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.
npm install openaideno add jsr:@openai/openai npx jsr add @openai/openaiThese commands will make the module importable from the @openai/openai scope. You can also import directly from JSR without an install step if you're using the Deno JavaScript runtime:
importOpenAIfrom'jsr:@openai/openai';The full API of this library can be found in api.md file along with many code examples.
The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.
importOpenAIfrom'openai';constclient=newOpenAI({apiKey: process.env['OPENAI_API_KEY'],// This is the default and can be omitted});constresponse=awaitclient.responses.create({model: 'gpt-4o',instructions: 'You are a coding assistant that talks like a pirate',input: 'Are semicolons optional in JavaScript?',});console.log(response.output_text);The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.
importOpenAIfrom'openai';constclient=newOpenAI({apiKey: process.env['OPENAI_API_KEY'],// This is the default and can be omitted});constcompletion=awaitclient.chat.completions.create({model: 'gpt-4o',messages: [{role: 'developer',content: 'Talk like a pirate.'},{role: 'user',content: 'Are semicolons optional in JavaScript?'},],});console.log(completion.choices[0].message.content);We provide support for streaming responses using Server Sent Events (SSE).
importOpenAIfrom'openai';constclient=newOpenAI();conststream=awaitclient.responses.create({model: 'gpt-4o',input: 'Say "Sheep sleep deep" ten times fast!',stream: true,});forawait(consteventofstream){console.log(event);}Request parameters that correspond to file uploads can be passed in many different forms:
File(or an object with the same structure)- a
fetchResponse(or an object with the same structure) - an
fs.ReadStream - the return value of our
toFilehelper
importfsfrom'fs';importfetchfrom'node-fetch';importOpenAI,{toFile}from'openai';constclient=newOpenAI();// If you have access to Node `fs` we recommend using `fs.createReadStream()`:awaitclient.files.create({file: fs.createReadStream('input.jsonl'),purpose: 'fine-tune'});// Or if you have the web `File` API you can pass a `File` instance:awaitclient.files.create({file: newFile(['my bytes'],'input.jsonl'),purpose: 'fine-tune'});// You can also pass a `fetch` `Response`:awaitclient.files.create({file: awaitfetch('https://somesite/input.jsonl'),purpose: 'fine-tune'});// Finally, if none of the above are convenient, you can use our `toFile` helper:awaitclient.files.create({file: awaittoFile(Buffer.from('my bytes'),'input.jsonl'),purpose: 'fine-tune',});awaitclient.files.create({file: awaittoFile(newUint8Array([0,1,2]),'input.jsonl'),purpose: 'fine-tune',});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:
asyncfunctionmain(){constjob=awaitclient.fineTuning.jobs.create({model: 'gpt-4o',training_file: 'file-abc123'}).catch(async(err)=>{if(errinstanceofOpenAI.APIError){console.log(err.request_id);console.log(err.status);// 400console.log(err.name);// BadRequestErrorconsole.log(err.headers);// {server: 'nginx', ...}}else{throwerr;}});}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 |
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:constclient=newOpenAI({maxRetries: 0,// default is 2});// Or, configure per-request:awaitclient.chat.completions.create({messages: [{role: 'user',content: 'How can I get the name of the current day in JavaScript?'}],model: 'gpt-4o'},{maxRetries: 5,});Requests time out after 10 minutes by default. You can configure this with a timeout option:
// Configure the default for all requests:constclient=newOpenAI({timeout: 20*1000,// 20 seconds (default is 10 minutes)});// Override per-request:awaitclient.chat.completions.create({messages: [{role: 'user',content: 'How can I list all files in a directory using Python?'}],model: 'gpt-4o'},{timeout: 5*1000,});On timeout, an APIConnectionTimeoutError is thrown.
Note that requests which time out will be retried twice by default.
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.
constresponse=awaitclient.responses.create({model: 'gpt-4o',input: 'testing 123'});console.log(response._request_id)// req_123You can also access the Request ID using the .withResponse() method:
const{data: stream, request_id }=awaitopenai.responses.create({model: 'gpt-4o',input: 'Say this is a test',stream: true,}).withResponse();List methods in the OpenAI API are paginated. You can use the for await … of syntax to iterate through items across all pages:
asyncfunctionfetchAllFineTuningJobs(params){constallFineTuningJobs=[];// Automatically fetches more pages as needed.forawait(constfineTuningJobofclient.fineTuning.jobs.list({limit: 20})){allFineTuningJobs.push(fineTuningJob);}returnallFineTuningJobs;}Alternatively, you can request a single page at a time:
letpage=awaitclient.fineTuning.jobs.list({limit: 20});for(constfineTuningJobofpage.data){console.log(fineTuningJob);}// Convenience methods are provided for manually paginating:while(page.hasNextPage()){page=awaitpage.getNextPage();// ...}The Realtime API enables you to build low-latency, multi-modal conversational experiences. It currently supports text and audio as both input and output, as well as function calling through a WebSocket connection.
import{OpenAIRealtimeWebSocket}from'openai/beta/realtime/websocket';constrt=newOpenAIRealtimeWebSocket({model: 'gpt-4o-realtime-preview-2024-12-17'});rt.on('response.text.delta',(event)=>process.stdout.write(event.delta));For more information see realtime.md.
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';constcredential=newDefaultAzureCredential();constscope='https://cognitiveservices.azure.com/.default';constazureADTokenProvider=getBearerTokenProvider(credential,scope);constopenai=newAzureOpenAI({ azureADTokenProvider,apiVersion: "<The API version, e.g. 2024-10-01-preview>"});constresult=awaitopenai.chat.completions.create({model: 'gpt-4o',messages: [{role: 'user',content: 'Say hello!'}],});console.log(result.choices[0]!.message?.content);For more information on support for the Azure API, see azure.md.
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.
constclient=newOpenAI();consthttpResponse=awaitclient.responses.create({model: 'gpt-4o',input: 'say this is a test.'}).asResponse();// access the underlying web standard Response objectconsole.log(httpResponse.headers.get('X-My-Header'));console.log(httpResponse.statusText);const{data: modelResponse,response: raw}=awaitclient.responses.create({model: 'gpt-4o',input: 'say this is a test.'}).withResponse();console.log(raw.headers.get('X-My-Header'));console.log(modelResponse);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.
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.
awaitclient.post('/some/path',{body: {some_prop: 'foo'},query: {some_query_arg: 'bar'},});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 publicbaz: '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.
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.
We're actively working on a new alpha version that migrates from
node-fetchto builtin fetch.Please try it out and let us know if you run into any issues! https://community.openai.com/t/your-feedback-requested-node-js-sdk-5-0-0-alpha/1063774
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';importOpenAIfrom'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).
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 exampleimportOpenAIfrom'openai';constclient=newOpenAI({fetch: async(url: RequestInfo,init?: RequestInit): Promise<Response>=>{console.log('About to make a request',url,init);constresponse=awaitfetch(url,init);console.log('Got response',response);returnresponse;},});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.
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:
importhttpfrom'http';import{HttpsProxyAgent}from'https-proxy-agent';// Configure the default for all requests:constclient=newOpenAI({httpAgent: newHttpsProxyAgent(process.env.PROXY_URL),});// Override per-request:awaitclient.models.list({httpAgent: newhttp.Agent({keepAlive: false}),});This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:
- Changes that only affect static types, without breaking runtime behavior.
- 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.)
- 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.
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.
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
dangerouslyAllowBrowserto true'.More explanation
Enabling the
dangerouslyAllowBrowseroption 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.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.
