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Agentic Browser - AI Agent Applications Tool

Overview

Agentic Browser is an open-source AI agent framework for automating web interactions through a natural-language interface. It coordinates specialized sub-agents — typically a Planner, a Browser, and a Critique — to break down high-level user intents into actionable browser steps, execute those steps (navigation, clicks, form filling, data extraction), and validate or refine results. The architecture emphasizes agent orchestration: the Planner decomposes tasks, the Browser agent drives a headless browser to perform actions, and the Critique agent evaluates outputs and requests retries or corrections when needed. Designed for tasks such as targeted scraping, automated form submission, e-commerce searching, and content retrieval, Agentic Browser exposes an extensible workflow so developers can add custom agents, adapt scraping logic, or integrate different LLMs. As an open-source project hosted on GitHub, the repository provides source code, examples, and configuration hooks so teams can run the system locally or extend it for production automation pipelines. For exact usage patterns, supported browsers/drivers, and runtime requirements, consult the project's README and examples on the GitHub repository.

Installation

Install via docker:

git clone https://github.com/TheAgenticAI/TheAgenticBrowser.git
cd TheAgenticBrowser
docker build -t agentic-browser .
docker run --rm -it agentic-browser

Key Features

  • Planner agent decomposes high-level tasks into ordered browser actions.
  • Browser agent drives headless Chromium for navigation, clicks, and form filling.
  • Critique agent validates outputs and requests retries or alternative strategies.
  • Natural-language interface accepts prompts for searches, scraping, and automation.
  • Modular architecture lets developers add agents, plugins, or custom workflows.

Community

The project is maintained as an open-source repository on GitHub, where source code, issues, and contribution guidance are available. Community engagement typically occurs via GitHub issues, pull requests, and example contributions; consult the repository for current activity, release notes, and contribution guidelines.

Last Refreshed: 2026-01-09

Key Information

  • Category: Agent Applications
  • Type: AI Agent Applications Tool
Visit Official Website

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