Bolt.new AI and Canvas LMS integration: Step-by-Step Guide 2025

 

Prerequisites

 

• A Bolt.new AI project set up with TypeScript.
• A Canvas LMS account along with an API token. This token will be used for authenticating API calls to Canvas LMS.
• Basic understanding of TypeScript and HTTP APIs.

 

Adding Dependencies via Code

 

• Since Bolt.new AI does not offer access to a terminal, you need to install dependencies by manually configuring your package file.
• Create or modify an existing file named package.json in your project root with the following content:

• 
  {
    "name": "bolt-canvas-integration",
    "version": "1.0.0",
    "dependencies": {
      "axios": "^0.27.2"
    },
    "devDependencies": {
      "typescript": "^4.9.5"
    },
    "scripts": {
      "start": "ts-node index.ts"
    }
  }
        

 

Creating the Canvas Service File

 

• Create a new file named canvasService.ts in the project’s root or in a utilities/services folder.
• This file will contain the integration logic for connecting to Canvas LMS using its REST API. Insert the following TypeScript code:

• 
  import axios from 'axios';
  
  // Replace 'YOURCANVASAPI_TOKEN' with your API token or use environment variables
  const canvasAPIToken = process.env.CANVASAPITOKEN || 'YOURCANVASAPI_TOKEN';
  const canvasBaseUrl = 'https://canvas.instructure.com/api/v1';
  // Function to fetch courses from Canvas LMS
  export async function getCourses() {
    try {
      const response = await axios.get(${canvasBaseUrl}/courses, {
        headers: {
          'Authorization': Bearer ${canvasAPIToken}
        }
      });
      return response.data;
    } catch (error) {
      console.error('Error fetching courses:', error);
      throw error;
    }
  }
        

  
  

 

Integrating Canvas Service into Your Main Project

 

• Create a file named index.ts in the root directory if it does not already exist.
• This file will serve as the main entry point for testing the Canvas LMS integration. Insert the following code:

• 
  import { getCourses } from './canvasService';
  
  (async () => {
    try {
      const courses = await getCourses();
      console.log('Canvas courses:', courses);
    } catch (error) {
      console.error('Error in retrieving courses:', error);
    }
  })();
        

  
  

 

Configuring Environment Variables

 

• To securely store your Canvas LMS API token, use Bolt.new AI's environment variable feature or secret management as provided by the platform.
• If using environment variables, ensure you set CANVASAPITOKEN in the platform’s configuration. The code in canvasService.ts automatically picks up this value.

 

Testing the Integration

 

• Once all files are updated, use the Bolt.new AI project's built-in Run or Preview feature to execute your code.
• Review the console output to ensure that the Canvas LMS courses are logged correctly without any errors.
• If there are errors, double-check the API token, endpoint, and any network restrictions that might be in place.

 

Deploying and Using Your Integration

 

• After testing, your integration with Canvas LMS is ready for production use within your Bolt.new AI project.
• You can expand your integration by adding more functions (such as creating courses, updating enrollments, etc.) following the same pattern shown in canvasService.ts.
• Remember to protect sensitive information by storing API keys securely and not committing them directly in your code.