Skip to content
Back to Tools
Codename Goose

Codename Goose

Engineering

Codename Goose is an open-source AI agent framework developed by Block (owner of Square). It connects large language models to real-world actions through a standardized protocol. Goose runs locally on your machine and can be pointed at any LLM of your choice. It functions as a true agent by not only generating code but also executing tasks autonomously - reading and writing files, running tests, installing dependencies, and handling other actions as needed.

Quick Info

Integrations:GitHub, Google Drive, Model Context Protocol (MCP), JetBrains IDEs
Deployment:Command Line, Desktop App
Expertise:Intermediate
Company Size:Enterprise, SMB, Startup

Screenshots

codename goose mcp server integrationcodename goose code automation example

Key Features

Extensible Architecture

Uses Anthropic's Model Context Protocol (MCP) to connect with various systems and tools. Discovers new systems on the fly to expand capabilities.

Multiple Interfaces

Includes both desktop application and command line interfaces for different workflow preferences.

Model Flexibility

Works with any large language model of your choice. You are not locked into a specific AI provider.

Autonomous Action

Executes tasks independently - runs code, writes files, and installs dependencies without constant supervision.

Use Cases

Code Migration

Automate moving codebases between frameworks (Ember to React) or languages (Ruby to Kotlin).

Test Generation

Create unit tests for software projects and increase code coverage above specific thresholds.

Performance Optimization

Conduct performance benchmarks for build commands using automation tools.

API Development

Scaffold APIs for data retention and other common development tasks.

Code Navigation

Explore and work with unfamiliar codebases or programming languages more efficiently.

Pricing

Free and open-source under Apache License 2.0

Setup Steps

  1. Download Goose from the official GitHub repository
  2. Install required dependencies
  3. Configure your preferred LLM provider connection
  4. Grant appropriate system permissions
  5. Start using via desktop app or command line