SwiftAI

Build beautiful and reliable LLM apps on iOS and MacOS

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SwiftAI is a modern, type-safe Swift library for building AI-powered apps. It provides a unified API that works seamlessly across different AI models, including Apple's on-device models and cloud-based services like OpenAI. With features like model agnosticism, structured output, agent tool loop, conversations, extensibility, and Swift-native design, SwiftAI offers a powerful toolset for developers to integrate AI capabilities into their applications. The library supports easy installation via Swift Package Manager and offers detailed guidance on getting started, structured responses, tool use, model switching, conversations, and advanced constraints. SwiftAI aims to simplify AI integration by providing a type-safe and versatile solution for various AI tasks.

README:

SwiftAI

A modern, type-safe Swift library for building AI-powered apps. SwiftAI provides a unified API that works seamlessly across different AI models - from Apple's on-device models to cloud-based services like OpenAI.

Features

🤖 Model Agnostic: Unified API across Apple's on-device models, OpenAI, MLX, and custom backends
🎯 Structured Output: Strongly-typed structured outputs with compile-time validation
🔧 Agent Tool Loop: First-class support for tool use
💬 Conversations: Stateful chat sessions with automatic context management
🏗️ Extensible: Plugin architecture for custom models and tools
⚡ Swift-Native: Built with async/await and modern Swift concurrency

Quick Start

import SwiftAI

let llm = SystemLLM()
let response = try await llm.reply(to: "What is the capital of France?")
print(response.content) // "Paris"

Installation

Swift Package Manager

Xcode:

Go to File → Add Package Dependencies
Enter: https://github.com/mi12labs/SwiftAI
Click Add Package

Package.swift (Non Xcode):

dependencies: [
  .package(url: "https://github.com/mi12labs/SwiftAI", from: "main")
]

Getting Started

Step 1: Your First AI Query

Start with the simplest possible example - just ask a question and get an answer:

import SwiftAI

// Initialize Apple's on-device language model.
let llm = SystemLLM()

// Ask a question and get a response.
let response = try await llm.reply(to: "What is the capital of France?")
print(response.content) // "Paris"

What just happened?

SystemLLM() creates Apple's on-device AI model
reply(to:) sends your question and returns a String by default
try await handles the asynchronous AI processing
The response is wrapped in a response object - use .content to get the actual text

Step 2: Structured Responses

Instead of getting plain text, let's get structured data that your app can use directly:

// Define the structure you want back
@Generable
struct CityInfo {
  let name: String
  let country: String
  let population: Int
}

let response = try await llm.reply(
  to: "Tell me about Tokyo",
  returning: CityInfo.self // Tell the LLM what to output
)

let cityInfo = response.content
print(cityInfo.name)       // "Tokyo"
print(cityInfo.country)    // "Japan"
print(cityInfo.population) // 13960000

What's new here?

@Generable tells SwiftAI this struct can be generated by AI
returning: CityInfo.self specifies you want structured data, not a string
SwiftAI automatically converts the AI's response into your struct
No JSON parsing required!

💡 Key Concept: Type-Safe AI

SwiftAI ensures the AI returns data in exactly the format your code expects. If the AI can't generate valid data, you'll get an error instead of broken data.

Step 3: Tool Use

Let your AI call functions in your app to get real-time information:

// Create a tool the AI can use
struct WeatherTool: Tool {
  let description = "Get current weather for a city"

  @Generable
  struct Arguments {
    let city: String
  }

  func call(arguments: Arguments) async throws -> String {
    // Your weather API logic here
    return "It's 72°F and sunny in \(arguments.city)"
  }
}

// Use the tool with your AI
let weatherTool = WeatherTool()
let response = try await llm.reply(
    to: "What's the weather like in San Francisco?",
    tools: [weatherTool]
)
print(response.content) // "Based on current data, it's 72°F and sunny in San Francisco"

What's new here?

Tool protocol lets you create functions the AI can call
Arguments struct defines what parameters your tool needs (also @Generable)
The AI automatically decides when to call your tool
You get back a natural language response that incorporates the tool's data

💡 Key Concept: AI Function Calling

The AI reads your tool's description and automatically decides whether to call it. You don't manually trigger tools - the AI does it when needed.

Step 4: Model Switching

Different AI models have different strengths. SwiftAI makes switching seamless:

// Choose your model based on availability
let llm: any LLM = {
  let systemLLM = SystemLLM()
  return systemLLM.isAvailable ? systemLLM : OpenaiLLM(apiKey: "your-api-key")
}()

// Same code works with any model
let response = try await llm.reply(to: "Write a haiku about Berlin.")
print(response.content)

What's new here?

SystemLLM runs on-device (private, fast, free)
OpenaiLLM uses the cloud (more capable, requires API key)
isAvailable checks if the on-device model is ready
Same reply() method works with any LLM

💡 Key Concept: Model Agnostic API

Your code doesn't change when you switch models. This lets you optimize for different scenarios (privacy, capabilities, cost) without rewriting your app.

Step 5: Conversations

For multi-turn conversations, use Chat to maintain context across messages:

// Create a chat with tools
let chat = try Chat(with: llm, tools: [weatherTool])

// Have a conversation
let greeting = try await chat.send("Hello! I'm planning a trip.")
let advice = try await chat.send("What should I pack for Seattle?")
// The AI remembers context from previous messages

What's new here?

Chat maintains conversation history automatically
send() is like reply() but remembers previous messages
Tools work in conversations too
The AI remembers context from earlier in the conversation

💡 Key Concept: Stateful vs Stateless

reply() is stateless - each call is independent
Chat is stateful - builds on previous conversation

Step 6: Advanced Constraints

Add validation rules and descriptions to guide AI generation:

@Generable
struct UserProfile {
  @Guide(description: "A valid username starting with a letter", .pattern("^[a-zA-Z][a-zA-Z0-9_]{2,}$"))
  let username: String

  @Guide(description: "User age in years", .minimum(13), .maximum(120))
  let age: Int

  @Guide(description: "One to three favorite colors", .minimumCount(1), .maximumCount(3))
  let favoriteColors: [String]
}

What's new here?

@Guide adds constraints and descriptions to fields which help LLM generate good content
.pattern() tells the LLM to follow a regex
.minimum() and .maximum() constrain numbers
.minimumCount() and .maximumCount() control array sizes

💡 Key Concept: Validated Generation

Constraints ensure the AI follows your business rules.

Locall AI using MLX (Experimental)

The MLX backend provides access to local language models through Apple's MLX framework.

Setup:

// Add SwiftAIMLX to your target in Package.swift
targets: [
  .target(
    name: "YourTarget",
    dependencies: [
      .product(name: "SwiftAI", package: "SwiftAI"),
      .product(name: "SwiftAIMLX", package: "SwiftAI")  // 👈 Add this
    ]
  )
]

Usage:

import SwiftAI
import SwiftAIMLX
import MLXLLM

// The model manager handles MLX models within an app instance.
// Responsibilities:
// - Downloading models from Hugging Face (if not already on disk)
// - Caching model weights in memory
// - Sharing model weights across the app instance
let modelManager = MlxModelManager(storageDirectory: .documentsDirectory)

// Create an LLM with a specific configuration.
// Available configurations are listed in `LLMRegistry` (from MLXLLM).
//
// If the model is not yet available locally, it will be automatically
// downloaded from Hugging Face on first use.
let llm = modelManager.llm(withConfiguration: LLMRegistry.gemma3n_E2B_it_lm_4bit)

// Use the same API as with other LLM backends.
let response = try await llm.reply(to: "Hello!")
print(response.content)

Note: Structured output generation is not yet supported with MLX models.

🎯 Quick Reference

What You Want	What To Use	Example
Simple text response	`reply(to:)`	`reply(to: "Hello")`
Structured data	`reply(to:returning:)`	`reply(to: "...", returning: MyStruct.self)`
Function calling	`reply(to:tools:)`	`reply(to: "...", tools: [myTool])`
Conversation	`Chat`	`chat.send("Hello")`
Model switching	`any LLM`	`SystemLLM()` or `OpenaiLLM()`

🔧 Supported Models

Model	Type	Privacy	Capabilities	Cost
SystemLLM	On-device	🔒 Private	Good	🆓 Free
OpenaiLLM	Cloud API	⚠️ Shared	Excellent	💰 Paid
MlxLLM	On-device	🔒 Private	Excellent	🆓 Free
CustomLLM	Your choice	Your choice	Your choice	Your choice

📖 Examples

ChatApp: Interactive chat app with 10+ models
ReadMitAI: Essay reading app with AI-powered features

⚡ Feature Parity Status vs FoundationModels SDK

Feature	Status
Structured outputs for enums	❌ #issue

🤝 Contributing

We welcome contributions! Please read our Contributing Guidelines.

Development Setup

git clone https://github.com/your-org/SwiftAI.git
cd SwiftAI
swift build
swift test

📄 License

SwiftAI is released under the MIT License. See LICENSE for details.

⚠️ Alpha ⚠️

SwiftAI is alpha 🚧 – rough edges and breaking changes are expected.

Built with ❤️ for the Swift community

For Tasks:

Click tags to check more tools for each tasks

build chatbot get weather info switch ai models maintain chat context validate user input

For Jobs:

software developer machine learning engineer ai researcher app developer data scientist

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