The outcome

Get into the official goose experience with the right account and a setup you understand.

goose is a open-source general-purpose AI agent for desktop, terminal, and API workflows. Running local coding and knowledge-work agents with selectable model providers, extensible MCP tools, reusable recipes, and configurable security controls. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Start at https://block.github.io/goose/ and confirm the domain before entering account or payment information.

  2. 02

    For desktop use, follow the official download link. Current availability: goose is available as a desktop app, CLI, and API for macOS, Linux, and Windows, with multiple model providers, MCP extensions, skills, recipes, and subagents..

  3. 03

    A supported local system, the official app or CLI, a configured model provider, and explicit approval for every extension, credential, directory, and external service used.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

Working standard

What good use looks like.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.

A local agent can still expose data or damage files through model requests, shell access, extensions, browsers, and remote APIs. Use sandbox and permission controls, distrust instructions embedded in files or web pages, install extensions and skills only from reviewed sources, scope credentials and directories narrowly, pin reusable recipes, require approval for side effects, and verify all outputs and changes independently.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.