How to Call GitHub Copilot from a VSCode Extension | Generated by AI

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GitHub Copilot is a powerful AI-powered coding assistant integrated into Visual Studio Code (VSCode) and IntelliJ IDEA through their respective plugins. While there’s no public REST API for directly calling Copilot, developers can interact with its capabilities programmatically within a VSCode extension using the VSCode Chat API, Language Model API, or command-based interactions. This blog post walks through creating a VSCode extension that triggers Copilot’s chat functionality with a custom prompt, effectively simulating an “API call” to Copilot, and explains how to leverage Copilot itself to streamline development.

Understanding Copilot’s Integration in VSCode

GitHub Copilot doesn’t expose a traditional API (e.g., REST endpoints) for direct programmatic access. Instead, its functionality is available through:

This guide focuses on using the workbench.action.chat.open command to trigger Copilot’s chat interface, as it’s the simplest way to integrate Copilot’s capabilities into an extension.

Step-by-Step: Building a VSCode Extension to Trigger Copilot Chat

Below is a step-by-step guide to creating a VSCode extension that opens Copilot’s chat window with a custom prompt, effectively “calling” Copilot to process a user-defined query.

1. Set Up the VSCode Extension

  1. Scaffold the Project:
    • Install the Yeoman VSCode extension generator: npm install -g yo generator-code.
    • Run yo code and select “New Extension (TypeScript)” to create a TypeScript-based extension.
    • Name the extension, e.g., copilot-api-caller.
  2. Configure package.json:
    • Define a command to trigger Copilot’s chat.
    • Example package.json:
{
  "name": "copilot-api-caller",
  "displayName": "Copilot API Caller",
  "description": "Triggers GitHub Copilot Chat with a custom prompt",
  "version": "0.0.1",
  "engines": {
    "vscode": "^1.85.0"
  },
  "categories": ["Other"],
  "activationEvents": [
    "onCommand:copilot-api-caller.triggerCopilotChat"
  ],
  "main": "./out/extension.js",
  "contributes": {
    "commands": [
      {
        "command": "copilot-api-caller.triggerCopilotChat",
        "title": "Trigger Copilot Chat"
      }
    ]
  },
  "scripts": {
    "vscode:prepublish": "npm run compile",
    "compile": "tsc -p ./",
    "watch": "tsc -watch -p ./"
  },
  "devDependencies": {
    "@types/vscode": "^1.85.0",
    "@types/node": "^20.2.5",
    "typescript": "^5.1.3"
  }
}

2. Write the Extension Code

Create the extension logic to register a command that opens Copilot’s chat with a user-provided prompt.

import * as vscode from 'vscode';

export function activate(context: vscode.ExtensionContext) {
  // Register the command to trigger Copilot Chat
  let disposable = vscode.commands.registerCommand('copilot-api-caller.triggerCopilotChat', async () => {
    // Get user input for the prompt
    const prompt = await vscode.window.showInputBox({
      prompt: 'Enter your query for GitHub Copilot',
      placeHolder: 'e.g., Write a JavaScript function to sort an array'
    });

    if (prompt) {
      try {
        // Execute the command to open Copilot Chat with the prompt
        await vscode.commands.executeCommand('workbench.action.chat.open', prompt);
        vscode.window.showInformationMessage('Sent prompt to Copilot Chat!');
      } catch (error) {
        vscode.window.showErrorMessage(`Failed to trigger Copilot Chat: ${error}`);
      }
    }
  });

  context.subscriptions.push(disposable);
}

export function deactivate() {}

3. Enhance with Context (Optional)

To make the extension more powerful, include context from the editor, such as selected code, to provide Copilot with additional information.

import * as vscode from 'vscode';

export function activate(context: vscode.ExtensionContext) {
  let disposable = vscode.commands.registerCommand('copilot-api-caller.triggerCopilotChat', async () => {
    // Get selected text from the active editor
    const editor = vscode.window.activeTextEditor;
    let context = '';
    if (editor) {
      const selection = editor.selection;
      context = editor.document.getText(selection);
    }

    // Prompt for user input
    const prompt = await vscode.window.showInputBox({
      prompt: 'Enter your query for GitHub Copilot',
      placeHolder: 'e.g., Explain this code',
      value: context ? `Regarding this code: \n\`\`\`\n${context}\n\`\`\`\n` : ''
    });

    if (prompt) {
      try {
        await vscode.commands.executeCommand('workbench.action.chat.open', prompt);
        vscode.window.showInformationMessage('Sent prompt to Copilot Chat!');
      } catch (error) {
        vscode.window.showErrorMessage(`Failed to trigger Copilot Chat: ${error}`);
      }
    }
  });

  context.subscriptions.push(disposable);
}

export function deactivate() {}

4. Test the Extension

  1. Press F5 in VSCode to launch the Extension Development Host.
  2. Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and run Trigger Copilot Chat.
  3. Enter a prompt (e.g., “Generate a REST API client in TypeScript”) or select code and run the command.
  4. Verify that Copilot’s chat window opens with your prompt and provides a response.
  5. Using Copilot: If errors occur, add a comment like // Handle errors for command execution, and Copilot may suggest try-catch blocks or error messages.

5. Advanced: Using the VSCode Chat API

For more control, use the VSCode Chat API to create a custom chat participant that integrates with Copilot’s language models, allowing natural language processing within your extension.

import * as vscode from 'vscode';

export function activate(context: vscode.ExtensionContext) {
  // Register a chat participant
  const participant = vscode.chat.createChatParticipant('copilot-api-caller.myParticipant', async (request, context, stream, token) => {
    stream.markdown('Processing your query...\n');
    // Use the Language Model API to generate a response
    const model = await vscode.lm.selectChatModels({ family: 'gpt-4' })[0];
    if (model) {
      const response = await model.sendRequest([{ text: request.prompt }], {}, token);
      for await (const chunk of response.stream) {
        stream.markdown(chunk);
      }
    } else {
      stream.markdown('No suitable model available.');
    }
  });

  participant.iconPath = new vscode.ThemeIcon('sparkle');
  context.subscriptions.push(participant);
}

export function deactivate() {}

6. Package and Deploy

  1. Install vsce: npm install -g @vscode/vsce.
  2. Run vsce package to create a .vsix file.
  3. Install the extension in VSCode via the Extensions view or share the .vsix file with others.
  4. Using Copilot: Add a comment like // Add script to package extension in package.json, and Copilot may suggest the vscode:prepublish script.

Leveraging Copilot During Development

GitHub Copilot can significantly speed up extension development:

Limitations

Conclusion

By using VSCode’s command system or Chat API, developers can effectively “call” GitHub Copilot from a custom extension, enabling programmatic interaction with its AI capabilities. The example above demonstrates triggering Copilot’s chat with a custom prompt, enhanced with editor context, and provides a foundation for more advanced integrations using the Language Model API. With Copilot’s assistance during development, creating such extensions becomes faster and more intuitive.

For further exploration, consider experimenting with the Chat API for custom participants or integrating additional editor context for richer prompts. Happy coding!

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