goose

goose is an open-source, extensible AI agent — desktop app, CLI, and API — that runs locally on your machine, works with any LLM, and connects to 70+ MCP extensions. We rate it 88/100 — if you want a free, vendor-neutral alternative to Claude Code or Cursor and you don't mind bringing your own API key, goose is now the most credible choice on the market.

What is goose?

goose is a general-purpose AI agent originally built by the Block Open Source team and donated, on April 7, 2026, to the Agentic AI Foundation (AAIF) at the Linux Foundation. It was first announced as “codename goose” on January 28, 2025 and has since grown into a full agent platform with a native desktop app for macOS, Linux, and Windows, a complete CLI, and an HTTP API. The repository now lives at github.com/aaif-goose/goose, has crossed 43,700 stars with 400+ contributors, and the latest stable release is v1.33.1 shipped on April 29, 2026.

The specific problem goose solves is that the most capable AI coding agents in 2026 — Claude Code, Cursor, Windsurf, Codex CLI — are either closed-source, locked to a single model vendor, or both. goose is built in Rust under the Apache 2.0 license, runs every action on your own machine, and lets you plug in 15+ LLM providers (Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure OpenAI, Amazon Bedrock, Groq, DeepSeek, and more) or use your existing Claude, ChatGPT, or Gemini subscription via the Agent Client Protocol. It is one of the earliest and deepest implementers of MCP — the connector standard now adopted by Anthropic, OpenAI, and Microsoft — with 70+ documented extensions for GitHub, Google Drive, browsers, databases, and more.

Key Features of goose

• Three surfaces, one agent: A native Tauri-based desktop app for macOS, Linux, and Windows; a full-featured CLI for terminal workflows; and an HTTP API to embed goose anywhere. All three share the same Rust core, so a recipe written for the CLI runs unchanged in the desktop app.
• Bring your own LLM — 15+ providers: Configure Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure OpenAI, Amazon Bedrock, Groq, DeepSeek, Cerebras, GitHub Copilot, Databricks, OpenAI-compatible endpoints, and more. Or skip API keys entirely and connect Claude Code, Codex, or Gemini CLI as ACP providers using your existing subscription.
• 70+ MCP extensions: Native connectors for GitHub, Google Drive, Slack, Jira, Postgres, browsers, the file system, the Block square API, and dozens more. The Extensions Manager lets you toggle them per-session and the Skills Marketplace adds reusable workflows on top.
• Recipes — portable YAML workflows: Capture an end-to-end agent run as a single YAML config (instructions + extensions + parameters + subrecipes), commit it to git, and run it in CI. Recipes are how teams turn one-off prompt sessions into reproducible automation.
• Subagents and parallel tasks: Spawn independent subagents to handle code review, file processing, or research in parallel without polluting the main conversation. The 1.30 release (March 2026) added structured outputs and shared memory between subagents.
• MCP Apps: Extensions can render interactive UIs — buttons, forms, charts — directly inside goose Desktop. This makes goose one of the only open agents that supports rich tool UIs without leaving the chat surface.
• Security baked in: Prompt-injection detection, per-tool permission prompts, sandbox mode for untrusted recipes, and an “adversary reviewer” subagent that double-checks risky actions before execution. All of this came out of Block’s “Operation Pale Fire” red team exercise in 2025.
• Built in Rust, ships as a single binary: Cold start is under 80 ms, idle memory is <60 MB, and the CLI installer is a 30 MB single binary — meaningfully lighter than Electron-based competitors.

What Users Say About goose

The recurring praise across Hacker News, Reddit’s r/LocalLLaMA, and the project’s Discord is that goose is the only first-class agent that runs entirely with local models via Ollama — a clear win for developers who refuse to send proprietary code to OpenAI or Anthropic. The most-upvoted HN comment on the original launch thread highlighted the MCP-first design as the differentiator, and a recent r/LocalLLaMA poll put goose ahead of OpenWebUI agents and LibreChat for “most useful local agent in 2026.” The honest criticism is also consistent: early-2025 versions had a clunky UI and brittle tool execution, and developers who tried it before the v1.0 release in November 2025 still carry that impression. The 2026 releases (1.30, 1.32, 1.33) have fixed most of that — tool calling now matches Claude Code on accuracy in independent benchmarks — but the perception lag is real. Production users include Block (Square, Cash App), Hugging Face, parts of Mozilla, and dozens of mid-sized engineering teams running it as a CI agent on self-hosted runners.

goose Pricing

goose itself is 100% free under the Apache 2.0 license. There is no paid tier, no telemetry, and no signup required. Your only cost is the underlying LLM — and even that can be zero if you run Ollama with a local model.

Plan	Price	Key Limits
Open Source (everything)	$0	Apache-2.0, unlimited use, no telemetry
Local LLM via Ollama	$0 of API spend	Llama 3.3, Qwen 3, DeepSeek — runs on your laptop
Hosted LLM (typical light use)	~$5–$20/month	Claude Sonnet 4.6 or GPT-4.1 — pay your provider directly
Use existing subscription	$0 extra	Connect Claude Pro / ChatGPT Plus / Gemini Advanced via ACP

Who Should Use goose?

Best for: Engineers who want a Claude Code or Cursor-style experience without locking into a single vendor; teams with strict data-residency rules that need a local agent; anyone running Ollama who wants real tool use, not just chat. Also a strong fit for OSS contributors who want to extend the agent itself.

Not ideal for: Non-technical users who want one-click magic — goose still rewards setup and config. Also not the best pick if you only ever use one model and want the absolute most polished UI — Cursor and Claude Code remain a step ahead on visual polish, even in 2026.

Pros and Cons

Pros:

• Genuinely free and Apache-2.0 — not a sales-funnel open source project
• Works with every major LLM provider, plus Ollama for fully local runs
• Deepest open-source MCP integration in the ecosystem (70+ extensions)
• Native Rust desktop app is fast and lightweight (<60 MB idle)
• Vendor-neutral governance under the Linux Foundation
• Recipes make agent runs reproducible and CI-friendly

Cons:

• Initial provider/extension setup is more involved than Cursor or Claude Code
• UI polish, especially the Extensions Manager, still trails commercial competitors
• Local-model performance lags Claude/GPT noticeably on complex multi-step tasks
• Documentation is improving but still scattered across goose-docs.ai, the GitHub wiki, and AAIF blog posts

Alternatives to goose

The natural commercial alternatives are Claude Code (locked to Anthropic, $20+/month) and Cursor (closed-source IDE, $20/month). On the open-source side, the closest competitors are Aider (terminal-only, smaller scope, no MCP), Continue (IDE-embedded, narrower than goose), and Cline (VS Code extension, single-surface). For pure local-model setups, OpenWebUI ships agent features but lacks goose’s depth of MCP support.

Verdict: Is goose Worth It?

Yes — goose is the open-source AI agent we recommend in 2026 if you want desktop-grade ergonomics without paying a per-seat subscription or shipping your code to a single model vendor. It earns 88/100 for being genuinely free, vendor-neutral, governed by the Linux Foundation, and now technically competitive with Claude Code on tool use. We knock points only for the still-rough first-time setup and the gap to Cursor on UI polish. If you write code daily and have an Anthropic, OpenAI, or Ollama setup already, install goose this afternoon — the cost is zero and the upside is real.

Frequently Asked Questions

Is goose free?

Yes. goose is 100% free and open source under the Apache 2.0 license. There is no paid tier. You only pay your LLM provider (Anthropic, OpenAI, etc.) for API usage — or nothing at all if you run a local model via Ollama.

What platforms does goose support?

goose ships native desktop apps for macOS, Linux, and Windows, plus a full CLI installable via a single shell command (curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash). There is also an HTTP API for embedding goose into other tools.

How does goose compare to Claude Code?

Claude Code is closed-source, Anthropic-only, and starts at about $20/month. goose is open source under Apache 2.0, works with 15+ LLM providers (including Anthropic), and is free. Tool-use accuracy is now broadly comparable, but Claude Code still wins on UI polish and out-of-the-box integration with your Anthropic subscription.

Is goose actually open source?

Yes — full source on GitHub under Apache 2.0. The project was donated by Block to the Linux Foundation’s Agentic AI Foundation in April 2026, ensuring vendor-neutral governance.

Does goose work with local models?

Yes. goose has first-class support for Ollama, LM Studio, and any OpenAI-compatible endpoint. Combined with Llama 3.3 or Qwen 3, you can run a fully offline agent with zero external API spend.

What is MCP and why does goose use it?

The Model Context Protocol is the open standard (originally from Anthropic) for connecting AI agents to tools and data sources. goose was one of the earliest adopters and now ships 70+ documented MCP extensions — from GitHub and Google Drive to Postgres and browsers.