Goose is a leading agent runtime in the 2026 AI agent engineering landscape (50.6k★).
When to use this skill
- Local, extensible AI agent as desktop app, CLI, and API
- MCP-native extension system — any MCP server becomes a goose extension
- Recipes: shareable, parameterized agent task definitions
- Headless automation via goose run for CI and scripts
- Provider-agnostic model configuration incl. local models
- Sessions with resume; .goosehints for project context
- Ecosystem overlap: Complements mcp-server-design (goose consumes MCP servers); no runtime overlap in catalog
Installation
curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash
Usage
curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash
goose configure # choose provider + extensions
goose session # interactive
goose run -t "update deps and run tests" # headless
Instructions
Step 1: Initialize the environment
Verify you have Rust installed and set up properly. Use the correct package manager (pip, npm, pnpm, uv) as specified in the installation section.
Step 2: Establish the agent flow
Architecture pattern: Rust core agent loop; capabilities added exclusively through MCP extensions; desktop app and CLI share the same engine; recipes parameterize tasks
Step 3: Implement tool integrations and policies
Integrate necessary tools, environment keys, and safety gates according to your architectural needs. Position in the ecosystem: Leading vendor-neutral local agent under Linux Foundation governance; alternatives: OpenClaw (personal assistant focus), Claude Code/opencode (coding products); goose is the neutral MCP-native workhorse
Examples
Example 1: Minimal Agent Run
curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash
goose configure # choose provider + extensions
goose session # interactive
goose run -t "update deps and run tests" # headless
Best practices
- Follow the primary installation commands for Goose to ensure dependencies are resolved correctly.
- Configure model providers and API keys securely before starting the runtime.
- Use appropriate sandboxing or permission isolation if executing AI-generated code.