The substitute intelligence coding revolution comes with a catch: it's expensive.
Claude Code, Anthropic's terminal-based AI agent that may write, debug, and deploy code autonomously, has captured the imagination of software developers worldwide. But its pricing — starting from $20 to $200 per thirty days depending on usage — has sparked a growing rise up among the many very programmers it goals to serve.
Now, a free alternative is gaining traction. Goose, an open-source AI agent developed by Block (the financial technology company formerly generally known as Square), offers nearly similar functionality to Claude Code but runs entirely on a user's local machine. No subscription fees. No cloud dependency. No rate limits that reset every five hours.
"Your data stays with you, period," said Parth Sareen, a software engineer who demonstrated the tool during a recent livestream. The comment captures the core appeal: Goose gives developers complete control over their AI-powered workflow, including the power to work offline — even on an airplane.
The project has exploded in popularity. Goose now boasts greater than 26,100 stars on GitHub, the code-sharing platform, with 362 contributors and 102 releases since its launch. The most recent version, 1.20.1, shipped on January 19, 2026, reflecting a development pace that rivals industrial products.
For developers frustrated by Claude Code's pricing structure and usage caps, Goose represents something increasingly rare within the AI industry: a genuinely free, no-strings-attached option for serious work.
Anthropic's recent rate limits spark a developer revolt
To know why Goose matters, it’s worthwhile to understand the Claude Code pricing controversy.
Anthropic, the San Francisco artificial intelligence company founded by former OpenAI executives, offers Claude Code as a part of its subscription tiers. The free plan provides no access in anyway. The Pro plan, at $17 per thirty days with annual billing (or $20 monthly), limits users to simply 10 to 40 prompts every five hours — a constraint that serious developers exhaust inside minutes of intensive work.
The Max plans, at $100 and $200 per thirty days, offer more headroom: 50 to 200 prompts and 200 to 800 prompts respectively, plus access to Anthropic's strongest model, Claude 4.5 Opus. But even these premium tiers include restrictions which have inflamed the developer community.
In late July, Anthropic announced recent weekly rate limits. Under the system, Pro users receive 40 to 80 hours of Sonnet 4 usage per week. Max users on the $200 tier get 240 to 480 hours of Sonnet 4, plus 24 to 40 hours of Opus 4. Nearly five months later, the frustration has not subsided.
The issue? Those "hours" aren’t actual hours. They represent token-based limits that change wildly depending on codebase size, conversation length, and the complexity of the code being processed. Independent evaluation suggests the actual per-session limits translate to roughly 44,000 tokens for Pro users and 220,000 tokens for the $200 Max plan.
"It's confusing and vague," one developer wrote in a widely shared evaluation. "After they say '24-40 hours of Opus 4,' that doesn't really let you know anything useful about what you're actually getting."
The backlash on Reddit and developer forums has been fierce. Some users report hitting their each day limits inside half-hour of intensive coding. Others have canceled their subscriptions entirely, calling the brand new restrictions "a joke" and "unusable for real work."
Anthropic has defended the changes, stating that the bounds affect fewer than five percent of users and goal people running Claude Code "repeatedly within the background, 24/7." But the corporate has not clarified whether that figure refers to 5 percent of Max subscribers or five percent of all users — a distinction that matters enormously.
How Block built a free AI coding agent that works offline
Goose takes a radically different approach to the identical problem.
Built by Block, the payments company led by Jack Dorsey, Goose is what engineers call an "on-machine AI agent." Unlike Claude Code, which sends your queries to Anthropic's servers for processing, Goose can run entirely in your local computer using open-source language models that you just download and control yourself.
The project's documentation describes it as going "beyond code suggestions" to "install, execute, edit, and test with any LLM." That last phrase — "any LLM" — is the important thing differentiator. Goose is model-agnostic by design.
You’ll be able to connect Goose to Anthropic's Claude models if you’ve gotten API access. You need to use OpenAI's GPT-5 or Google's Gemini. You’ll be able to route it through services like Groq or OpenRouter. Or — and that is where things get interesting — you possibly can run it entirely locally using tools like Ollama, which allow you to download and execute open-source models on your individual hardware.
The sensible implications are significant. With an area setup, there aren’t any subscription fees, no usage caps, no rate limits, and no concerns about your code being sent to external servers. Your conversations with the AI never leave your machine.
"I exploit Ollama on a regular basis on planes — it's a whole lot of fun!" Sareen noted during an illustration, highlighting how local models free developers from the constraints of web connectivity.
What Goose can try this traditional code assistants can't
Goose operates as a command-line tool or desktop application that may autonomously perform complex development tasks. It may construct entire projects from scratch, write and execute code, debug failures, orchestrate workflows across multiple files, and interact with external APIs — all without constant human oversight.
The architecture relies on what the AI industry calls "tool calling" or "function calling" — the power for a language model to request specific actions from external systems. Once you ask Goose to create a brand new file, run a test suite, or check the status of a GitHub pull request, it doesn't just generate text describing what should occur. It actually executes those operations.
This capability depends heavily on the underlying language model. Claude 4 models from Anthropic currently perform best at tool calling, based on the Berkeley Function-Calling Leaderboard, which ranks models on their ability to translate natural language requests into executable code and system commands.
But newer open-source models are catching up quickly. Goose's documentation highlights several options with strong tool-calling support: Meta's Llama series, Alibaba's Qwen models, Google's Gemma variants, and DeepSeek's reasoning-focused architectures.
The tool also integrates with the Model Context Protocol, or MCP, an emerging standard for connecting AI agents to external services. Through MCP, Goose can access databases, search engines like google and yahoo, file systems, and third-party APIs — extending its capabilities far beyond what the bottom language model provides.
Setting Up Goose with a Local Model
For developers considering a totally free, privacy-preserving setup, the method involves three important components: Goose itself, Ollama (a tool for running open-source models locally), and a compatible language model.
Step 1: Install Ollama
Ollama is an open-source project that dramatically simplifies the strategy of running large language models on personal hardware. It handles the complex work of downloading, optimizing, and serving models through a straightforward interface.
Download and install Ollama from ollama.com. Once installed, you possibly can pull models with a single command. For coding tasks, Qwen 2.5 offers strong tool-calling support:
ollama run qwen2.5
The model downloads mechanically and begins running in your machine.
Step 2: Install Goose
Goose is offered as each a desktop application and a command-line interface. The desktop version provides a more visual experience, while the CLI appeals to developers preferring working entirely within the terminal.
Installation instructions vary by operating system but generally involve downloading from Goose's GitHub releases page or using a package manager. Block provides pre-built binaries for macOS (each Intel and Apple Silicon), Windows, and Linux.
Step 3: Configure the Connection
In Goose Desktop, navigate to Settings, then Configure Provider, and choose Ollama. Confirm that the API Host is ready to http://localhost:11434 (Ollama's default port) and click on Submit.
For the command-line version, run goose configure, select "Configure Providers," select Ollama, and enter the model name when prompted.
That's it. Goose is now connected to a language model running entirely in your hardware, able to execute complex coding tasks with none subscription fees or external dependencies.
The RAM, processing power, and trade-offs you must find out about
The apparent query: what type of computer do you would like?
Running large language models locally requires substantially more computational resources than typical software. The important thing constraint is memory — specifically, RAM on most systems, or VRAM if using a dedicated graphics card for acceleration.
Block's documentation suggests that 32 gigabytes of RAM provides "a solid baseline for larger models and outputs." For Mac users, this implies the pc's unified memory is the first bottleneck. For Windows and Linux users with discrete NVIDIA graphics cards, GPU memory (VRAM) matters more for acceleration.
But you don't necessarily need expensive hardware to start. Smaller models with fewer parameters run on rather more modest systems. Qwen 2.5, for example, is available in multiple sizes, and the smaller variants can operate effectively on machines with 16 gigabytes of RAM.
"You don't must run the most important models to get excellent results," Sareen emphasized. The sensible suggestion: start with a smaller model to check your workflow, then scale up as needed.
For context, Apple's entry-level MacBook Air with 8 gigabytes of RAM would struggle with most capable coding models. But a MacBook Pro with 32 gigabytes — increasingly common amongst skilled developers — handles them comfortably.
Why keeping your code off the cloud matters greater than ever
Goose with an area LLM will not be an ideal substitute for Claude Code. The comparison involves real trade-offs that developers should understand.
Model Quality: Claude 4.5 Opus, Anthropic's flagship model, stays arguably probably the most capable AI for software engineering tasks. It excels at understanding complex codebases, following nuanced instructions, and producing high-quality code on the primary attempt. Open-source models have improved dramatically, but a spot persists — particularly for probably the most difficult tasks.
One developer who switched to the $200 Claude Code plan described the difference bluntly: "After I say 'make this look modern,' Opus knows what I mean. Other models give me Bootstrap circa 2015."
Context Window: Claude Sonnet 4.5, accessible through the API, offers a large one-million-token context window — enough to load entire large codebases without chunking or context management issues. Most local models are limited to 4,096 or 8,192 tokens by default, though many could be configured for longer contexts at the fee of increased memory usage and slower processing.
Speed: Cloud-based services like Claude Code run on dedicated server hardware optimized for AI inference. Local models, running on consumer laptops, typically process requests more slowly. The difference matters for iterative workflows where you're making rapid changes and waiting for AI feedback.
Tooling Maturity: Claude Code advantages from Anthropic's dedicated engineering resources. Features like prompt caching (which may reduce costs by as much as 90 percent for repeated contexts) and structured outputs are polished and well-documented. Goose, while actively developed with 102 releases to this point, relies on community contributions and will lack equivalent refinement in specific areas.
How Goose stacks up against Cursor, GitHub Copilot, and the paid AI coding market
Goose enters a crowded market of AI coding tools, but occupies a particular position.
Cursor, a preferred AI-enhanced code editor, charges $20 per thirty days for its Pro tier and $200 for Ultra—pricing that mirrors Claude Code's Max plans. Cursor provides roughly 4,500 Sonnet 4 requests per thirty days on the Ultra level, a substantially different allocation model than Claude Code's hourly resets.
Cline, Roo Code, and similar open-source projects offer AI coding assistance but with various levels of autonomy and gear integration. Many concentrate on code completion moderately than the agentic task execution that defines Goose and Claude Code.
Amazon's CodeWhisperer, GitHub Copilot, and enterprise offerings from major cloud providers goal large organizations with complex procurement processes and dedicated budgets. They’re less relevant to individual developers and small teams in search of lightweight, flexible tools.
Goose's combination of real autonomy, model agnosticism, local operation, and 0 cost creates a singular value proposition. The tool will not be attempting to compete with industrial offerings on polish or model quality. It's competing on freedom — each financial and architectural.
The $200-a-month era for AI coding tools could also be ending
The AI coding tools market is evolving quickly. Open-source models are improving at a pace that continually narrows the gap with proprietary alternatives. Moonshot AI's Kimi K2 and z.ai's GLM 4.5 now benchmark near Claude Sonnet 4 levels — and so they're freely available.
If this trajectory continues, the standard advantage that justifies Claude Code's premium pricing may erode. Anthropic would then face pressure to compete on features, user experience, and integration moderately than raw model capability.
For now, developers face a transparent selection. Those that need the best possible model quality, who can afford premium pricing, and who accept usage restrictions may prefer Claude Code. Those that prioritize cost, privacy, offline access, and suppleness have a real alternative in Goose.
The incontrovertible fact that a $200-per-month industrial product has a zero-dollar open-source competitor with comparable core functionality is itself remarkable. It reflects each the maturation of open-source AI infrastructure and the appetite amongst developers for tools that respect their autonomy.
Goose will not be perfect. It requires more technical setup than industrial alternatives. It relies on hardware resources that not every developer possesses. Its model options, while improving rapidly, still trail the most effective proprietary offerings on complex tasks.
But for a growing community of developers, those limitations are acceptable trade-offs for something increasingly rare within the AI landscape: a tool that actually belongs to them.
Goose is offered for download at github.com/block/goose. Ollama is offered at ollama.com. Each projects are free and open source.
