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Web Agent

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Basic Information

Web-Agent is an intelligent browsing companion and automation tool designed to perform and simplify complex web tasks by combining large language models with the Playwright browser automation framework. The project enables an agent to navigate websites, interact with dynamic content, perform targeted searches, download files, and adapt to changing pages based on user queries. It provides a Python-based API and a simple example showing how to instantiate a ChatGemini LLM-backed agent, configure a GOOGLE_API_KEY in an environment file, and invoke the agent from a script or app entry point. The repository includes installation and setup instructions, Playwright setup guidance, and demo prompts that illustrate tasks such as price lookup on ecommerce sites, posting to social platforms, playing media on video sites, and visiting GitHub pages. The project is distributed under the MIT License and accepts contributions.

Links

Stars
106
App URL
https://github.com/CursorTouch/Web-Agent
Github Repository
https://github.com/CursorTouch/Web-Agent/blob/main/README.md

Categorization

App Details

Features
Web-Agent bundles several practical features for automated web interactions. It uses Playwright for reliable browser control and supports LLM integration as shown with a ChatGemini example for natural language driven workflows. The agent can navigate and interact with dynamic pages, perform smart searches, extract or download files, and adapt its actions to changing site structures. The repo lists prerequisites including Python 3.11, Langgraph, and Playwright, and provides step-by-step installation: clone, pip install requirements, and run playwright install. A dotenv-based configuration is used with a GOOGLE_API_KEY environment variable in examples. The README includes demo scenarios demonstrating ecommerce price checks, social posts, media playback, and GitHub navigation. The project includes CONTRIBUTING guidance and is MIT-licensed.
Use Cases
This repository helps users automate recurring or complex web tasks without building custom browser automation from scratch. By combining an LLM with Playwright, the agent can interpret natural language requests, perform multi-step navigation, handle dynamic content, find and download resources, and interact with social and media sites based on simple prompts. It reduces manual browsing time for activities like price comparison, content posting, media playback, and data retrieval. The included example shows how to wire up a Gemini model, configure environment variables, and run the agent from a Python script or the provided app entry point. Install and setup instructions lower the barrier to entry for developers and contributors, and demo recordings illustrate practical capabilities for testing and extension.

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Content Creation (5042)
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