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Tracked since 2026
The Bottom Line
- Price
- Free, no paid tier. Compare plans
Key facts
- AI agent that runs in your terminal
- Execute complex coding tasks
- Works with your local files
Pros
- Completely free and open source
- True autonomous execution
- Works with any LLM provider
- Both desktop and CLI versions
- Runs entirely locally
- Strong enterprise backing (Block)
- Active community (359 contributors)
- Offline-capable
Cons
- Requires own API keys
- Newer project
- Less documentation than Claude Code
- Autonomous mode requires trust
- Learning curve for configuration
What is Goose?
Goose is an open source, locally-run AI agent that automates engineering tasks. Developed by Block (formerly Square), Goose operates autonomously to complete development tasks end-to-end rather than just providing suggestions. It can build projects from scratch, write and execute code, debug failures, and orchestrate complex workflows. With support for any LLM and multiple model configurations, Goose offers flexibility for cost and performance optimization. Available as both desktop app and CLI, with MCP integration for extensibility.
Available on: macOS, Linux, Windows
Data compiled by Toolradar Pricing checked · How we evaluate
Sourcegithub.comPreview
Key Features
- Fully open source (Apache 2.0)
- Autonomous task execution
- Multi-LLM support
- Desktop and CLI interfaces
- MCP server integration
- Builds projects from scratch
- Code execution and debugging
- Workflow orchestration
- External API interaction
- Local-first architecture
- Cost/performance optimization
- Active Block development
- 359+ contributors
Pricing Plans
Pricing checked Oct 4, 2026. Source: github.com/block/goose, read Oct 4, 2026
| Plan | Price | Details |
|---|---|---|
| Open Source | Free |
+1 more Show less
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Is Goose worth the price?
Great value
Goose's pricing is exceptionally generous, as it is entirely free and open-source.
This makes it an incredibly fair and accessible option for anyone looking to leverage an AI coding agent without any financial commitment. It is best for individual developers, small teams, and open-source enthusiasts who are comfortable with self-hosting and community support.
Reviews
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Goose FAQ
How does Goose automate engineering tasks?
Goose operates as an autonomous AI agent, completing development tasks end-to-end rather than just offering suggestions. It can build projects from scratch, write and execute code, debug failures, and orchestrate complex workflows without constant human intervention.
Which teams would benefit most from using Goose?
Teams looking for an AI agent that can handle engineering tasks autonomously, from code generation to debugging, would find Goose beneficial. Its local execution and support for various LLMs make it suitable for developers who prioritize data privacy and customization.
How is Goose priced?
Goose is completely free to use, as it is an open-source project. Users are not required to subscribe to any paid plans to access its features, though they will need to provide their own API keys for LLM providers.
What kind of flexibility does Goose offer regarding language models?
Goose supports any large language model (LLM) and offers multiple model configurations, allowing users to optimize for cost or performance. This flexibility enables integration with preferred LLM providers and fine-tuning for specific needs.
Can Goose be used without an internet connection?
Yes, Goose is offline-capable because it runs entirely locally on the user's machine. This allows developers to continue their work even without an active internet connection, provided the necessary models are set up locally.
How does Goose compare to Claude Code?
Goose distinguishes itself from Claude Code by offering true autonomous execution of engineering tasks, rather than just providing suggestions. While Claude Code may have more extensive documentation, Goose is open source, runs locally, and supports any LLM, requiring users to provide their own API keys.
What are the main trade-offs when adopting Goose?
Adopting Goose involves a learning curve for configuration, and its autonomous mode requires a degree of trust in the agent's decisions. Users also need to provide their own API keys for LLM providers, as these are not included with the free, open-source tool.
Source: github.com