Goose
Block's open-source CLI coding agent that executes shell commands, edits files, and extends via MCP plugins on any LLM - free under MIT license.
Goose is an open-source autonomous AI coding agent created by Block (formerly Square) and released in October 2024 under the MIT license. Running from the command line, Goose completes multi-step coding tasks by writing and executing code, editing files, browsing the web, and calling APIs without manual intervention between steps. Unlike most coding agents tied to a single cloud provider, Goose is model-agnostic and connects to OpenAI, Anthropic, Ollama, Groq, and any MCP-compatible LLM through interchangeable provider adapters. The extension system is built on the Model Context Protocol, enabling teams to integrate Goose with internal APIs, databases, and developer tools without modifying the agent core.
Key Features
- Terminal-first design executes shell commands, reads and writes files, and calls external APIs directly from a CLI session
- Provider-agnostic architecture connects to OpenAI, Anthropic, Ollama, Groq, and any MCP-compatible LLM with a config change
- MCP (Model Context Protocol) extension support adds custom tools for GitHub, databases, browser automation, and internal APIs
- Local model execution via Ollama enables fully private offline coding assistance with zero data sent to cloud providers
- Session state persistence allows reviewing and restarting interrupted tasks without losing the original task context
- Toolkit manager installs and configures extensions through the Goose config file without requiring code changes
- MIT license with no usage restrictions permits embedding Goose into commercial products and internal developer platforms
Use Cases
- Backend developers running multi-step refactoring and debugging tasks from the terminal without switching to a GUI agent
- Platform teams deploying Goose with custom MCP servers to give internal users AI access to proprietary APIs and databases
- Privacy-focused organizations running Goose with local Ollama models for zero data-sharing compliance requirements
- DevOps engineers automating server-side scripting and infrastructure tasks using an AI agent with direct shell access
Pros
- MIT-licensed with no usage caps - the only cost is LLM API tokens consumed, with no seat fees or subscription overhead
- Local model support via Ollama enables completely private offline AI coding assistance with no cloud dependency
- MCP extension architecture makes integrating Goose with any internal system possible without touching the agent core code
Cons
- CLI-only interface requires terminal fluency - less accessible to developers accustomed to GUI agents like Cursor or Windsurf
- Community-maintained extension ecosystem has variable quality and coverage compared to curated commercial agent tool libraries
- Younger project than Aider or Claude Code with fewer documented workflows and community-tested patterns for complex tasks
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