Goose

aideveloper toolsautomation

Open source local AI agent with desktop, CLI, API, MCP extensions, and support for cloud or local model provider workflows

#ai-agent#local-ai#mcp#cli#desktop-app#automation#developer-tools#llm
Alternative to Claude CodeCursorChatGPT

Quick Start

curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash

Overview

Goose is an open source AI agent that runs on your own machine through a desktop app, CLI, or API. Goose can edit code, run terminal tasks, work with local files, connect to MCP extensions, and use models from OpenAI, Anthropic, Google, Ollama, OpenRouter, Azure, Bedrock, and other providers.

The self hosted angle is strong, but it is not a normal server dashboard. You run the agent locally, then choose where inference happens. That can be a paid API, a ChatGPT or Claude subscription through supported auth flows, Ollama, LM Studio, Docker Model Runner, or Goose’s built in local inference path. For privacy sensitive work, the local model route matters because prompts and project files can stay on your machine.

Goose is now part of the Agentic AI Foundation at the Linux Foundation, which gives it more institutional weight than most open agent projects. The tradeoff is risk. This is an agent with local system access, so you should run it with human approvals, limited permissions, or a container when working around private data or production systems. For technical founders and small teams, Goose is a credible way to test vendor neutral agent workflows without committing to one model provider.

Goose: Pros & Cons

Pros (The Wins)Cons (The Friction)
Cost & Value:
Free open source app
Bring your own model.
Cost & Value:
Cloud models still cost
Local models need hardware.
Model Choice:
Works with many providers
and local model runners.
Setup:
Provider config and keys
take some care.
Workflow:
Desktop app, CLI,
API, and MCP extensions.
Safety:
Agent actions need
human approval boundaries.
Project Health:
Apache 2.0 project
under AAIF governance.
Maturity:
Fast moving agent stack
with rough edges.

Use Cases

Specific ways to use Goose for your workflow.

01
Run a local AI agent for coding, research, writing, data analysis, and automation tasks
02
Connect an agent to MCP extensions for databases, browsers, file systems, and developer tools
03
Use cloud models, subscriptions, or local model runners from one desktop or CLI workflow
04
Test agent workflows with Docker or container boundaries before giving access to sensitive systems

Deployment Strategy

Recommended ways to host Goose in your own environment.

desktop
cli
docker
self-hosted