OpenAI

---

This repository contains Swift community-maintained implementation over OpenAI public API.

• Installation
  • Swift Package Manager
• Usage
  • Initialization
  • Using the SDK for other providers except OpenAI
  • Cancelling requests
• Text and prompting
  • Responses
  • Chat Completions
• Function calling
• Tools
  • MCP (Model Context Protocol)
    • MCP Tool Integration
• Images
  • Create Image
  • Create Image Edit
  • Create Image Variation
• Audio
  • Audio Create Speech
  • Audio Transcriptions
  • Audio Translations
• Structured Outputs
• Specialized models
  • Embeddings
  • Moderations
• Assistants (Beta)
  • Create Assistant
  • Modify Assistant
  • List Assistants
  • Threads
    • Create Thread
    • Create and Run Thread
    • Get Threads Messages
    • Add Message to Thread
  • Runs
    • Create Run
    • Retrieve Run
    • Retrieve Run Steps
    • Submit Tool Outputs for Run
  • Files
    • Upload File
• Other APIs
  • Models
    • List Models
    • Retrieve Model
  • Utilities
• Support for other providers: Gemini, DeepSeek, Perplexity, OpenRouter, etc.
• Example Project
• Contribution Guidelines
• Links
• License

Documentation

This library implements it's types and methods in close accordance to the REST API documentation, which can be found on platform.openai.com.

Installation

Swift Package Manager

To integrate OpenAI into your Xcode project using Swift Package Manager:

1. In Xcode, go to File > Add Package Dependencies...
2. Enter the repository URL: https://github.com/MacPaw/OpenAI.git
3. Choose your desired dependency rule (e.g., "Up to Next Major Version").

Alternatively, you can add it directly to your Package.swift file:

dependencies:[.package(url:"https://github.com/MacPaw/OpenAI.git", branch:"main")]

Usage

Initialization

To initialize API instance you need to obtain API token from your Open AI organization.

Remember that your API key is a secret! Do not share it with others or expose it in any client-side code (browsers, apps). Production requests must be routed through your own backend server where your API key can be securely loaded from an environment variable or key management service.

Once you have a token, you can initialize OpenAI class, which is an entry point to the API.

⚠️ OpenAI strongly recommends developers of client-side applications proxy requests through a separate backend service to keep their API key safe. API keys can access and manipulate customer billing, usage, and organizational data, so it's a significant risk to expose them.

letopenAI=OpenAI(apiToken:"YOUR_TOKEN_HERE")

Optionally you can initialize OpenAI with token, organization identifier and timeoutInterval.

letconfiguration=OpenAI.Configuration(token:"YOUR_TOKEN_HERE", organizationIdentifier:"YOUR_ORGANIZATION_ID_HERE", timeoutInterval:60.0)letopenAI=OpenAI(configuration: configuration)

See OpenAI.Configuration for more values that can be passed on init for customization, like: host, basePath, port, scheme and customHeaders.

Once you posses the token, and the instance is initialized you are ready to make requests.

Using the SDK for other providers except OpenAI

This SDK is more focused on working with OpenAI Platform, but also works with other providers that support OpenAI-compatible API.

Use .relaxed parsing option on Configuration, or see more details on the topic here

Cancelling requests

For Swift Concurrency calls, you can simply cancel the calling task, and corresponding underlying URLSessionDataTask would get cancelled automatically.

lettask=Task{do{letchatResult=tryawait openAIClient.chats(query:.init(messages:[], model:"asd"))}catch{
// Handle cancellation or error
}}
task.cancel()

Cancelling closure-based API calls

When you call any of the closure-based API methods, it returns discardable CancellableRequest. Hold a reference to it to be able to cancel the request later.

letcancellableRequest= object.chats(query: query, completion:{ _ in})
cancellableReques

Cancelling Combine subscriptions

In Combine, use a default cancellation mechanism. Just discard the reference to a subscription, or call `cancel()` on it.

letsubscription= openAIClient
.images(query: query).sink(receiveCompletion:{ completion in}, receiveValue:{ imagesResult in})
subscription.cancel()

Text and prompting

Responses

Use responses variable on OpenAIProtocol to call Responses API methods.

publicprotocolOpenAIProtocol{
// ...
varresponses:ResponsesEndpointProtocol{get}
// ...
}

Specify params by passing CreateModelResponseQuery to a method. Get ResponseObject or a stream of ResponseStreamEvent events in response.

Example: Generate text from a simple prompt

letclient:OpenAIProtocol= /* client initialization code */
let query =CreateModelResponseQuery(
input:.textInput("Write a one-sentence bedtime story about a unicorn."),
model:.gpt4_1
)letresponse:ResponseObject=tryawait client.responses.createResponse(query: query)
// ...

print(response)

ResponseObject(
createdAt: 1752146109,
error: nil,
id: "resp_686fa0bd8f588198affbbf5a8089e2d208a5f6e2111e31f5",
incompleteDetails: nil,
instructions: nil,
maxOutputTokens: nil,
metadata: [:],
model: "gpt-4.1-2025-04-14",
object: "response",
output: [
OpenAI.OutputItem.outputMessage(
OpenAI.Components.Schemas.OutputMessage(
id: "msg_686fa0bee24881988a4d1588d7f65c0408a5f6e2111e31f5",
_type: OpenAI.Components.Schemas.OutputMessage._TypePayload.message,
role: OpenAI.Components.Schemas.OutputMessage.RolePayload.assistant,
content: [
OpenAI.Components.Schemas.OutputContent.OutputTextContent(
OpenAI.Components.Schemas.OutputTextContent(
_type: OpenAI.Components.Schemas.OutputTextContent._TypePayload.outputText,
text: "Under a sky full of twinkling stars, a gentle unicorn named Luna danced through fields of stardust, spreading sweet dreams to every sleeping child.",
annotations: [],
logprobs: Optional([])
)
)
],
status: OpenAI.Components.Schemas.OutputMessage.StatusPayload.completed
)
)
],
parallelToolCalls: true,
previousResponseId: nil,
reasoning: Optional(
OpenAI.Components.Schemas.Reasoning(
effort: nil,
summary: nil,
generateSummary: nil
)
),
status: "completed",
temperature: Optional(1.0),
text: OpenAI.Components.Schemas.ResponseProperties.TextPayload(
format: Optional(
OpenAI.Components.Schemas.TextResponseFormatConfiguration.ResponseFormatText(
OpenAI.Components.Schemas.ResponseFormatText(
_type: OpenAI.Components.Schemas.ResponseFormatText._TypePayload.text
)
)
),
toolChoice: OpenAI.Components.Schemas.ResponseProperties.ToolChoicePayload.ToolChoiceOptions(
OpenAI.Components.Schemas.ToolChoiceOptions.auto
),
tools: [],
topP: Optional(1.0),
truncation: Optional("disabled"),
usage: Optional(
OpenAI.Components.Schemas.ResponseUsage(
inputTokens: 18,
inputTokensDetails: OpenAI.Components.Schemas.ResponseUsage.InputTokensDetailsPayload(
cachedTokens: 0
),
outputTokens: 32,
outputTokensDetails: OpenAI.Components.Schemas.ResponseUsage.OutputTokensDetailsPayload(
reasoningTokens: 0
),
totalTokens: 50
)
),
user: nil
)
)

An array of content generated by the model is in the output property of the response.

[!NOTE] The output array often has more than one item in it! It can contain tool calls, data about reasoning tokens generated by reasoning models, and other items. It is not safe to assume that the model's text output is present at output[0].content[0].text.

Because of the note above, to safely and fully read the response, we'd need to switch both over messages and their contents, like this:

// ...
foroutputin response.output {switch output {case.outputMessage(let outputMessage):forcontentin outputMessage.content {switch content {case.OutputTextContent(let textContent):print(textContent.text)case.RefusalContent(let refusalContent):print(refusalContent.refusal)}}default:
// Unhandled output items. Handle or throw an error.
}}

Chat Completions

Use ChatQuery with func chats(query:) and func chatsStream(query:) methods on OpenAIProtocol to generate text using Chat Completions API. Get ChatResult or ChatStreamResult in response.

Example: Generate text from a simple prompt

letquery=ChatQuery(
messages:[.user(.init(content:.string("Who are you?")))],
model:.gpt4_o
)letresult=tryawait openAI.chats(query: query)print(result.choices.first?.message.content ??"")
// printed to console:
// I'm an AI language model created by OpenAI, designed to assist with a wide range of questions and tasks. How can I help you today?

po result

(lldb) po result
▿ ChatResult
- id : "chatcmpl-BgWJTzbVczdJDusTqVpnR6AQ2w6Fd"
- created : 1749473687
- model : "gpt-4o-2024-08-06"
- object : "chat.completion"
▿ serviceTier : Optional<ServiceTier>
- some : OpenAI.ServiceTier.defaultTier
▿ systemFingerprint : Optional<String>
- some : "fp_07871e2ad8"
▿ choices : 1 element
▿ 0 : Choice
- index : 0
- logprobs : nil
▿ message : Message
▿ content : Optional<String>
- some : "I am an AI language model created by OpenAI, known as ChatGPT. I\'m here to assist with answering questions, providing explanations, and engaging in conversation on a wide range of topics. If you have any questions or need assistance, feel free to ask!"
- refusal : nil
- role : "assistant"
▿ annotations : Optional<Array<Annotation>>
- some : 0 elements
- audio : nil
- toolCalls : nil
- _reasoning : nil
- _reasoningContent : nil
- finishReason : "stop"
▿ usage : Optional<CompletionUsage>
▿ some : CompletionUsage
- completionTokens : 52
- promptTokens : 11
- totalTokens : 63
▿ promptTokensDetails : Optional<PromptTokensDetails>
▿ some : PromptTokensDetails
- audioTokens : 0
- cachedTokens : 0
- citations : nil

Function calling

See OpenAI Platform Guide: Function calling for more details.

Chat Completions API Examples

Function calling with get_weather function

letopenAI=OpenAI(apiToken:"...")
// Declare functions which model might decide to call.
letfunctions=[ChatQuery.ChatCompletionToolParam.FunctionDefinition(
name:"get_weather",
description:"Get current temperature for a given location.",
parameters:.init(fields:[.type(.object),.properties(["location":.init(fields:[.type(.string),.description("City and country e.g. Bogotá, Colombia")])]),.required(["location"]),.additionalProperties(.boolean(false))]))]letquery= ChatQuery(
messages:[.user(.init(content:.string("What is the weather like in Paris today?"],
model:.gpt4_1,
tools: functions.map{.init(function: $0)})let result =tryawait openAI.chats(query: query)
print(result.choices[0].message.toolCalls)

Result will be (serialized as JSON here for readability):

{
"id": "chatcmpl-1234",
"object": "chat.completion",
"created": 1686000000,
"model": "gpt-3.5-turbo-0613",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"tool_calls": [
{
"id": "call-0",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\n\"location\": \"Boston, MA\"\n}"
}
}
]
},
"finish_reason": "function_call"
}
],
"usage": { "total_tokens": 100, "completion_tokens": 18, "prompt_tokens": 82 }
}

Images

Given a prompt and/or an input image, the model will generate a new image.

As Artificial Intelligence continues to develop, so too does the intriguing concept of Dall-E. Developed by OpenAI, a research lab for artificial intelligence purposes, Dall-E has been classified as an AI system that can generate images based on descriptions provided by humans. With its potential applications spanning from animation and illustration to design and engineering - not to mention the endless possibilities in between - it's easy to see why there is such excitement over this new technology.

Create Image

Request

structImagesQuery:Codable{
/// A text description of the desired image(s). The maximum length is 1000 characters.
publicletprompt:String
/// The number of images to generate. Must be between 1 and 10.
publicletn:Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
publicletsize:String?}

Response

structImagesResult:Codable,Equatable{publicstructURLResult:Codable,Equatable{publicleturl:String}publicletcreated:TimeIntervalpublicletdata:[URLResult]}

Example

letquery=ImagesQuery(prompt:"White cat with heterochromia sitting on the kitchen table", n:1, size:"1024x1024")
openAI.images(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.images(query: query)

(lldb) po result
▿ ImagesResult
- created : 1671453505.0
▿ data : 1 element
▿ 0 : URLResult
- url : "https://oaidalleapiprodscus.blob.core.windows.net/private/org-CWjU5cDIzgCcVjq10pp5yX5Q/user-GoBXgChvLBqLHdBiMJBUbPqF/img-WZVUK2dOD4HKbKwW1NeMJHBd.png?st=2022-12-19T11%3A38%3A25Z&se=2022-12-19T13%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2022-12-19T09%3A35%3A16Z&ske=2022-12-20T09%3A35%3A16Z&sks=b&skv=2021-08-06&sig=mh52rmtbQ8CXArv5bMaU6lhgZHFBZz/ePr4y%2BJwLKOc%3D"

Generated image

Create Image Edit

Creates an edited or extended image given an original image and a prompt.

Request

publicstructImageEditsQuery:Codable{
/// The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
publicletimage:DatapublicletfileName:String
/// An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.
publicletmask:Data?publicletmaskFileName:String?
/// A text description of the desired image(s). The maximum length is 1000 characters.
publicletprompt:String
/// The number of images to generate. Must be between 1 and 10.
publicletn:Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
publicletsize:String?}

Response

Uses the ImagesResult response similarly to ImagesQuery.

Example

letdata= image.pngData()letquery=ImageEditQuery(image: data, fileName:"whitecat.png", prompt:"White cat with heterochromia sitting on the kitchen table with a bowl of food", n:1, size:"1024x1024")
openAI.imageEdits(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.imageEdits(query: query)

Create Image Variation

Creates a variation of a given image.

Request

publicstructImageVariationsQuery:Codable{
/// The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
publicletimage:DatapublicletfileName:String
/// The number of images to generate. Must be between 1 and 10.
publicletn:Int?
/// The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024.
publicletsize:String?}

Response

Uses the ImagesResult response similarly to ImagesQuery.

Example

letdata= image.pngData()letquery=ImageVariationQuery(image: data, fileName:"whitecat.png", n:1, size:"1024x1024")
openAI.imageVariations(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.imageVariations(query: query)

Review Images Documentation for more info.

Audio

The speech to text API provides two endpoints, transcriptions and translations, based on our state-of-the-art open source large-v2 Whisper model. They can be used to:

Transcribe audio into whatever language the audio is in. Translate and transcribe the audio into english. File uploads are currently limited to 25 MB and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm.

Audio Create Speech

This function sends an AudioSpeechQuery to the OpenAI API to create audio speech from text using a specific voice and format.

Learn more about voices.
Learn more about models.

Request:

publicstructAudioSpeechQuery:Codable,Equatable{
//...
publicletmodel:Model // tts-1 or tts-1-hd publicletinput:Stringpublicletvoice:AudioSpeechVoicepublicletresponseFormat:AudioSpeechResponseFormatpublicletspeed:String? // Initializes with Double?
//...
}

Response:

/// Audio data for one of the following formats :`mp3`, `opus`, `aac`, `flac`, `pcm`
publicletaudioData:Data?

Example:

letquery=AudioSpeechQuery(model:.tts_1, input:"Hello, world!", voice:.alloy, responseFormat:.mp3, speed:1.0)
openAI.audioCreateSpeech(query: query){ result in
// Handle response here
}
//or
letresult=tryawait openAI.audioCreateSpeech(query: query)

OpenAI Create Speech – Documentation

Audio Create Speech Streaming

Audio Create Speech is available by using audioCreateSpeechStream function. Tokens will be sent one-by-one.

Closures

openAI.audioCreateSpeechStream(query: query){ partialResult inswitch partialResult {case.success(let result):print(result.audio)case.failure(let error):
//Handle chunk error here
}} completion:{ error in
//Handle streaming error here
}

Combine

openAI
.audioCreateSpeechStream(query: query).sink{ completion in
//Handle completion result here
} receiveValue:{ result in
//Handle chunk here
}.store(in:&cancellables)

Structured concurrency

fortryawaitresultin openAI.audioCreateSpeechStream(query: query){
//Handle result here
}

Audio Transcriptions

Transcribes audio into the input language.

Request

publicstructAudioTranscriptionQuery:Codable,Equatable{publicletfile:DatapublicletfileName:Stringpublicletmodel:Modelpublicletprompt:String?publiclettemperature:Double?publicletlanguage:String?}

Response

publicstructAudioTranscriptionResult:Codable,Equatable{publiclettext:String}

Example

letdata=Data(contentsOfURL:...)letquery=AudioTranscriptionQuery(file: data, fileName:"audio.m4a", model:.whisper_1) openAI.audioTranscriptions(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.audioTranscriptions(query: query)

Audio Translations

Translates audio into into English.

Request

publicstructAudioTranslationQuery:Codable,Equatable{publicletfile:DatapublicletfileName:Stringpublicletmodel:Modelpublicletprompt:String?publiclettemperature:Double?}

Response

publicstructAudioTranslationResult:Codable,Equatable{publiclettext:String}

Example

letdata=Data(contentsOfURL:...)letquery=AudioTranslationQuery(file: data, fileName:"audio.m4a", model:.whisper_1) openAI.audioTranslations(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.audioTranslations(query: query)

Review Audio Documentation for more info.

Structured Outputs

[!NOTE] This section focuses on non-function calling use cases in the Responses and Chat Completions APIs. To learn more about how to use Structured Outputs with function calling, check out the Function Calling.

To configure structured outputs you would define a JSON Schema and pass it to a query.

This SDK supports multiple ways to define a schema; choose the one you prefer.

JSONSchemaDefinition.jsonSchema

Build a schema by specifying fields

This definition accepts JSONSchema which is either boolean or object JSON Document.

Instead of providing schema yourself you can build one in a type-safe manner using initializers that accept [JSONSchemaField], as shown in the example below.

While this method of defining a schema is direct, it can be verbose. For alternative ways to define a schema, see the options below.

Example

letquery=CreateModelResponseQuery(
input:.textInput("Return structured output"),
model:.gpt4_o,
text:.jsonSchema(.init(
name:"research_paper_extraction",
schema:.jsonSchema(.init(.type(.object),.properties(["title":Schema.buildBlock(.type(.string)),"authors":.init(.type(.array),.items(.init(.type(.string)))),"abstract":.init(.type(.string)),"keywords":.init(.type(.array),.items(.init(.type(.string))))]),.required(["title, authors, abstract, keywords"]),.additionalProperties(.boolean(false)))),
description:"desc",
strict:false)))letresponse=tryawait openAIClient.responses.createResponse(query: query)foroutputin response.output {switch output {case.outputMessage(let message):forcontentin message.content {switch content {case.OutputTextContent(let textContent):print("json output structured by the schema: ", textContent.text)case.RefusalContent(let refusal):
// Handle refusal
break}}default:
// Handle other OutputItems
break}}

JSONSchemaDefinition.derivedJsonSchema

Implement a type that describes a schema

Use Pydantic or Zod fashion to define schemas.

• Use the derivedJsonSchema(_ type:) response format when creating a ChatQuery or CreateModelResponseQuery
• Provide a type that conforms to JSONSchemaConvertible and generates an instance as an example
• Make sure all enum types within the provided type conform to JSONSchemaEnumConvertible and generate an array of names for all cases

Example

structMovieInfo:JSONSchemaConvertible{lettitle:Stringletdirector:Stringletrelease:Dateletgenres:[MovieGenre]letcast:[String]staticletexample:Self={.init(
title:"Earth",
director:"Alexander Dovzhenko",
release:Calendar.current.date(from:DateComponents(year:1930, month:4, day:1))!,
genres:[.drama],
cast:["Stepan Shkurat","Semyon Svashenko","Yuliya Solntseva"])}()}enumMovieGenre:String,Codable,JSONSchemaEnumConvertible{case action, drama, comedy, scifi
varcaseNames:[String]{Self.allCases.map{ $0.rawValue }}}letquery=ChatQuery(
messages:[.system(.init(content:.textContent("Best Picture winner at the 2011 Oscars")))],
model:.gpt4_o,
responseFormat:.jsonSchema(.init(
name:"movie-info",
description:nil,
schema:.derivedJsonSchema(MovieInfo.self),
strict:true)))letresult=tryawait openAI.chats(query: query)

JSONSchemaDefinition.dynamicJsonSchema

Define a schema with an instance of any type that conforms to Encodable

Define your JSON schema using simple Dictionaries, or specify JSON schema with a library like https://github.com/kevinhermawan/swift-json-schema.

Example

structAnyEncodable:Encodable{privatelet_encode:(Encoder)throws->Voidpublicinit<T:Encodable>(_ wrapped:T){
_encode = wrapped.encode
}func encode(to encoder:Encoder)throws{try_encode(encoder)}}letschema=["type":AnyEncodable("object"),"properties":AnyEncodable(["title":AnyEncodable(["type":"string"]),"director":AnyEncodable(["type":"string"]),"release":AnyEncodable(["type":"string"]),"genres":AnyEncodable(["type":AnyEncodable("array"),"items":AnyEncodable(["type":AnyEncodable("string"),"enum":AnyEncodable(["action","drama","comedy","scifi"])])]),"cast":AnyEncodable(["type":AnyEncodable("array"),"items":AnyEncodable(["type":"string"])])]),"additionalProperties":AnyEncodable(false)]letquery=ChatQuery(
messages:[.system(.init(content:.textContent("Return a structured response.")))],
model:.gpt4_o,
responseFormat:.jsonSchema(.init(name:"movie-info", schema:.dynamicJsonSchema(schema))))letresult=tryawait openAI.chats(query: query)

Review Structured Output Documentation for more info.

Tools

Remote MCP (Model Context Protocol)

The Model Context Protocol (MCP) enables AI models to securely connect to external data sources and tools through standardized server connections. This OpenAI Swift library supports MCP integration, allowing you to extend model capabilities with remote tools and services.

You can use the MCP Swift library to connect to MCP servers and discover available tools, then integrate those tools with OpenAI's chat completions.

MCP Tool Integration

Request

// Create an MCP tool for connecting to a remote server
letmcpTool=Tool.mcpTool(.init(
_type:.mcp,
serverLabel:"GitHub_MCP_Server",
serverUrl:"https://api.githubcopilot.com/mcp/",
headers:.init(additionalProperties:["Authorization":"Bearer YOUR_TOKEN_HERE"]),
allowedTools:.case1(["search_repositories","get_file_contents"]),
requireApproval:.case2(.always)))letquery=ChatQuery(
messages:[.user(.init(content:.string("Search for Swift repositories on GitHub")))],
model:.gpt4_o,
tools:[mcpTool])

MCP Tool Properties

• serverLabel: A unique identifier for the MCP server
• serverUrl: The URL endpoint of the MCP server
• headers: Authentication headers and other HTTP headers required by the server
• allowedTools: Specific tools to enable from the server (optional - if not specified, all tools are available)
• requireApproval: Whether tool calls require user approval (.always, .never, or conditional)

Example with MCP Swift Library

import MCP
import OpenAI
// Connect to MCP server using the MCP Swift library
letmcpClient=MCP.Client(name:"MyApp", version:"1.0.0")lettransport=HTTPClientTransport(
endpoint:URL(string:"https://api.githubcopilot.com/mcp/")!,
configuration:URLSessionConfiguration.default
)letresult=tryawait mcpClient.connect(transport: transport)lettoolsResponse=tryawait mcpClient.listTools()
// Create OpenAI MCP tool with discovered tools
letenabledToolNames= toolsResponse.tools.map{ $0.name }letmcpTool=Tool.mcpTool(.init(
_type:.mcp,
serverLabel:"GitHub_MCP_Server",
serverUrl:"https://api.githubcopilot.com/mcp/",
headers:.init(additionalProperties: authHeaders),
allowedTools:.case1(enabledToolNames),
requireApproval:.case2(.always)))
// Use in chat completion
letquery=ChatQuery(
messages:[.user(.init(content:.string("Help me search GitHub repositories")))],
model:.gpt4_o,
tools:[mcpTool])letchatResult=tryawait openAI.chats(query: query)

MCP Tool Call Handling

When using MCP tools, the model may generate tool calls that are executed on the remote MCP server. Handle MCP-specific output items in your response processing:

// Handle MCP tool calls in streaming responses
fortryawaitresultin openAI.chatsStream(query: query){forchoicein result.choices {iflet outputItem = choice.delta.content {switch outputItem {case.mcpToolCall(let mcpCall):print("MCP tool call: \(mcpCall.name)")iflet output = mcpCall.output {print("Result: \(output)")}case.mcpApprovalRequest(let approvalRequest):
// Handle approval request if requireApproval is enabled
print("MCP tool requires approval: \(approvalRequest)")default:
// Handle other output types
break}}}}

Specialized models

Embeddings

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

Request

structEmbeddingsQuery:Codable{
/// ID of the model to use.
publicletmodel:Model
/// Input text to get embeddings for
publicletinput:String}

Response

structEmbeddingsResult:Codable,Equatable{publicstructEmbedding:Codable,Equatable{publicletobject:Stringpublicletembedding:[Double]publicletindex:Int}publicletdata:[Embedding]publicletusage:Usage}

Example

letquery=EmbeddingsQuery(model:.textSearchBabbageDoc, input:"The food was delicious and the waiter...")
openAI.embeddings(query: query){ result in
//Handle response here
}
//or
letresult=tryawait openAI.embeddings(query: query)

(lldb) po result
▿ EmbeddingsResult
▿ data : 1 element
▿ 0 : Embedding
- object : "embedding"
▿ embedding : 2048 elements
- 0 : 0.0010535449
- 1 : 0.024234328
- 2 : -0.0084999
- 3 : 0.008647452
.......
- 2044 : 0.017536353
- 2045 : -0.005897616
- 2046 : -0.026559394
- 2047 : -0.016633155
- index : 0
(lldb)

Review Embeddings Documentation for more info.

Moderations

Given a input text, outputs if the model classifies it as violating OpenAI's content policy.

Request

publicstructModerationsQuery:Codable{publicletinput:Stringpublicletmodel:Model?}

Response

publicstructModerationsResult:Codable,Equatable{publicletid:Stringpublicletmodel:Modelpublicletresults:[CategoryResult]}

Example

letquery=ModerationsQuery(input:"I want to kill them.")
openAI.moderations(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.moderations(query: query)

Review Moderations Documentation for more info.

Other APIs

Models

Models are represented as a typealias typealias Model = String.

publicextensionModel{staticletgpt5_1="gpt-5.1"staticletgpt5_1_chat_latest="gpt-5.1-chat-latest"staticletgpt5="gpt-5"staticletgpt5_mini="gpt-5-mini"staticletgpt5_nano="gpt-5-nano"staticletgpt5_chat="gpt-5-chat"staticletgpt4_1="gpt-4.1"staticletgpt4_1_mini="gpt-4.1-mini"staticletgpt4_1_nano="gpt-4.1-nano"staticletgpt4_turbo_preview="gpt-4-turbo-preview"staticletgpt4_vision_preview="gpt-4-vision-preview"staticletgpt4_0125_preview="gpt-4-0125-preview"staticletgpt4_1106_preview="gpt-4-1106-preview"staticletgpt4="gpt-4"staticletgpt4_0613="gpt-4-0613"staticletgpt4_0314="gpt-4-0314"staticletgpt4_32k="gpt-4-32k"staticletgpt4_32k_0613="gpt-4-32k-0613"staticletgpt4_32k_0314="gpt-4-32k-0314"staticletgpt3_5Turbo="gpt-3.5-turbo"staticletgpt3_5Turbo_0125="gpt-3.5-turbo-0125"staticletgpt3_5Turbo_1106="gpt-3.5-turbo-1106"staticletgpt3_5Turbo_0613="gpt-3.5-turbo-0613"staticletgpt3_5Turbo_0301="gpt-3.5-turbo-0301"staticletgpt3_5Turbo_16k="gpt-3.5-turbo-16k"staticletgpt3_5Turbo_16k_0613="gpt-3.5-turbo-16k-0613"staticlettextDavinci_003="text-davinci-003"staticlettextDavinci_002="text-davinci-002"staticlettextCurie="text-curie-001"staticlettextBabbage="text-babbage-001"staticlettextAda="text-ada-001"staticlettextDavinci_001="text-davinci-001"staticletcodeDavinciEdit_001="code-davinci-edit-001"staticlettts_1="tts-1"staticlettts_1_hd="tts-1-hd"staticletwhisper_1="whisper-1"staticletdall_e_2="dall-e-2"staticletdall_e_3="dall-e-3"staticletdavinci="davinci"staticletcurie="curie"staticletbabbage="babbage"staticletada="ada"staticlettextEmbeddingAda="text-embedding-ada-002"staticlettextSearchAda="text-search-ada-doc-001"staticlettextSearchBabbageDoc="text-search-babbage-doc-001"staticlettextSearchBabbageQuery001="text-search-babbage-query-001"staticlettextEmbedding3="text-embedding-3-small"staticlettextEmbedding3Large="text-embedding-3-large"staticlettextModerationStable="text-moderation-stable"staticlettextModerationLatest="text-moderation-latest"staticletmoderation="text-moderation-007"}

GPT-4 models are supported.

As an example: To use the gpt-4-turbo-preview model, pass .gpt4_turbo_preview as the parameter to the ChatQuery init.

letquery=ChatQuery(model:.gpt4_turbo_preview, messages:[.init(role:.system, content:"You are Librarian-GPT. You know everything about the books."),.init(role:.user, content:"Who wrote Harry Potter?")])letresult=tryawait openAI.chats(query: query)XCTAssertFalse(result.choices.isEmpty)

You can also pass a custom string if you need to use some model, that is not represented above.

List Models

Lists the currently available models.

Response

publicstructModelsResult:Codable,Equatable{publicletdata:[ModelResult]publicletobject:String}

Example

openAI.models(){ result in
//Handle result here
}
//or
letresult=tryawait openAI.models()

Retrieve Model

Retrieves a model instance, providing ownership information.

Request

publicstructModelQuery:Codable,Equatable{publicletmodel:Model}

Response

publicstructModelResult:Codable,Equatable{publicletid:Modelpublicletobject:StringpublicletownedBy:String}

Example

letquery=ModelQuery(model:.gpt4)
openAI.model(query: query){ result in
//Handle result here
}
//or
letresult=tryawait openAI.model(query: query)

Review Models Documentation for more info.

Utilities

The component comes with several handy utility functions to work with the vectors.

publicstructVector{
/// Returns the similarity between two vectors
///
/// - Parameters:
/// - a: The first vector
/// - b: The second vector
publicstaticfunc cosineSimilarity(a:[Double], b:[Double])->Double{returndot(a, b)/(mag(a)* mag(b))}
/// Returns the difference between two vectors. Cosine distance is defined as `1 - cosineSimilarity(a, b)`
///
/// - Parameters:
/// - a: The first vector
/// - b: The second vector
publicfunc cosineDifference(a:[Double], b:[Double])->Double{return1- Self.cosineSimilarity(a: a, b: b)}}

Example

letvector1=[0.213123,0.3214124,0.421412,0.3214521251,0.412412,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.4214214,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251]letvector2=[0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.511515,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3214521251,0.213123,0.3214124,0.1414124,0.3213213]letsimilarity=Vector.cosineSimilarity(a: vector1, b: vector2)print(similarity) //0.9510201910206734

In data analysis, cosine similarity is a measure of similarity between two sequences of numbers.

Read more about Cosine Similarity here.

Assistants

Review Assistants Documentation for more info.

Create Assistant

Example: Create Assistant

letquery=AssistantsQuery(model:Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)
openAI.assistantCreate(query: query){ result in
//Handle response here
}

Modify Assistant

Example: Modify Assistant

letquery=AssistantsQuery(model:Model.gpt4_o_mini, name: name, description: description, instructions: instructions, tools: tools, toolResources: toolResources)
openAI.assistantModify(query: query, assistantId:"asst_1234"){ result in
//Handle response here
}

List Assistants

Example: List Assistants

openAI.assistants(){ result in
//Handle response here
}

Threads

Review Threads Documentation for more info.

Create Thread

Example: Create Thread

letthreadsQuery=ThreadsQuery(messages:[Chat(role: message.role, content: message.content)])
openAI.threads(query: threadsQuery){ result in
//Handle response here
}

Create and Run Thread

Example: Create and Run Thread

letthreadsQuery=ThreadQuery(messages:[Chat(role: message.role, content: message.content)])letthreadRunQuery=ThreadRunQuery(assistantId:"asst_1234" thread: threadsQuery)
openAI.threadRun(query: threadRunQuery){ result in
//Handle response here
}

Get Threads Messages

Review Messages Documentation for more info.

Example: Get Threads Messages

openAI.threadsMessages(threadId: currentThreadId){ result in
//Handle response here
}

Add Message to Thread

Example: Add Message to Thread

letquery=MessageQuery(role: message.role.rawValue, content: message.content)
openAI.threadsAddMessage(threadId: currentThreadId, query: query){ result in
//Handle response here
}

Runs

Review Runs Documentation for more info.

Create Run

Example: Create Run

letrunsQuery=RunsQuery(assistantId: currentAssistantId)
openAI.runs(threadId: threadsResult.id, query: runsQuery){ result in
//Handle response here
}

Retrieve Run

Example: Retrieve Run

openAI.runRetrieve(threadId: currentThreadId, runId: currentRunId){ result in
//Handle response here
}

Retrieve Run Steps

Example: Retrieve Run Steps

openAI.runRetrieveSteps(threadId: currentThreadId, runId: currentRunId){ result in
//Handle response here
}

Submit Tool Outputs for Run

Example: Submit Tool Outputs for Run

letoutput=RunToolOutputsQuery.ToolOutput(toolCallId:"call123", output:"Success")letquery=RunToolOutputsQuery(toolOutputs:[output])
openAI.runSubmitToolOutputs(threadId: currentThreadId, runId: currentRunId, query: query){ result in
//Handle response here
}

Files

Review Files Documentation for more info.

Upload file

Example: Upload file

letquery=FilesQuery(purpose:"assistants", file: fileData, fileName: url.lastPathComponent, contentType:"application/pdf")
openAI.files(query: query){ result in
//Handle response here
}

Support for other providers

TL;DR Use .relaxed parsing option on Configuration

This SDK has a limited support for other providers like Gemini, Perplexity etc.

The top priority of this SDK is OpenAI, and the main rule is for all the main types to be fully compatible with OpenAI's API Reference. If it says a field should be optional, it must be optional in main subset of Query/Result types of this SDK. The same goes for other info declared in the reference, like default values.

That said we still want to give a support for other providers.

Option 1: Use .relaxed parsing option

.relaxed parsing option handles both missing and additional key/values in responses. It should be sufficient for most use-cases. Let us know if it doesn't cover any case you need.

Option 2: Specify parsing options separately

Handle missing keys in responses

Some providers return responses that don't completely satisfy OpenAI's scheme. Like, Gemini chat completion response ommits id field which is a required field in OpenAI's API Reference.

In such case use fillRequiredFieldIfKeyNotFound Parsing Option, like this:

letconfiguration=OpenAI.Configuration(token:"", parsingOptions:.fillRequiredFieldIfKeyNotFound)

Handle missing values in responses

Some fields are required to be present (non-optional) by OpenAI, but other providers may return null for them.

Use .fillRequiredFieldIfValueNotFound to handle missing values

What if a provider returns additional fields?

Currently we handle such cases by simply adding additional fields to main model set. This is possible because optional fields wouldn't break or conflict with OpenAI's scheme. At the moment, such additional fields are added:

ChatResult

• citationsPerplexity

ChatResult.Choice.Message

• reasoningContentGrok, DeepSeek
• reasoningOpenRouter

Example Project

You can find example iOS application in Demo folder.

Contribution Guidelines

Make your Pull Requests clear and obvious to anyone viewing them.
Set main as your target branch.

Use Conventional Commits principles in naming PRs and branches:

• Feat: ... for new features and new functionality implementations.
• Bug: ... for bug fixes.
• Fix: ... for minor issues fixing, like typos or inaccuracies in code.
• Chore: ... for boring stuff like code polishing, refactoring, deprecation fixing etc.

PR naming example: Feat: Add Threads API handling or Bug: Fix message result duplication

Branch naming example: feat/add-threads-API-handling or bug/fix-message-result-duplication

Write description to pull requests in following format:

• What

  ...

• Why

  ...

• Affected Areas

  ...

• More Info

  ...

We'll appreciate you including tests to your code if it is needed and possible. ❤️

Links

• OpenAI Documentation
• OpenAI Playground
• OpenAI Examples
• Dall-E
• Cosine Similarity

License

MIT License
Copyright (c) 2023 MacPaw Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.