Pydoll

Pydoll is revolutionizing browser automation by eliminating the need for webdrivers completely! Unlike other solutions that rely on external dependencies, Pydoll connects directly to browsers using their DevTools Protocol, providing a seamless and reliable automation experience with native asynchronous performance.

Open-Source RPA Software - Ui.Vision Web and Desktop Automation Tutorial - YouTube

Ui.Vision Open-Source RPA Software with Computer Vision, OCR, Anthropic Computer Use/LLM. Selenium IDE import/export.

SikuliX

Visual Automation and Testing

A Survey on (M)LLM-Based GUI Agents

Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM-based GUI Agents, systematically analyzing their architectural foundations, technical components, and evaluation methodologies. We identify and analyze four fundamental components that constitute modern GUI Agents: (1) perception systems that integrate text-based parsing with multimodal understanding for comprehensive interface comprehension; (2) exploration mechanisms that construct and maintain knowledge bases through internal modeling, historical experience, and external information retrieval; (3) planning frameworks that leverage advanced reasoning methodologies for task decomposition and execution; and (4) interaction systems that manage action generation with robust safety controls. Through rigorous analysis of these components, we reveal how recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms. We critically examine current evaluation frameworks, highlighting methodological limitations in existing benchmarks while proposing directions for standardization. This survey also identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control, while outlining promising research directions for enhancing GUI Agents’ capabilities. Our systematic review provides researchers and practitioners with a thorough understanding of the field’s current state and offers insights into future developments in intelligent interface automation.

Understanding System Abstractions for LLM Integration | Joche Ojeda

GitHub - chocolatey-community/chocolatey-package-recipes: Chocolatey Ganache - Chocolatey repository full of package recipes and patterns

Chocolatey Ganache - Chocolatey repository full of package recipes and patterns - chocolatey-community/chocolatey-package-recipes

GitHub - chocolatey-community/chocolatey-package-recipes: Chocolatey Ganache - Chocolatey repository full of package recipes and patterns

Chocolatey Ganache - Chocolatey repository full of package recipes and patterns - chocolatey-community/chocolatey-package-recipes

GitHub - chocolatey-community/chocolatey-package-recipes: Chocolatey Ganache - Chocolatey repository full of package recipes and patterns

Chocolatey Ganache - Chocolatey repository full of package recipes and patterns - chocolatey-community/chocolatey-package-recipes

GitHub - chocolatey-community/chocolatey-package-recipes: Chocolatey Ganache - Chocolatey repository full of package recipes and patterns

Chocolatey Ganache - Chocolatey repository full of package recipes and patterns - chocolatey-community/chocolatey-package-recipes