Welcome to the Goose CLI integration guide. This guide will help you connect ByteRover with Goose CLI, enabling this multi-model AI assistant to remember your coding patterns and solutions.

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What You’ll Need

  • Goose CLI installed
  • A ByteRover account

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Getting Started

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1. Start Connection

Go to your ByteRover dashboard:
  1. Click “Connect your agent”
  2. Select Goose CLI
  3. Hit “Connect with Goose CLI”

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2. Connect ByteRover to Goose CLI

Step 1: Open Goose configure: 
goose configure
Step 2: Configure Byterover for Goose by following: What would you like to do? Answer: Add Extension What type of extension would you like to add? Answer: Remote Extension (Streaming HTTP) What would you like to call this extension? Answer: byterover-mcp What is the streaming HTTP endpoint URI? Answer: https://mcp.byterover.dev/v2/mcp Please set the timeout for this tool (in secs): Answer: 300 Would you like to add a description? Answer: No Would you like to add custom headers? Answer: Yes Header name: Answer: Authorization Header value: Answer: Bearer [DASHBOARD_TOKEN] Add another header? Answer: No Step 3: Connect to ByteRover Type goose to start Goose CLI and process authorization with ByteRover. Once authorization is complete, you will see the main Goose window.

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Test Connection

Try this with Goose: 
Store this validation helper to ByteRover:

function validateRequired(obj, fields) {
  const missing = fields.filter(field => 
    obj[field] === undefined || obj[field] === null || obj[field] === ''
  );
  
  return {
    valid: missing.length === 0,
    missing: missing
  };
}
Goose should confirm storage to ByteRover.

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Benefits

Goose can switch between different AI models while maintaining consistent memory of your coding patterns through ByteRover.
Your development history and solutions remain available across all Goose sessions, regardless of which AI model is active.
Join our Discord for Goose CLI support.