explaingit

aaif-goose/goose

📈 Trending45,481RustAudience · developerComplexity · 4/5ActiveLicenseSetup · moderate

TLDR

A local AI agent that autonomously executes tasks on your computer, installing software, running commands, editing files, browsing the web, by connecting to 15+ AI providers and supporting 70+ tool extensions.

Mindmap

mindmap
  root((Goose))
    What it does
      Autonomous task execution
      Multi-step workflows
      File and code editing
      Web browsing and research
    How to use it
      Desktop app macOS Linux Windows
      Command-line CLI
      Embeddable API
    AI providers
      Anthropic Claude
      OpenAI ChatGPT
      Google Gemini
      Ollama local models
      Azure AWS Bedrock
    Extensibility
      Model Context Protocol
      70+ tool extensions
      Database integrations
      Code environments
    Tech stack
      Rust core
      Cross-platform
      Open source

Things people build with this

USE CASE 1

Automate multi-step development workflows like running tests, deploying code, and managing repositories without manual intervention.

USE CASE 2

Research and analyze data by having the agent browse the web, download files, and summarize findings into documents.

USE CASE 3

Set up development environments by having the agent install dependencies, configure tools, and scaffold projects from scratch.

USE CASE 4

Automate repetitive file management and data processing tasks across your local machine or connected systems.

Tech stack

RustAnthropicOpenAIGoogleOllamaModel Context Protocol

Getting it running

Difficulty · moderate Time to first run · 30min

Requires at least one AI provider API key (Anthropic, OpenAI, Google, or local Ollama) and Rust toolchain to build.

Use freely for any purpose, including commercial use, as long as you include the original copyright notice and license text.

In plain English

Goose is an open-source, general-purpose AI agent that runs locally on your machine and can do far more than just suggest code. Unlike typical AI coding assistants that sit in your editor and offer completions, goose can autonomously install software, execute shell commands, edit files, run tests, browse the web, conduct research, and automate multi-step workflows. You give it a goal and it figures out the sequence of actions to complete it. The core concept is that goose acts as an agent, it has tools it can call, memory of what it has done, and the ability to loop through tasks until a goal is satisfied. It is not limited to programming: you can use it to write documents, analyze data, manage files, or automate any task your computer can perform. Goose connects to over 15 AI providers, including Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, and AWS Bedrock, so you can use it with API keys or your existing Claude, ChatGPT, or Gemini subscriptions. It supports the Model Context Protocol (MCP), an open standard that defines how AI agents can communicate with external tools and data sources, giving it access to over 70 extensions for databases, web browsers, code environments, and more. It also supports the Agentic Communication Protocol (ACP) for provider connections. The project is available as a native desktop application for macOS, Linux, and Windows, as a full-featured CLI for terminal workflows, and as an embeddable API for building your own tools on top of it. The core is written in Rust for performance and cross-platform portability. Goose was originally developed by Block (the company behind Square and Cash App) and has since moved to the Agentic AI Foundation under the Linux Foundation. It is licensed under Apache 2.0.

Copy-paste prompts

Prompt 1
Set up a new Python project with a virtual environment, install dependencies from requirements.txt, and run the test suite.
Prompt 2
Research the latest updates for [topic] by browsing the web, then write a summary document with sources.
Prompt 3
Audit my codebase for security issues, generate a report, and create pull request descriptions for fixes.
Prompt 4
Download and process CSV files from [source], clean the data, and generate charts showing key metrics.
Prompt 5
Deploy my application to production by running build scripts, running tests, and pushing to my deployment service.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.