Goose overview: local models, MCP and Telegram control

This Russian-language video introduces Goose and demonstrates its desktop interface and remote tasks. Goose is presented as an open source AI agent that runs on your computer and can execute commands, edit files and use external tools. The walkthrough covers its desktop interface, command-line utility and API service, with examples aimed at development and task automation.

The speaker explains how to connect model providers through API keys or an OpenAI-compatible endpoint. Local runners such as Ollama provide a way to run models locally; cloud providers are also options. Running the agent on your own machine does not by itself mean every model request stays there. That depends on the provider you configure.

MCP extensions supply tools, resources and reusable prompts. The walkthrough also describes MCP Apps, which can display tables, buttons and other interfaces inside the client. Subagents divide work across tasks, while recipes and GitHub Actions support workflows such as checking dependencies, running tests and preparing pull requests.

A Telegram example shows remote task assignment while the computer remains powered on. The speaker discusses manual, smart and autonomous modes, extension access controls and additional action review. These are descriptions of the controls, rather than proof that every automated action is safe. The comparison with Cursor focuses on model choice and tool access, without a measured performance benchmark.