Goose is an open-source, general-purpose AI agent designed to run locally on a user’s machine via desktop app, CLI, or API. It acts as an interactive “AI coworker” that can execute tasks such as coding, automation, research, and data processing by combining a language model with tool-based execution capabilities.

The system is built around a modular architecture consisting of an interface (desktop or CLI), an agent runtime, and extensible tool integrations. These tools—often exposed via the Model Context Protocol (MCP)—allow Goose to interact with external systems such as file systems, APIs, browsers, and development tools.

Goose supports multiple LLM providers, including OpenAI, Anthropic, Google, and local models via tools like Ollama, giving users flexibility in how intelligence is powered. It can also integrate subscription-based models and routing services, making it adaptable for both personal and enterprise use cases.

A key capability of Goose is extensibility. Developers can add or build MCP extensions (“skills” or tools) that expand its abilities into domains such as GitHub automation, database querying, web scraping, and productivity workflows. It also supports sub-agents, allowing parallel task execution for complex workflows.

Goose is part of the broader Agentic AI Foundation under the Linux Foundation, emphasizing open governance, interoperability standards, and long-term ecosystem neutrality.

The agent is designed for both developers and general users, enabling tasks like coding assistance, research, automation pipelines, and interactive workflows where the model can execute actions rather than only generating text.

Key features include:

• Local-first AI agent (CLI, desktop app, API)
• Extensible via MCP tools and community extensions
• Supports multiple LLM providers (cloud and local)
• Sub-agent execution for parallel workflows
• Built-in tools for coding, automation, and research
• Secure execution with permission and sandbox controls
• Recipe/workflow system for reusable automation
• Open-source (Apache 2.0) and community governed
• Cross-platform support (macOS, Linux, Windows)

Common use cases include:

• Automating software development workflows
• Running AI-assisted coding and debugging tasks
• Performing research and data analysis locally
• Building internal automation tools and scripts
• Integrating AI agents into CI/CD or dev environments
• Executing multi-step browser or API workflows

Goose is positioned as a “native AI agent runtime” that turns LLMs into tool-using systems capable of executing real-world tasks directly on a user’s machine, rather than just producing text responses.