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Use OpenAI GPT function calling with JavaScript and Cloudflare Workers

​​ Before you start

All of the tutorials assume you have already completed the Get started guide, which gets you set up with a Cloudflare Workers account, C3, and Wrangler.

​​ Overview

In this tutorial, you will learn to use OpenAI function calling with JavaScript and Cloudflare Workers. Your Worker application will interact with the OpenAI API to define a function in your Worker code, have OpenAI tell you when to call that function and share those results with OpenAI to continue that conversation. For this guide, you will allow users to specify real-time data that needs to be pulled from a URL.

You will learn how to:

  • Make an API call from Cloudflare Workers to OpenAI’s API using JavaScript.
  • Determine if OpenAI is indicating you should call a function in your code based on a user’s input.
  • Use Worker secrets with Wrangler.

​​ 1. Create a Worker project

Create a Worker project in the command line:

Create a Worker project
$ npm create cloudflare@latest

For setup, select the following options:

  • For Where do you want to create your application?, indicate openai-function-calling-workers.
  • For What type of application do you want to create?, choose "Hello World" Worker.
  • For Do you want to use TypeScript?, choose no.
  • For Do you want to deploy your application?, choose yes.

Go to your new openai-function-calling-workers Worker project:

$ cd openai-function-calling-workers

Inside of your new openai-function-calling-workers directory, find the src/index.js file. You will configure this file for most of the tutorial.

You will also need an OpenAI account and API key for this tutorial. If you do not have one, create a new OpenAI account and create an API key to continue with this tutorial. Make sure to store you API key somewhere safe so you can use it later.

​​ 2. Make a request to OpenAI

With your Worker project created, make your first request to OpenAI. You will use the OpenAI node library to interact with the OpenAI API. You will also be using the axios-fetch adapter to interact with OpenAI’s node library on the edge. Install the OpenAI node library and axios-fetch adapter with npm:

$ npm install openai
$ npm install @vespaiach/axios-fetch-adapter

Initially, your generated index.js file should look like this:

export default {
async fetch(request, env, ctx) {
return new Response('Hello World!');

Above export default, add the imports for openai and axios-fetch:

import { Configuration, OpenAIApi } from "openai";
import fetchAdapter from "@vespaiach/axios-fetch-adapter";

Within your fetch function, set up the configuration and instantiate your OpenAIApi client:

async fetch(request, env, ctx) {
const configuration = new Configuration({
apiKey: env.OPENAI_API_KEY,
baseOptions: {
adapter: fetchAdapter
const openai = new OpenAIApi(configuration);
return new Response('Hello World!');

To make this work, you need to use wrangler secret put to set your OPENAI_API_KEY. This key is the API key you created earlier in the OpenAI dashboard:

$ npx wrangler secret put <OPENAI_API_KEY>

For local development, create a new file .dev.vars in your Worker project and add this line. Make sure to replace OPENAI_API_KEY with your own OpenAI API key:


Now, make a request to the OpenAI Chat Completions API with your functions argument to indicate that you are enabling function calling with this request.

try {
const chatCompletion = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
messages: [{role: "user", content: "What's happening in the NBA today?"}],
functions: [
name: "read_website_content",
description: "Read the content on a given website",
parameters: {
type: "object",
properties: {
url: {
type: "string",
description: "The URL to the website to read ",
required: ["url"],
const msg =[0].message;
return new Response('Hello World!');
} catch (e) {
return new Response(e);

Review the arguments you are passing to OpenAI:

  • model: The model you want OpenAI to use for your request.
  • messages: A list containing the messages that are part of the conversation happening. In this guide, you only have one user message asking what is happening in the NBA today.
  • functions: A list containing all the functions that you are telling OpenAI about. In this guide, you only have one function. Your function has the following properties:
    • name: The name of your function. You will be calling yours read_website_content.
    • description: A short description that lets OpenAI know what your function does. You are letting OpenAI know that your function reads the content on a given website.
    • parameters: The parameters that the function can accept described as a JSON Schema object. You will have one parameter called url.

After your request to OpenAI completes, you are logging the message back to confirm it is telling you to call your function. Run your code with npx wrangler dev and open it in a browser by pressing b. You should see the following in your terminal log:

Object {
name: read_website_content,
arguments: {
"url": ""

Function calling intelligently determines what content to pass in the argument. Take note that you did not specify your messages content as "what's happening on today?", but instead asked "What's happening in the NBA today?". OpenAI determined that “” was the right URL to pass to your function.

​​ 3. Building your read_website_content() function

Add the code to call your function when OpenAI determines you need to:

let websiteContent;
if ( === "read_website_content") {
const url = JSON.parse(msg.function_call.arguments).url;
websiteContent = await read_website_content(url);

The above function does not exist. You need to create it. Use a node library called cheerio to the websites content. Run npm to install cheerio:

$ npm install cheerio

With cheerio installed, import it at the top of your index.js file and immediately create your read_website_content function:

import cheerio from "cheerio";
async function read_website_content(url) {
console.log("reading website content");
const response = await fetch(url);
const body = await response.text();
let cheerioBody = await cheerio.load(body);
const resp = {
website_body: cheerioBody("p").text(),
url: url
return JSON.stringify(resp);

In this function, you take the URL that you received back from OpenAI and use JavaScript’s Fetch API to pull the content of the website. Then, your function will use cheerio to pull out only the text of the website. You then create a JSON object for the response.

With this function in place, run your code again to review that you are properly calling your function and pulling website data:

$ npx wrangler dev

When you open a browser, you should see the log of the website content in your terminal.

​​ 4. Send your function response back to OpenAI

The last part of your application is returning the data you got back from your index.js function to OpenAI and having it answer the user’s original question. Right after you log websiteContent, make a second call to the chat completion API:

const secondChatCompletion = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
messages: [
{role: "user", content: "What's happening in New York City today?"},
role: "function",
content: websiteContent
return new Response([0].message.content);

This request to OpenAI will look similar to first request. But this time, instead of passing the schema data about your function, you are passing a message that contains the response you got back from your function. OpenAI will use this information to build its response, which you will output to the browser.

Run your code again by running npx wrangler dev and open it in your browser. This will now show you OpenAI’s response using real-time information from the website. You can try other websites and topics by updating the user’s message in your two API calls.

​​ 5. Deploy your Worker application

To deploy your application, run the npx wrangler deploy command to deploy your Worker application:

$ npx wrangler deploy

You can now preview your Worker at <YOUR_WORKER>.<YOUR_SUBDOMAIN>

​​ 6. Next steps

Reference the finished code for this tutorial on GitHub.

To continue working with Workers and AI, refer to the guide on using LangChaing and Cloudflare Workers together or how to build a ChatGPT plugin with Cloudflare Workers.

If you have any questions, need assistance, or would like to share your project, join the Cloudflare Developer community on Discord to connect with fellow developers and the Cloudflare team.