How to Get Started with Goose: A Free Open-Source Alternative to Claude Code

In the rapidly evolving world of AI-powered coding tools, a new contender has emerged that promises to deliver the capabilities of premium services like Claude Code without the hefty price tag. Goose, an open-source agent framework developed by Block (formerly Square), combined with the Qwen3-coder model, offers developers a powerful local alternative that runs entirely on your machine.

This comprehensive guide will walk you through setting up this free AI coding stack, compare it with commercial alternatives, and help you determine if it’s the right solution for your development needs.

ZDNET’s Key Takeaways

Free AI tools Goose and Qwen3-coder may replace expensive Claude Code subscriptions
Setup is straightforward but requires a powerful local machine with sufficient RAM
Early tests show promise, though accuracy issues and multiple retries may be needed
Complete local processing ensures privacy and eliminates cloud dependency
Hardware requirements are significant – 16GB+ RAM recommended, 32GB+ ideal

The Rise of Local AI Coding Tools

Back in July 2024, Jack Dorsey, the founder of Twitter (now X), Square (now Block), and Bluesky, posted a cryptic message on X that would spark significant interest in the developer community: “goose + qwen3-coder = wow.”

Since then, both Goose and Qwen3-coder have gained traction as serious alternatives to commercial AI coding assistants. Goose serves as an open-source agent framework similar to Claude Code, while Qwen3-coder is a coding-optimized large language model comparable to Claude’s Sonnet-4.5 model.

The promise is compelling: a fully functional AI coding assistant that runs locally on your machine, doesn’t require ongoing subscription fees, and keeps all your code and data private. But does it live up to the hype?

Hardware Requirements: Can Your Machine Handle It?

Before diving into the setup process, it’s crucial to understand the hardware requirements. Unlike cloud-based solutions that leverage massive server infrastructure, local AI tools depend entirely on your machine’s capabilities.

Minimum Requirements

RAM: 16GB minimum (32GB+ recommended)
Storage: 20GB+ available space
Processor: Modern multi-core CPU (Apple Silicon M1/M2/M3/M4 preferred)
Operating System: macOS 12+, Windows 10+, or Linux

Why Hardware Matters

The Qwen3-coder model alone is 17GB in size, and during operation, it requires substantial memory for processing. When I tested this setup on my M4 Max Mac Studio with 128GB of RAM, I was able to run multiple applications simultaneously (Chrome, Fusion, Final Cut, VS Code, Xcode, Wispr Flow, and Photoshop) without noticeable performance degradation.

However, when my colleague Tiernan Ray attempted to run Ollama on his 16GB M1 Mac, he found the performance to be “brutal” and nearly unusable. This highlights a critical consideration: if you’re working with limited RAM, local AI tools may not be practical.

Setting Up Your Free AI Coding Stack

The setup process involves three main components: Ollama (the LLM server), Qwen3-coder (the model), and Goose (the agent framework). Here’s a detailed walkthrough:

Step 1: Install Ollama

Download Ollama from the official website and install it on your machine. Ollama serves as the local server that runs your AI models.

Installation Tip: Choose the app version rather than the command-line interface for easier management, especially if you’re new to local AI tools.

Step 2: Download and Configure Qwen3-coder

Once Ollama is installed, launch the application. You’ll see a chat-like interface with a default model (usually gpt-oss-20b). Click on the model name to access the model list.

Select Qwen3-coder:30b – the “30b” indicates 30 billion parameters, making this a powerful coding-optimized model. Note that this model is 17GB, so ensure you have sufficient storage space.

Important: The model won’t download until you force it to respond to a prompt. Type “test” to initiate the download process.

Step 3: Configure Network Access

To allow Goose to communicate with Ollama, you need to expose Ollama to the network:

Open Ollama’s settings from the menu bar
Enable “Expose Ollama to the network”
Set your context length (I recommend 32K for coding tasks)
Avoid signing in to maintain complete local operation

Step 4: Install Goose

Download the appropriate Goose version for your operating system. For macOS with Apple Silicon, choose the Desktop version.

Step 5: Configure Goose to Use Ollama

Launch Goose and navigate to the provider settings. Scroll down to find Ollama in the list of available providers and click “Configure.”

Select Qwen3-coder:30b as your model, then hit “Select Model” to complete the configuration.

Testing Your Setup: First Impressions

With everything configured, it’s time to test your new AI coding assistant. I used my standard test challenge – building a simple WordPress plugin – to evaluate performance.

Initial Results

The first attempt failed to produce a working plugin. The second and third attempts also failed, though they showed incremental improvements. By the fifth attempt, Goose successfully generated a working plugin.

Key Observation: Unlike traditional chatbots that generate code snippets, Goose works directly on your source files. This means that even failed attempts contribute to the learning process and eventual success.

Performance Comparison

When tested against other free chatbots with the same assignment, most competitors (except Grok and pre-Gemini 3 Gemini) succeeded on the first try. However, the agentic approach of Goose offers advantages:

Direct file manipulation: Changes are made to actual source code
Iterative improvement: Failed attempts still contribute to the final result
Local processing: No cloud dependency or privacy concerns

Cost Comparison: Free vs. Premium

The most compelling aspect of this setup is the cost – or lack thereof. Here’s how it compares to premium alternatives:

Service	Monthly Cost	Setup Complexity	Privacy
Goose + Qwen3-coder	$0	Moderate	Complete
Claude Code Max	$100	Easy	Cloud-based
OpenAI Codex Pro	$200	Easy	Cloud-based

While premium services offer easier setup and potentially better initial accuracy, the free alternative provides comparable long-term value, especially for developers who prioritize privacy and don’t mind a slightly steeper learning curve.

Limitations and Considerations

Before fully committing to this setup, consider these limitations:

Accuracy Issues

The Qwen3-coder model may require multiple attempts to achieve correct results, particularly for complex coding tasks. This contrasts with premium services that often succeed on the first try.

Hardware Dependency

Your machine’s performance directly impacts the AI’s responsiveness. Users with older or less powerful hardware may experience significant delays or may not be able to run the system at all.

Learning Curve

Setting up and configuring local AI tools requires more technical knowledge than simply signing up for a cloud service. However, once configured, the daily usage is straightforward.

Model Limitations

While Qwen3-coder is powerful, it may not match the latest capabilities of premium models like Claude 3.5 Sonnet or GPT-4o in terms of reasoning, code optimization, or handling specialized tasks.

Who Should Use This Setup?

This free AI coding stack is ideal for:

Privacy-conscious developers who don’t want their code sent to cloud services
Budget-conscious individuals who can’t justify premium subscription costs
Developers with powerful hardware (16GB+ RAM, modern processors)
Open-source enthusiasts who prefer community-driven tools
Learning environments where cost is a significant factor

It may not be suitable for:

Users with limited hardware resources
Developers requiring guaranteed first-attempt accuracy
Teams needing enterprise support and SLAs
Those preferring plug-and-play solutions

Future Outlook

As local AI technology continues to advance, we can expect improvements in model efficiency, reduced hardware requirements, and enhanced accuracy. The trend toward local processing aligns with growing privacy concerns and the desire for offline capabilities.

For now, Goose + Qwen3-coder represents a viable free alternative to premium AI coding tools, particularly for developers with sufficient hardware resources and patience for iterative development.

Next Steps

In upcoming articles, we’ll explore:

Deep dive into agent frameworks: Understanding how Goose, Ollama, and Qwen3-coder work together
Advanced configuration: Optimizing performance and accuracy
Real-world project testing: Building a full iPad app using this setup
Comparison with commercial tools: Head-to-head performance testing

Your Experience Matters

Have you tried running a coding-focused LLM locally with tools like Goose, Ollama, or Qwen? How did setup go for you, and what hardware are you running it on? If you’ve used cloud options like Claude or OpenAI Codex, how does local performance and output quality compare?

Share your experiences in the comments below – your insights could help other developers make informed decisions about their AI coding tool choices.

I tried a Claude Code rival that’s local, open source, and completely free – how it went