UI-TARS is an LLM GUI automation framework and multimodal action grounding system. It functions as a GUI agent orchestrator and cross-platform device controller that uses large language models to interpret graphical interfaces and execute actions across desktop and mobile operating systems. The system translates model-generated coordinates into precise screen positions to interact with visual user interface elements. It employs a multimodal approach to interpret screen layouts and decomposes complex goals into multi-step trajectories through reasoning and error correction. The project provid
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
OmniParser is a multimodal interaction engine designed to function as a desktop automation agent. It interprets visual screen information to execute complex, multi-step tasks across operating system environments by bridging visual interface perception with language models. Through a continuous cycle of observation and command execution, the system grounds high-level natural language instructions into precise, coordinate-based actions. The project distinguishes itself by utilizing vision-based parsing to interact with software interfaces without requiring access to underlying application progr
This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs for automating desktop tasks. It functions as an autonomous agent and vision-based orchestrator that interprets screen visuals to interact with user interfaces. The system employs vision language models and object detection to locate and click interface elements. It utilizes visual grounding to overlay numerical markers on UI components and uses optical character recognition to map on-screen text to precise pixel coordinates. The framework supports voice-controlled computing
UI-TARS-desktop is a cross-platform desktop application designed to automate software interface interactions. It functions as a local agent environment that interprets graphical user interfaces through multimodal visual-language model reasoning, allowing it to navigate and manipulate software by simulating human-like mouse and keyboard inputs.
The main features of bytedance/ui-tars-desktop are: Cross-Platform Visual Automation Tools, Automated Desktop Interaction Systems, Desktop Automation Agents, Local AI Execution Environments, Desktop Automation, Cross-Platform Desktop Automation Libraries, Local Data Processing, Local AI Inference.
Open-source alternatives to bytedance/ui-tars-desktop include: bytedance/ui-tars — UI-TARS is an LLM GUI automation framework and multimodal action grounding system. It functions as a GUI agent… simular-ai/agent-s — Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through… microsoft/omniparser — OmniParser is a multimodal interaction engine designed to function as a desktop automation agent. It interprets visual… othersideai/self-operating-computer — This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs… openai/openai-agents-python — This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime… dinoki-ai/osaurus — Osaurus is a local AI workflow engine and LLM agent orchestration framework designed for private execution on local…