open computer use
Basic Information
Open Computer Use is a repository that aims to enable AI-driven interaction with a desktop environment by combining open source large language models and the E2B Desktop Sandbox. The short repository description indicates it is intended as a foundation for allowing LLMs to perform or orchestrate tasks on a computer within a sandboxed desktop context. The README visible in the repository is currently missing or broken, so detailed usage instructions and implementation specifics are not available in the provided content. The primary audience appears to be developers and researchers interested in experimenting with or building systems that let LLMs control or automate desktop-level operations in a controlled, open-source setting.
Links
Stars
1435
Github Repository
Categorization
App Details
Features
The repository description highlights two core signals: use of open source LLMs and integration with an E2B Desktop Sandbox. From those signals it can be inferred the project focuses on connecting language models to a sandboxed desktop runtime for experimentation and automation. The visible file tree entry shows a main README reference but the file is not accessible, so explicit feature lists, example code, dependencies, or scripts are not present in the provided content. The project emphasizes an open-source approach and desktop sandboxing which suggests features like model interoperability, sandboxed execution, and developer-oriented tooling, though exact implementations are not available in the provided material.
Use Cases
For developers and researchers, this repository promises a starting point for exploring how open source LLMs can be used to interact with or control a desktop environment inside a sandbox. It could help teams prototype agent behaviors that perform local tasks, evaluate safety and containment strategies provided by the E2B Desktop Sandbox, and reduce reliance on closed-source services by using open models. Because the README and documentation are not present in the visible content, users should expect to investigate the codebase directly to understand setup, integration points, and limitations. The open-source orientation makes it suitable as a research or proof-of-concept base for agent-driven desktop automation.
-
$$$$ -
$$$$