.g. @e1, @e2). Use these refs directly in subsequent click, fill, hover, or get commands — no CSS selectors or XPath required.

Supports click, double-click, focus, fill, type, key press/hold/release, hover, checkbox check/uncheck, dropdown select, drag-and-drop, file upload, and scroll — covering virtually every user interaction a browser supports.

Save authenticated browser state (cookies, storage) to a JSON file with agent-browser state save, then reload it in future sessions to skip re-authentication. Parallel isolated sessions via --session <name> allow multiple simultaneous browser contexts.

Capture full-page screenshots, save pages as PDFs, or record browser interactions as .webm video files. Recording preserves cookies and storage from the current session, making it ideal for creating reproducible demos.

Intercept, block, or mock HTTP requests with agent-browser network route. Filter and inspect tracked requests to debug API calls or simulate offline/error states during testing.

When refs aren't available, use semantic locators to find elements by ARIA role, visible text, label, or CSS selector — for example, agent-browser find role button click --name "Submit" or agent-browser find text "Sign In" click.

Un agente de IA abre una URL objetivo, toma un snapshot de la página para identificar los campos del formulario por ref, rellena cada campo con agent-browser fill, lo envía con un clic, espera a que la red esté inactiva y vuelve a tomar un snapshot para confirmar el éxito — todo sin intervención humana.

Inicia sesión una vez de forma interactiva, guarda la sesión con agent-browser state save auth.json y vuelve a cargarla en todas las ejecuciones futuras del agente. Esto evita flujos de inicio de sesión repetidos y mantiene las credenciales fuera de los scripts de automatización.

Navega a una página renderizada con JavaScript, ejecuta agent-browser snapshot para capturar el árbol de accesibilidad completo y luego usa los comandos agent-browser get text o agent-browser get attr para extraer contenido estructurado que un scraper HTTP simple pasaría por alto.

Escribe una secuencia de interacciones en el navegador, captura capturas de pantalla o una grabación de vídeo en cada paso y exporta un PDF del estado final — proporcionando a