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goose
goose is an AI agent available as a desktop app, CLI, and API. It uses a configured model and connected tools to work on code, files, research, and other workflows.
The project belongs to the Agentic AI Foundation under the Linux Foundation. Recipes package a task's prompts, extensions, and settings for reuse. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
An agent that uses tools
goose connects model responses to local or external tools, including MCP extensions.
Why it stands out
Reusable workflow recipes
Recipes keep instructions and settings together, with parameters for inputs that change between runs.
Availability
Desktop, CLI, and API
Official documentation covers installation, provider configuration, extensions, and recipe use.
Why it matters
What makes it useful
For a recurring code-review task, a Recipe can keep the review instructions and tool setup while accepting a new language or focus as parameters. The documentation includes that example, showing how to reuse the setup without rewriting the task each time.
What to know
Where it fits
goose sits between your chosen model and the tools doing the work. Extensions determine what it can reach; Recipes describe the workflow, and subrecipes can carry out parts of a larger task.
Notable points
What stands out
A Recipe can define a JSON response schema for its final output. That gives a downstream script named fields to parse; matching the schema does not establish that the generated answer is factually correct.
Before using
What to review
Configure a model provider and inspect the extensions the workflow needs.
The project documents risks from running code and following instructions in untrusted content; connected tools can act on your computer.
The official goose review advisory affects CLI versions below 1.44.0 and names 1.44.0 as the fix. Check the installed version if using that command.
Reader fit
Who may find it relevant
People who repeat tasks that need both model responses and tool actions.
Developers connecting agent output to scripts through structured recipe responses.
Users prepared to manage model settings and tool access.
Editorial note
Why LifeHubber lists it
A Recipe's file parameter reads the file and substitutes its contents, not just its path, into the prompt. Selecting a source file therefore changes what the model receives. This is a useful distinction when adapting a recipe for code review or another task involving local files.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Set the boundaries around a tool-using workflow.
A Recipe describes the task, but its connected tools determine what goose can reach. Continue with practical access, approval, stopping, and recovery checks before a workflow acts on important files or accounts.
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