browser-use
B
Browser Use
Overview :
Browser-use is an open-source web automation library that allows large language models (LLMs) to interact with websites and perform complex web operations through a simple interface. Its major advantages include universal support for various language models, automatic detection of interactive elements, multi-tab management, XPath extraction, support for visual models, among others. It addresses several pain points in traditional web automation, such as handling dynamic content and managing long tasks. With its flexibility and ease of use, browser-use provides developers with a powerful tool for creating smarter and more automated web interaction experiences.
Target Users :
The target audience for browser-use consists of developers and automation engineers, particularly those who need to build or integrate intelligent web automation solutions. With its support for multiple language models and the ability to automate complex web interactions, it is well-suited for professionals who handle large volumes of web data and operations, as well as developers looking to enhance the efficiency of web automation tasks.
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Top Region: US(19.34%)
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Use Cases
Use browser-use to retrieve the titles, scores, and hours of the top 10 posts from 'show hn' on Hacker News, calculating the score-per-hour ratio for each post.
Search for the top 3 AI companies of 2024, identifying the hardware models each utilizes across 3 new tabs.
Find one-way flights from Zurich to San Francisco on January 12, 2025, at kayak.com.
Features
Universal LLM support - Compatible with any language model
Automatic interactive element detection - Automatically identifies interactive web elements
Multi-tab management - Seamlessly handles browser tabs
XPath extraction - Data scraping without the need for manual checks in DevTools
Visual model support - Handles visual page content
Customizable actions - Adds custom browser interactions
Dynamic content handling - Automatically manages cookies or changing content
Chain-of-thought prompts and memory - Tackles long-term tasks
Self-correction - If the LLM makes a mistake, the agent corrects its actions autonomously
How to Use
1. Create a virtual environment and install dependencies: Use pip to install browser-use.
2. Add your API key to the .env file: Copy .env.example to .env and add your API key.
3. Configure with any LLM model supported by LangChain by setting the appropriate environment variables.
4. Write code to implement automation tasks: Use the browser-use library in your Python code to accomplish specific web automation tasks.
5. Run your automation script: Execute your Python script, and browser-use will automatically perform web operations according to your instructions.
6. Check the results: browser-use will output the results of the automation tasks, which you can view in the console or specified output files.
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