Browser Use is an open-source Python library that connects large language models to real web browsers, letting AI agents perform tasks online the way a human would. Built on Playwright, it works with GPT-4, Claude, Gemini, and local models to navigate pages, click buttons, fill forms, and extract data. You describe a task in plain English, and the agent figures out the steps. It's used by developers building web scrapers, automated workflows, QA testing, and hands-free browsing assistants, with an optional managed cloud for production workloads.

• Natural Language Browser Control: Describe any web task in plain English and the agent plans and executes each step, from clicking buttons to filling multi-page forms.
• Multi-LLM Support: Works with OpenAI, Anthropic, Google, DeepSeek, Azure, and local Ollama models, so you can pick the model that fits your budget and accuracy needs.
• Built on Playwright: Runs on Chromium, Firefox, and WebKit with reliable cross-browser automation and access to modern web features.
• DOM + Visual Understanding: Parses HTML structure and can use screenshots with multimodal models to understand pages the way humans see them.
• Managed Cloud Option: Hosted stealth browsers, proxy pools, CAPTCHA handling, and parallel session management for production workloads.
• Bring Your Own Key or Proxy: Connect existing Anthropic, OpenAI, or Google API keys and proxy contracts to keep costs down.
• Open-Source Framework: Self-host the Browser Use agent under the MIT license and customize it for your own applications.

• Adapts to Website Changes: Unlike Selenium or hard-coded Playwright scripts, the agent reasons about the page and keeps working when class names or layouts change.
• Fast to Prototype: A working automation takes about 20 lines of Python and a natural language prompt, making experimentation quick.
• Model Flexibility: Swap between GPT-4, Claude, Gemini, or local models without rewriting your automation logic.
• Strong Community: Large open-source project with active GitHub discussions, Discord, and hundreds of contributors.
• Production Path Available: Start with the free self-hosted library and graduate to the managed cloud when you need scale, stealth, and higher success rates.

• LLM Costs Add Up: Token consumption grows with page complexity and task length, which can get expensive for high-volume workloads.
• Technical Setup For Self-Hosting: Running the open-source version requires developer tools, model configuration, and API keys.
• Not Ideal for Real-Time Tasks: High-frequency scraping or latency-sensitive monitoring is better served by traditional Playwright or Puppeteer scripts.

• Competitor and Price Monitoring: Point the agent at competitor sites to collect pricing, product listings, and content changes on a schedule, without rewriting scripts when their site redesigns.
• Form Filling on Legacy Portals: Automate submissions on old government or vendor portals where the UI is inconsistent and traditional selectors constantly break.
• QA Testing of Web Apps: Write test cases in natural language to verify user flows, catching regressions across releases without maintaining brittle selector-based scripts.
• Data Enrichment from Multiple Sources: Have the agent visit LinkedIn, company websites, and directories to gather lead data or fill in missing CRM fields.
• Personal Task Automation: Book appointments, fill out surveys, place recurring orders, or complete repetitive shopping tasks by handing the agent a link and instructions.
• Research and Content Aggregation: Instruct the agent to browse news sites, forums, or academic sources and return structured summaries of what it finds.