Goose is a free, open-source AI coding agent that delivers much of the autonomous capability of Claude Code while running entirely on your own machine, with no subscription, no cloud dependency, and no rate limits. Built by Block, the fintech company formerly known as Square, it has become a popular escape hatch for developers frustrated by the cost and caps of paid AI coding tools.

The pitch is straightforward. Claude Code, Anthropic's terminal-based agent, runs from $20 to $200 per month depending on tier, and recent usage limits have drawn pushback from the very developers it targets. Goose sidesteps that model by keeping your data and workflow on local hardware, making it a compelling entry in the open-source AI software space.

Why developers are frustrated with paid plans

Anthropic's Pro plan, around $17 to $20 a month, caps users at roughly 10 to 40 prompts every five hours. The Max plans at $100 and $200 add headroom but introduced weekly rate limits that some heavy users say they burn through within 30 minutes of intensive work. Much of the confusion comes from "hours" that actually represent token-based budgets, which swing with codebase size and conversation length. Anthropic has said the limits affect fewer than five percent of users, mainly those running the tool continuously in the background.

How Goose works offline

Goose is model-agnostic by design, which is its biggest structural advantage. You can:

  • Connect it to Claude, OpenAI's GPT-5, or Google's Gemini through an API key
  • Route requests through services like Groq or OpenRouter for speed or model variety
  • Run it fully locally with Ollama and open-source models, so nothing leaves your machine

That local option is the headline: no fees, no caps, and no code shipped to a third-party server. The project has grown fast, passing 26,100 stars on GitHub with hundreds of contributors and dozens of releases. Like other agentic tools, Goose relies on tool calling to take real actions, creating files, running test suites, and hitting external APIs through the Model Context Protocol.

Setting up a free local model

A completely free configuration uses three pieces: Goose, Ollama, and a compatible model. The general flow looks like this:

  • Install Ollama from ollama.com and pull a coding-capable model such as Qwen 2.5
  • Install Goose as a desktop app or command-line tool for macOS, Windows, or Linux
  • In Goose, select Ollama as the provider and confirm the API host points to the default local port

The trade-offs to weigh

Goose is not a perfect drop-in for a paid agent. Block's documentation suggests 32GB of RAM as a comfortable baseline, though smaller models will run on 16GB. The real compromises come down to model quality, context window, and speed:

  • Anthropic's flagship model is still arguably the strongest for complex software engineering tasks
  • Cloud APIs offer far larger context windows than most default local models
  • Local inference on a consumer laptop is typically slower than dedicated server hardware

Even so, as open-source models from Meta, Alibaba, and Google narrow the gap, the premium that justifies a $200-a-month tool faces mounting pressure. For developers who prioritize cost, privacy, and offline access, Goose is a genuine alternative they fully own. For more on developer tooling, see our latest tech news.

Frequently asked questions

Is Goose really free to use?

Yes, Goose itself is open source and free. Running it locally with Ollama and an open model costs nothing beyond your own hardware. You only pay if you choose to connect a paid cloud API like Claude or GPT-5.

Can Goose match Claude Code's quality?

For many everyday coding tasks, a strong local model gets close. For the most complex engineering work, Anthropic's flagship model still holds an edge, and cloud APIs offer larger context windows than typical local setups.

What hardware do I need to run Goose locally?

Block recommends about 32GB of RAM for a smooth experience, though smaller models run on 16GB. Faster CPUs or GPUs improve response speed compared with lightweight laptops.