Which is Better AI Interaction Libraries?
openai vs @azure/openai vs chatgpt
1 Year
openai@azure/openaichatgpt
What's AI Interaction Libraries?

These libraries facilitate interaction with OpenAI's models, enabling developers to integrate advanced natural language processing capabilities into their applications. They provide various functionalities for generating text, understanding context, and managing conversational AI, making it easier for developers to leverage AI in their projects. Each package has its unique features and use cases, catering to different needs in AI development.

NPM Package Downloads Trend
Github Stars Ranking
Stat Detail
Package
Downloads
Stars
Size
Issues
Publish
License
openai2,313,0087,9572.73 MB753 days agoApache-2.0
@azure/openai193,618-397 kB-10 days agoMIT
chatgpt12,18216,502131 kB14a year agoMIT
Feature Comparison: openai vs @azure/openai vs chatgpt

Integration

  • openai: openai offers a more general integration with OpenAI's API, allowing developers to access various models for different tasks. It provides flexibility in how you can implement AI functionalities, making it suitable for a wide range of applications beyond just chat.
  • @azure/openai: @azure/openai is designed for seamless integration with Azure services, allowing developers to utilize Azure's infrastructure for enhanced performance and security. It supports Azure's authentication and resource management features, making it suitable for enterprise-level applications.
  • chatgpt: chatgpt is tailored for building conversational interfaces, providing built-in features for managing dialogue state and context. It simplifies the process of creating chatbots by focusing on conversational flows and user interactions, making it ideal for chat-based applications.

Ease of Use

  • openai: openai has a flexible API that is relatively easy to use, but it may require a deeper understanding of AI concepts for advanced implementations. It is suitable for developers who want to explore various AI capabilities.
  • @azure/openai: @azure/openai provides a straightforward API that is well-documented, making it easier for developers familiar with Azure to get started quickly. However, it may require some understanding of Azure's ecosystem for optimal use.
  • chatgpt: chatgpt is designed with user-friendly methods for managing conversations, making it easier for developers to implement chat functionalities without deep knowledge of AI. Its focus on dialogue management simplifies the learning curve for new users.

Use Cases

  • openai: openai is versatile and can be used for a variety of tasks, including content generation, summarization, code generation, and more, making it suitable for developers looking for a broad range of AI functionalities.
  • @azure/openai: @azure/openai is ideal for enterprise applications that require secure and compliant AI solutions, such as customer support systems, data analysis, and automated reporting within the Azure ecosystem.
  • chatgpt: chatgpt is best suited for applications focused on conversational AI, such as chatbots for customer service, virtual assistants, and interactive storytelling, where maintaining context is crucial.

Performance

  • openai: openai provides robust performance for a variety of tasks, but response times may vary based on the complexity of the request and the model used. It is important to optimize API calls for performance in high-demand applications.
  • @azure/openai: @azure/openai leverages Azure's infrastructure, providing high availability and scalability for enterprise applications. It can handle large volumes of requests efficiently, making it suitable for production environments.
  • chatgpt: chatgpt is optimized for real-time interactions, ensuring quick responses in conversational applications. Its design focuses on maintaining context and delivering relevant replies, which is essential for user satisfaction in chat interfaces.

Community and Support

  • openai: openai has a large and active community of developers, providing numerous resources, libraries, and forums for support. This can be beneficial for troubleshooting and learning from others' experiences in implementing AI.
  • @azure/openai: @azure/openai benefits from Azure's extensive support and community resources, providing access to a wealth of documentation, tutorials, and forums for troubleshooting and best practices.
  • chatgpt: chatgpt has a growing community focused on conversational AI, offering resources, examples, and shared experiences that can help developers implement effective chat solutions.
How to Choose: openai vs @azure/openai vs chatgpt
  • openai: Opt for openai if you need a general-purpose library that provides access to a wide range of OpenAI models. This package is suitable for diverse applications, from text generation to code completion, and offers flexibility for various AI tasks.
  • @azure/openai: Choose @azure/openai if you are looking for a solution that integrates seamlessly with Azure services, providing robust security and compliance features. This package is ideal for enterprise applications that require scalability and reliability within the Azure ecosystem.
  • chatgpt: Select chatgpt if your primary focus is on building conversational agents or chatbots that require a more tailored approach to dialogue management. It is designed specifically for chat interactions, making it easier to handle context and maintain conversations.
README for openai

OpenAI Node API Library

NPM version npm bundle size JSR Version

This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript.

It is generated from our OpenAPI specification with Stainless.

To learn how to use the OpenAI API, check out our API Reference and Documentation.

Installation

npm install openai

You can also import from jsr:

import OpenAI from 'jsr:@openai/openai';

Usage

The full API of this library can be found in api.md file along with many code examples. The code below shows how to get started using the chat completions API.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

async function main() {
  const chatCompletion = await client.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gpt-3.5-turbo',
  });
}

main();

Streaming responses

We provide support for streaming responses using Server Sent Events (SSE).

import OpenAI from 'openai';

const client = new OpenAI();

async function main() {
  const stream = await client.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  });
  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

main();

If you need to cancel a stream, you can break from the loop or call stream.controller.abort().

Request & Response types

This library includes TypeScript definitions for all request params and response fields. You may import and use them like so:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

async function main() {
  const params: OpenAI.Chat.ChatCompletionCreateParams = {
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'gpt-3.5-turbo',
  };
  const chatCompletion: OpenAI.Chat.ChatCompletion = await client.chat.completions.create(params);
}

main();

Documentation for each method, request param, and response field are available in docstrings and will appear on hover in most modern editors.

[!IMPORTANT] Previous versions of this SDK used a Configuration class. See the v3 to v4 migration guide.

Polling Helpers

When interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes helper functions which will poll the status until it reaches a terminal state and then return the resulting object. If an API method results in an action which could benefit from polling there will be a corresponding version of the method ending in 'AndPoll'.

For instance to create a Run and poll until it reaches a terminal state you can run:

const run = await openai.beta.threads.runs.createAndPoll(thread.id, {
  assistant_id: assistantId,
});

More information on the lifecycle of a Run can be found in the Run Lifecycle Documentation

Bulk Upload Helpers

When creating and interacting with vector stores, you can use the polling helpers to monitor the status of operations. For convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once.

const fileList = [
  createReadStream('/home/data/example.pdf'),
  ...
];

const batch = await openai.vectorStores.fileBatches.uploadAndPoll(vectorStore.id, fileList);

Streaming Helpers

The SDK also includes helpers to process streams and handle the incoming events.

const run = openai.beta.threads.runs
  .stream(thread.id, {
    assistant_id: assistant.id,
  })
  .on('textCreated', (text) => process.stdout.write('\nassistant > '))
  .on('textDelta', (textDelta, snapshot) => process.stdout.write(textDelta.value))
  .on('toolCallCreated', (toolCall) => process.stdout.write(`\nassistant > ${toolCall.type}\n\n`))
  .on('toolCallDelta', (toolCallDelta, snapshot) => {
    if (toolCallDelta.type === 'code_interpreter') {
      if (toolCallDelta.code_interpreter.input) {
        process.stdout.write(toolCallDelta.code_interpreter.input);
      }
      if (toolCallDelta.code_interpreter.outputs) {
        process.stdout.write('\noutput >\n');
        toolCallDelta.code_interpreter.outputs.forEach((output) => {
          if (output.type === 'logs') {
            process.stdout.write(`\n${output.logs}\n`);
          }
        });
      }
    }
  });

More information on streaming helpers can be found in the dedicated documentation: helpers.md

Streaming responses

This library provides several conveniences for streaming chat completions, for example:

import OpenAI from 'openai';

const openai = new OpenAI();

async function main() {
  const stream = await openai.beta.chat.completions.stream({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  });

  stream.on('content', (delta, snapshot) => {
    process.stdout.write(delta);
  });

  // or, equivalently:
  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }

  const chatCompletion = await stream.finalChatCompletion();
  console.log(chatCompletion); // {id: "…", choices: […], …}
}

main();

Streaming with openai.beta.chat.completions.stream({…}) exposes various helpers for your convenience including event handlers and promises.

Alternatively, you can use openai.chat.completions.create({ stream: true, … }) which only returns an async iterable of the chunks in the stream and thus uses less memory (it does not build up a final chat completion object for you).

If you need to cancel a stream, you can break from a for await loop or call stream.abort().

Automated function calls

We provide the openai.beta.chat.completions.runTools({…}) convenience helper for using function tool calls with the /chat/completions endpoint which automatically call the JavaScript functions you provide and sends their results back to the /chat/completions endpoint, looping as long as the model requests tool calls.

If you pass a parse function, it will automatically parse the arguments for you and returns any parsing errors to the model to attempt auto-recovery. Otherwise, the args will be passed to the function you provide as a string.

If you pass tool_choice: {function: {name: …}} instead of auto, it returns immediately after calling that function (and only loops to auto-recover parsing errors).

import OpenAI from 'openai';

const client = new OpenAI();

async function main() {
  const runner = client.beta.chat.completions
    .runTools({
      model: 'gpt-3.5-turbo',
      messages: [{ role: 'user', content: 'How is the weather this week?' }],
      tools: [
        {
          type: 'function',
          function: {
            function: getCurrentLocation,
            parameters: { type: 'object', properties: {} },
          },
        },
        {
          type: 'function',
          function: {
            function: getWeather,
            parse: JSON.parse, // or use a validation library like zod for typesafe parsing.
            parameters: {
              type: 'object',
              properties: {
                location: { type: 'string' },
              },
            },
          },
        },
      ],
    })
    .on('message', (message) => console.log(message));

  const finalContent = await runner.finalContent();
  console.log();
  console.log('Final content:', finalContent);
}

async function getCurrentLocation() {
  return 'Boston'; // Simulate lookup
}

async function getWeather(args: { location: string }) {
  const { location } = args;
  // … do lookup …
  return { temperature, precipitation };
}

main();

// {role: "user",      content: "How's the weather this week?"}
// {role: "assistant", tool_calls: [{type: "function", function: {name: "getCurrentLocation", arguments: "{}"}, id: "123"}
// {role: "tool",      name: "getCurrentLocation", content: "Boston", tool_call_id: "123"}
// {role: "assistant", tool_calls: [{type: "function", function: {name: "getWeather", arguments: '{"location": "Boston"}'}, id: "1234"}]}
// {role: "tool",      name: "getWeather", content: '{"temperature": "50degF", "preciptation": "high"}', tool_call_id: "1234"}
// {role: "assistant", content: "It's looking cold and rainy - you might want to wear a jacket!"}
//
// Final content: "It's looking cold and rainy - you might want to wear a jacket!"

Like with .stream(), we provide a variety of helpers and events.

Note that runFunctions was previously available as well, but has been deprecated in favor of runTools.

Read more about various examples such as with integrating with zod, next.js, and proxying a stream to the browser.

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

  • File (or an object with the same structure)
  • a fetch Response (or an object with the same structure)
  • an fs.ReadStream
  • the return value of our toFile helper
import fs from 'fs';
import fetch from 'node-fetch';
import OpenAI, { toFile } from 'openai';

const client = new OpenAI();

// If you have access to Node `fs` we recommend using `fs.createReadStream()`:
await client.files.create({ file: fs.createReadStream('input.jsonl'), purpose: 'fine-tune' });

// Or if you have the web `File` API you can pass a `File` instance:
await client.files.create({ file: new File(['my bytes'], 'input.jsonl'), purpose: 'fine-tune' });

// You can also pass a `fetch` `Response`:
await client.files.create({ file: await fetch('https://somesite/input.jsonl'), purpose: 'fine-tune' });

// Finally, if none of the above are convenient, you can use our `toFile` helper:
await client.files.create({
  file: await toFile(Buffer.from('my bytes'), 'input.jsonl'),
  purpose: 'fine-tune',
});
await client.files.create({
  file: await toFile(new Uint8Array([0, 1, 2]), 'input.jsonl'),
  purpose: 'fine-tune',
});

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be thrown:

async function main() {
  const job = await client.fineTuning.jobs
    .create({ model: 'gpt-3.5-turbo', training_file: 'file-abc123' })
    .catch(async (err) => {
      if (err instanceof OpenAI.APIError) {
        console.log(err.status); // 400
        console.log(err.name); // BadRequestError
        console.log(err.headers); // {server: 'nginx', ...}
      } else {
        throw err;
      }
    });
}

main();

Error codes are as followed:

| Status Code | Error Type | | ----------- | -------------------------- | | 400 | BadRequestError | | 401 | AuthenticationError | | 403 | PermissionDeniedError | | 404 | NotFoundError | | 422 | UnprocessableEntityError | | 429 | RateLimitError | | >=500 | InternalServerError | | N/A | APIConnectionError |

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

const completion = await client.chat.completions.create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-4' });
console.log(completion._request_id) // req_123

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won't always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({ azureADTokenProvider });

const result = await openai.chat.completions.create({
  model: 'gpt-4-1106-preview',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

Retries

Certain errors will be automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.

You can use the maxRetries option to configure or disable this:

// Configure the default for all requests:
const client = new OpenAI({
  maxRetries: 0, // default is 2
});

// Or, configure per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I get the name of the current day in Node.js?' }], model: 'gpt-3.5-turbo' }, {
  maxRetries: 5,
});

Timeouts

Requests time out after 10 minutes by default. You can configure this with a timeout option:

// Configure the default for all requests:
const client = new OpenAI({
  timeout: 20 * 1000, // 20 seconds (default is 10 minutes)
});

// Override per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I list all files in a directory using Python?' }], model: 'gpt-3.5-turbo' }, {
  timeout: 5 * 1000,
});

On timeout, an APIConnectionTimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Auto-pagination

List methods in the OpenAI API are paginated. You can use the for await … of syntax to iterate through items across all pages:

async function fetchAllFineTuningJobs(params) {
  const allFineTuningJobs = [];
  // Automatically fetches more pages as needed.
  for await (const fineTuningJob of client.fineTuning.jobs.list({ limit: 20 })) {
    allFineTuningJobs.push(fineTuningJob);
  }
  return allFineTuningJobs;
}

Alternatively, you can request a single page at a time:

let page = await client.fineTuning.jobs.list({ limit: 20 });
for (const fineTuningJob of page.data) {
  console.log(fineTuningJob);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = page.getNextPage();
  // ...
}

Advanced Usage

Accessing raw Response data (e.g., headers)

The "raw" Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return.

You can also use the .withResponse() method to get the raw Response along with the parsed data.

const client = new OpenAI();

const response = await client.chat.completions
  .create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo' })
  .asResponse();
console.log(response.headers.get('X-My-Header'));
console.log(response.statusText); // access the underlying Response object

const { data: chatCompletion, response: raw } = await client.chat.completions
  .create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo' })
  .withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(chatCompletion);

Making custom/undocumented requests

This library is typed for convenient access to the documented API. If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

await client.post('/some/path', {
  body: { some_prop: 'foo' },
  query: { some_query_arg: 'bar' },
});

Undocumented request params

To make requests using undocumented parameters, you may use // @ts-expect-error on the undocumented parameter. This library doesn't validate at runtime that the request matches the type, so any extra values you send will be sent as-is.

client.foo.create({
  foo: 'my_param',
  bar: 12,
  // @ts-expect-error baz is not yet public
  baz: 'undocumented option',
});

For requests with the GET verb, any extra params will be in the query, all other requests will send the extra param in the body.

If you want to explicitly send an extra argument, you can do so with the query, body, and headers request options.

Undocumented response properties

To access undocumented response properties, you may access the response object with // @ts-expect-error on the response object, or cast the response object to the requisite type. Like the request params, we do not validate or strip extra properties from the response from the API.

Customizing the fetch client

By default, this library uses node-fetch in Node, and expects a global fetch function in other environments.

If you would prefer to use a global, web-standards-compliant fetch function even in a Node environment, (for example, if you are running Node with --experimental-fetch or using NextJS which polyfills with undici), add the following import before your first import from "OpenAI":

// Tell TypeScript and the package to use the global web fetch instead of node-fetch.
// Note, despite the name, this does not add any polyfills, but expects them to be provided if needed.
import 'openai/shims/web';
import OpenAI from 'openai';

To do the inverse, add import "openai/shims/node" (which does import polyfills). This can also be useful if you are getting the wrong TypeScript types for Response (more details).

Logging and middleware

You may also provide a custom fetch function when instantiating the client, which can be used to inspect or alter the Request or Response before/after each request:

import { fetch } from 'undici'; // as one example
import OpenAI from 'openai';

const client = new OpenAI({
  fetch: async (url: RequestInfo, init?: RequestInit): Promise<Response> => {
    console.log('About to make a request', url, init);
    const response = await fetch(url, init);
    console.log('Got response', response);
    return response;
  },
});

Note that if given a DEBUG=true environment variable, this library will log all requests and responses automatically. This is intended for debugging purposes only and may change in the future without notice.

Configuring an HTTP(S) Agent (e.g., for proxies)

By default, this library uses a stable agent for all http/https requests to reuse TCP connections, eliminating many TCP & TLS handshakes and shaving around 100ms off most requests.

If you would like to disable or customize this behavior, for example to use the API behind a proxy, you can pass an httpAgent which is used for all requests (be they http or https), for example:

import http from 'http';
import { HttpsProxyAgent } from 'https-proxy-agent';

// Configure the default for all requests:
const client = new OpenAI({
  httpAgent: new HttpsProxyAgent(process.env.PROXY_URL),
});

// Override per-request:
await client.models.list({
  httpAgent: new http.Agent({ keepAlive: false }),
});

Semantic versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

  1. Changes that only affect static types, without breaking runtime behavior.
  2. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals).
  3. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Requirements

TypeScript >= 4.5 is supported.

The following runtimes are supported:

  • Node.js 18 LTS or later (non-EOL) versions.

  • Deno v1.28.0 or higher, using import OpenAI from "npm:openai".

  • Bun 1.0 or later.

  • Cloudflare Workers.

  • Vercel Edge Runtime.

  • Jest 28 or greater with the "node" environment ("jsdom" is not supported at this time).

  • Nitro v2.6 or greater.

  • Web browsers: disabled by default to avoid exposing your secret API credentials. Enable browser support by explicitly setting dangerouslyAllowBrowser to true'.

    More explanation

    Why is this dangerous?

    Enabling the dangerouslyAllowBrowser option can be dangerous because it exposes your secret API credentials in the client-side code. Web browsers are inherently less secure than server environments, any user with access to the browser can potentially inspect, extract, and misuse these credentials. This could lead to unauthorized access using your credentials and potentially compromise sensitive data or functionality.

    When might this not be dangerous?

    In certain scenarios where enabling browser support might not pose significant risks:

    • Internal Tools: If the application is used solely within a controlled internal environment where the users are trusted, the risk of credential exposure can be mitigated.
    • Public APIs with Limited Scope: If your API has very limited scope and the exposed credentials do not grant access to sensitive data or critical operations, the potential impact of exposure is reduced.
    • Development or debugging purpose: Enabling this feature temporarily might be acceptable, provided the credentials are short-lived, aren't also used in production environments, or are frequently rotated.

Note that React Native is not supported at this time.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.

Contributing

See the contributing documentation.