Quick Answer: Block Goose is an open-source local AI agent that acts as an intelligent traffic controller for your AI requests. Instead of juggling multiple AI tools, it provides a single CLI interface that analyzes your prompts and routes them to the best service like ChatGPT for explanations, Copilot for code generation, or custom APIs for specialized tasks.

What Makes Block Goose Different?

  • Local First — Your code stays on your machine unless you explicitly send it to an AI service
  • Smart Routing — Automatically routes prompts to the right AI tool based on context
  • Extensible — Connect to any MCP server or API to expand capabilities
  • Open Source — Built with full transparency

How Block Goose Works

Block Goose uses a plugin architecture built on the Model Context Protocol (MCP). The CLI accepts natural language prompts and routes them based on:

  • Prompt analysis — Keywords, file types, and context
  • Provider capabilities — What each AI service does best
  • MCP servers — Extensions for custom APIs and services

The system parses your prompt, selects the best AI service, adds relevant context, and returns formatted results.

Real-World Use Cases

Common use cases include:

Debugging Made Simple

Instead of spending hours tracing through error logs, you can ask Block Goose to analyze the problem:

# Run your script and pipe errors to goose for debugging
uv run index.py "some args" 2>&1 | goose run --instructions -

# Or create a debug instruction file
echo "debug this error and fix the problem in the script" > debug.txt
uv run index.py "some args" 2>&1 >> debug.txt
goose run --instructions debug.txt

Code Migration

# Convert shell script to Python
echo "Convert this shell script to Python:" > migration.txt
cat script.sh >> migration.txt
goose run --instructions migration.txt
rm migration.txt

API Testing

# Test an API endpoint and analyze the response
curl -s https://jsonplaceholder.typicode.com/posts/1 | goose run --instructions -

# Generate test cases from API response
echo "Generate a Python test file for this API endpoint:" > api_test.txt
curl -s https://jsonplaceholder.typicode.com/posts/1 >> api_test.txt
goose run --instructions api_test.txt

Why Local AI Matters

Block Goose’s local-first approach keeps your proprietary code on your machine while still leveraging AI services. You control what gets sent where and can audit exactly what’s happening with your data.

Getting Started with Block Goose

The setup is surprisingly straightforward:

  1. Install Goose — Follow the installation guide on their website.
  2. Configure your AI services — Connect your ChatGPT, Copilot, or other API keys.
  3. Start automating — Begin with simple tasks and gradually tackle more complex workflows.

When to Use Block Goose

Block Goose works best for:

  • Complex debugging requiring multiple AI perspectives
  • Code migration and systematic refactoring
  • API integration with complex business logic
  • Documentation generation from existing codebases
  • Test data creation with validation requirements

The Future of Local AI

Block Goose represents a shift toward intelligent, privacy-conscious development tools. The open-source nature means the community can extend it in ways the original developers never imagined.

Want to connect it to your internal APIs? Build an extension. Need it to work with your custom AI models? Add an MCP server.

Built by the team at Block (formerly Square), it combines local execution with intelligent AI routing to give developers both privacy and power.

Build an extension.

Do you need it to work with your custom AI models?

Add an MCP server.

Final Thoughts

Block Goose represents a shift toward more intelligent, privacy-conscious development tools. By combining local execution with intelligent AI routing, it gives developers the best of both worlds: privacy and power.

Block Goose is open source and built by the team at Block (formerly Square) which gives me confidence in its long-term viability.

So, now that you know what Block Goose is, give it a whirl.

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