Goose is a free, open-source AI agent that runs on your own machine and handles coding, research, writing, automation, and data-analysis tasks, originally built by Block and now governed by the Agentic AI Foundation (AAIF) at the Linux Foundation.
Who builds it
Goose started as a project inside Block (the fintech company behind Square and Cash App) and has since moved under the Linux Foundation's Agentic AI Foundation, with more than 500 contributors maintaining it as a community-driven, vendor-neutral project rather than a single company's closed product.
Core features
- Runs locally: available as a native desktop app, a command-line tool, and an API, so agent execution happens on your machine rather than solely in a vendor's cloud.
- Bring-your-own model: works with 15+ LLM providers including Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, and AWS Bedrock, so you can reuse an existing Claude, ChatGPT, or Gemini subscription instead of paying twice.
- MCP extensions: connects to 70+ Model Context Protocol extensions covering databases, APIs, browsers, GitHub, and Google Drive, and can render interactive UIs from those extensions via "MCP Apps."
- Recipes: shareable, YAML-based workflow definitions that let a team package a repeatable agent task and hand it to someone else.
- Subagents: Goose can spin off independent subagents to work on parts of a task in parallel.
- Security features: prompt-injection detection, granular tool permissions, a sandbox mode, and an "adversary reviewer" designed to catch risky agent actions before they run.
Pricing
Goose itself is fully open source and free to use, licensed under Apache 2.0. There is no Goose subscription or seat fee — the only ongoing cost is whatever you pay the LLM provider(s) you connect it to (an API key, or an existing paid Claude/ChatGPT/Gemini plan), which is entirely separate from Goose's own pricing.
Who it's for
Developers and teams who want a vendor-neutral, locally-run agent framework instead of a closed SaaS coding assistant — especially those who already have an LLM provider relationship and want to plug it into a flexible, MCP-extensible agent rather than being locked into one vendor's chat UI. Its recipe and subagent system also suits teams that want to standardize and share repeatable automation workflows, while the sandboxing and permission controls make it usable in security-conscious environments.