Foundation Models Framework — Complete API Reference

Overview

The Foundation Models framework provides access to Apple’s on-device Large Language Model (3 billion parameters, 2-bit quantized) with a Swift API. This reference covers every API, all WWDC 2025 code examples, and comprehensive implementation patterns.

Model Specifications

3B parameter model, 2-bit quantized, 4096 token context (input + output combined). Optimized for on-device summarization, extraction, classification, and generation. NOT suited for world knowledge, complex reasoning, math, or translation. Runs entirely on-device — no network, no cost, no data leaves device.

When to Use This Reference

Use this reference when:

• Implementing Foundation Models features
• Understanding API capabilities
• Looking up specific code examples
• Planning architecture with Foundation Models
• Migrating from prototype to production
• Debugging implementation issues

Related Skills:

• axiom-foundation-models — Discipline skill with anti-patterns, pressure scenarios, decision trees
• axiom-foundation-models-diag — Diagnostic skill for troubleshooting issues

LanguageModelSession

Overview

LanguageModelSession is the core class for interacting with the model. It maintains conversation history (transcript), handles multi-turn interactions, and manages model state.

Creating a Session

Basic Creation:

import FoundationModels

let session = LanguageModelSession()

With Custom Instructions:

let session = LanguageModelSession(instructions: """
    You are a friendly barista in a pixel art coffee shop.
    Respond to the player's question concisely.
    """
)

From WWDC 301:1:05

With Tools:

let session = LanguageModelSession(
    tools: [GetWeatherTool()],
    instructions: "Help user with weather forecasts."
)

From WWDC 286:15:03

With Specific Model/Use Case:

let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging)
)

From WWDC 286:18:39

Instructions vs Prompts

Instructions:

• Come from developer
• Define model’s role, style, constraints
• Mostly static
• First entry in transcript
• Model trained to obey instructions over prompts (security feature)

Prompts:

• Come from user (or dynamic app state)
• Specific requests for generation
• Dynamic input
• Each call to respond(to:) adds prompt to transcript

Security Consideration:

• NEVER interpolate untrusted user input into instructions
• User input should go in prompts only
• Prevents prompt injection attacks

respond(to:) Method

Basic Text Generation:

func respond(userInput: String) async throws -> String {
    let session = LanguageModelSession(instructions: """
        You are a friendly barista in a world full of pixels.
        Respond to the player's question.
        """
    )
    let response = try await session.respond(to: userInput)
    return response.content
}

From WWDC 301:1:05

Return Type: Response<String> with .content property

respond(to:generating:) Method

Structured Output with @Generable:

@Generable
struct SearchSuggestions {
    @Guide(description: "A list of suggested search terms", .count(4))
    var searchTerms: [String]
}

let prompt = """
    Generate a list of suggested search terms for an app about visiting famous landmarks.
    """

let response = try await session.respond(
    to: prompt,
    generating: SearchSuggestions.self
)

print(response.content) // SearchSuggestions instance

From WWDC 286:5:51

Return Type: Response<SearchSuggestions> with .content property

Generation Options

See Sampling & Generation Options for GenerationOptions including sampling:, temperature:, and includeSchemaInPrompt:.

Multi-Turn Interactions

Retaining Context

let session = LanguageModelSession()

// First turn
let firstHaiku = try await session.respond(to: "Write a haiku about fishing")
print(firstHaiku.content)
// Silent waters gleam,
// Casting lines in morning mist—
// Hope in every cast.

// Second turn - model remembers context
let secondHaiku = try await session.respond(to: "Do another one about golf")
print(secondHaiku.content)
// Silent morning dew,
// Caddies guide with gentle words—
// Paths of patience tread.

print(session.transcript) // Shows full history

From WWDC 286:17:46

How it works:

• Each respond() call adds entry to transcript
• Model uses entire transcript for context
• Enables conversational interactions

Transcript Property

let transcript = session.transcript

for entry in transcript.entries {
    print("Entry: \(entry.content)")
}

Use cases:

• Debugging generation issues
• Displaying conversation history in UI
• Exporting chat logs
• Condensing for context management

isResponding Property

Gate UI on session.isResponding to prevent concurrent requests:

Button("Go!") {
    Task { haiku = try await session.respond(to: prompt).content }
}
.disabled(session.isResponding)

From WWDC 286:18:22

@Generable Macro

Overview

@Generable enables structured output from the model using Swift types. The macro generates a schema at compile-time and uses constrained decoding to guarantee structural correctness.

Basic Usage

On Structs:

@Generable
struct Person {
    let name: String
    let age: Int
}

let response = try await session.respond(
    to: "Generate a person",
    generating: Person.self
)

let person = response.content // Type-safe Person instance

From WWDC 301:8:14

On Enums:

@Generable
struct NPC {
    let name: String
    let encounter: Encounter

    @Generable
    enum Encounter {
        case orderCoffee(String)
        case wantToTalkToManager(complaint: String)
    }
}

From WWDC 301:10:49

Supported Types

Primitives:

• String
• Int, Float, Double, Decimal
• Bool

Collections:

• [ElementType] (arrays)

Composed Types:

@Generable
struct Itinerary {
    var destination: String
    var days: Int
    var budget: Float
    var rating: Double
    var requiresVisa: Bool
    var activities: [String]
    var emergencyContact: Person
    var relatedItineraries: [Itinerary] // Recursive!
}

From WWDC 286:6:18

@Guide Constraints

@Guide constrains generated properties. Supports description: (natural language), .range() (numeric bounds), .count() / .maximumCount() (array length), and Regex (pattern matching).

@Generable
struct NPC {
    @Guide(description: "A full name")
    let name: String

    @Guide(.range(1...10))
    let level: Int

    @Guide(.count(3))
    let attributes: [String]
}

From WWDC 301:11:20

Constrained Decoding

How it works:

1. @Generable macro generates schema at compile-time
2. Schema defines valid token sequences
3. During generation, model creates probability distribution for next token
4. Framework masks out invalid tokens based on schema
5. Model can only pick tokens valid according to schema
6. Guarantees structural correctness – no hallucinated keys, no invalid JSON

From WWDC 286: “Constrained decoding prevents structural mistakes. Model is prevented from generating invalid field names or wrong types.”

Benefits:

• Zero parsing code needed
• No runtime parsing errors
• Type-safe Swift objects
• Compile-time safety (changes to struct caught by compiler)

Property Declaration Order

Properties generated in order declared:

@Generable
struct Itinerary {
    var name: String        // Generated FIRST
    var days: [DayPlan]     // Generated SECOND
    var summary: String     // Generated LAST
}

Why it matters:

• Later properties can reference earlier ones
• Better model quality: Summaries after content
• Better streaming UX: Important properties first

From WWDC 286:11:00

Streaming

Overview

Foundation Models uses snapshot streaming (not delta streaming). Instead of raw deltas, the framework streams PartiallyGenerated types with optional properties that fill in progressively.

PartiallyGenerated Type

The @Generable macro automatically creates a PartiallyGenerated nested type:

@Generable
struct Itinerary {
    var name: String
    var days: [DayPlan]
}

// Compiler generates:
extension Itinerary {
    struct PartiallyGenerated {
        var name: String?        // All properties optional!
        var days: [DayPlan]?
    }
}

From WWDC 286:9:20

streamResponse Method

@Generable
struct Itinerary {
    var name: String
    var days: [Day]
}

let stream = session.streamResponse(
    to: "Craft a 3-day itinerary to Mt. Fuji.",
    generating: Itinerary.self
)

for try await partial in stream {
    print(partial) // Incrementally updated Itinerary.PartiallyGenerated
}

From WWDC 286:9:40

Return Type: AsyncSequence<Itinerary.PartiallyGenerated>

SwiftUI Integration

struct ItineraryView: View {
    let session: LanguageModelSession
    let dayCount: Int
    let landmarkName: String

    @State
    private var itinerary: Itinerary.PartiallyGenerated?

    var body: some View {
        VStack {
            if let name = itinerary?.name {
                Text(name).font(.title)
            }

            if let days = itinerary?.days {
                ForEach(days, id: \.self) { day in
                    DayView(day: day)
                }
            }

            Button("Start") {
                Task {
                    do {
                        let prompt = """
                            Generate a \(dayCount) itinerary \
                            to \(landmarkName).
                            """

                        let stream = session.streamResponse(
                            to: prompt,
                            generating: Itinerary.self
                        )

                        for try await partial in stream {
                            self.itinerary = partial
                        }
                    } catch {
                        print(error)
                    }
                }
            }
        }
    }
}

From WWDC 286:10:05

Best Practices

1. Use SwiftUI animations:

if let name = itinerary?.name {
    Text(name)
        .transition(.opacity)
}

2. View identity for arrays:

// ✅ GOOD - Stable identity
ForEach(days, id: \.id) { day in
    DayView(day: day)
}

// ❌ BAD - Identity changes
ForEach(days.indices, id: \.self) { index in
    DayView(day: days[index])
}

3. Property order optimization:

// ✅ GOOD - Title first for streaming
@Generable
struct Article {
    var title: String      // Shows immediately
    var summary: String    // Shows second
    var fullText: String   // Shows last
}

From WWDC 286:11:00

Tool Protocol

Overview

Tools let the model autonomously execute your custom code to fetch external data or perform actions. Tools integrate with MapKit, WeatherKit, Contacts, EventKit, or any custom API.

Protocol Definition

protocol Tool {
    var name: String { get }
    var description: String { get }

    associatedtype Arguments: Generable

    func call(arguments: Arguments) async throws -> ToolOutput
}

Example: GetWeatherTool

import FoundationModels
import WeatherKit
import CoreLocation

struct GetWeatherTool: Tool {
    let name = "getWeather"
    let description = "Retrieve the latest weather information for a city"

    @Generable
    struct Arguments {
        @Guide(description: "The city to fetch the weather for")
        var city: String
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        let places = try await CLGeocoder().geocodeAddressString(arguments.city)
        let weather = try await WeatherService.shared.weather(for: places.first!.location!)
        let temperature = weather.currentWeather.temperature.value

        let content = GeneratedContent(properties: ["temperature": temperature])
        let output = ToolOutput(content)

        // Or if your tool's output is natural language:
        // let output = ToolOutput("\(arguments.city)'s temperature is \(temperature) degrees.")

        return output
    }
}

From WWDC 286:13:42

Attaching Tools to Session

let session = LanguageModelSession(
    tools: [GetWeatherTool()],
    instructions: "Help the user with weather forecasts."
)

let response = try await session.respond(
    to: "What is the temperature in Cupertino?"
)

print(response.content)
// It's 71˚F in Cupertino!

From WWDC 286:15:03

How it works:

1. Session initialized with tools
2. User prompt: “What’s Tokyo’s weather?”
3. Model analyzes prompt, decides weather data needed
4. Model generates tool call: getWeather(city: "Tokyo")
5. Framework calls call() method
6. Your code fetches real data from API
7. Tool output inserted into transcript
8. Model generates final response using tool output

From WWDC 301: “Model autonomously decides when and how often to call tools. Can call multiple tools per request, even in parallel.”

Stateful Tools

Use class instead of struct to maintain state across tool calls. The tool instance persists for the session lifetime, enabling patterns like tracking previously returned results:

class FindContactTool: Tool {
    let name = "findContact"
    let description = "Finds a contact from a specified age generation."
    var pickedContacts = Set<String>()

    @Generable
    struct Arguments {
        let generation: Generation
        @Generable
        enum Generation { case babyBoomers, genX, millennial, genZ }
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        // Fetch, filter out already-picked, return new contact
        pickedContacts.insert(pickedContact.givenName)
        return ToolOutput(pickedContact.givenName)
    }
}

From WWDC 301:18:47, 301:21:55

ToolOutput

Two forms:

1. Natural language (String):

return ToolOutput("Temperature is 71°F")

2. Structured (GeneratedContent):

let content = GeneratedContent(properties: ["temperature": 71])
return ToolOutput(content)

Tool Naming Best Practices

DO:

• Short, readable names: getWeather, findContact
• Use verbs: get, find, fetch, create
• One sentence descriptions
• Keep descriptions concise (they’re in prompt)

DON’T:

• Abbreviations: gtWthr
• Implementation details in description
• Long descriptions (increases token count)

From WWDC 301: “Tool name and description put verbatim in prompt. Longer strings mean more tokens, which increases latency.”

Multiple Tools

let session = LanguageModelSession(
    tools: [
        GetWeatherTool(),
        FindRestaurantTool(),
        FindHotelTool()
    ],
    instructions: "Plan travel itineraries."
)

// Model autonomously decides which tools to call and when

Tool Calling Behavior

Key facts:

• Tool can be called multiple times per request
• Multiple tools can be called in parallel
• Model decides when to call (not guaranteed to call)
• Arguments guaranteed valid via @Generable

From WWDC 301: “When tools called in parallel, your call method may execute concurrently. Keep this in mind when accessing data.”

Dynamic Schemas

Overview

DynamicGenerationSchema enables creating schemas at runtime instead of compile-time. Useful for user-defined structures, level creators, or dynamic forms.

Creating and Using Dynamic Schemas

Build properties with DynamicGenerationSchema.Property, compose into schemas, then validate with GenerationSchema:

// Build schema at runtime
let questionProp = DynamicGenerationSchema.Property(
    name: "question", schema: DynamicGenerationSchema(type: String.self)
)
let answersProp = DynamicGenerationSchema.Property(
    name: "answers", schema: DynamicGenerationSchema(
        arrayOf: DynamicGenerationSchema(referenceTo: "Answer")
    )
)

let riddleSchema = DynamicGenerationSchema(name: "Riddle", properties: [questionProp, answersProp])
let answerSchema = DynamicGenerationSchema(name: "Answer", properties: [/* text, isCorrect */])

// Validate and use
let schema = try GenerationSchema(root: riddleSchema, dependencies: [answerSchema])
let response = try await session.respond(to: "Generate a riddle", schema: schema)

let question = try response.content.value(String.self, forProperty: "question")

From WWDC 301:14:50, 301:15:10

Dynamic vs Static @Generable

Use @Generable when:

• Structure known at compile-time
• Want type safety
• Want automatic parsing

Use Dynamic Schemas when:

• Structure only known at runtime
• User-defined schemas
• Maximum flexibility

From WWDC 301: “Compile-time @Generable gives type safety. Dynamic schemas give runtime flexibility. Both use same constrained decoding guarantees.”

Sampling & Generation Options

Greedy (deterministic) — use for tests and demos. Only deterministic within same model version:

let response = try await session.respond(
    to: prompt,
    options: GenerationOptions(sampling: .greedy)
)

Temperature — controls variance. 0.1-0.5 focused, 1.0 default, 1.5-2.0 creative:

let response = try await session.respond(
    to: prompt,
    options: GenerationOptions(temperature: 0.5)
)

From WWDC 301:6:14

Built-in Use Cases

Content Tagging Adapter

Specialized adapter for:

• Tag generation
• Entity extraction
• Topic detection

@Generable
struct Result {
    let topics: [String]
}

let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging)
)

let response = try await session.respond(
    to: articleText,
    generating: Result.self
)

From WWDC 286:19:19

Custom Use Cases

With custom instructions:

@Generable
struct Top3ActionEmotionResult {
    @Guide(.maximumCount(3))
    let actions: [String]
    @Guide(.maximumCount(3))
    let emotions: [String]
}

let session = LanguageModelSession(
    model: SystemLanguageModel(useCase: .contentTagging),
    instructions: "Tag the 3 most important actions and emotions in the given input text."
)

let response = try await session.respond(
    to: text,
    generating: Top3ActionEmotionResult.self
)

From WWDC 286:19:35

Error Handling

GenerationError Types

Catch LanguageModelSession.GenerationError cases:

• .exceededContextWindowSize — Context limit (4096 tokens) exceeded. Condense transcript or create new session.
• .guardrailViolation — Content policy triggered. Show graceful message.
• .unsupportedLanguageOrLocale — Language not supported. Check supportedLanguages.

From WWDC 301:3:37, 301:7:06

Context Window Management

Strategy 1: Fresh Session

var session = LanguageModelSession()

do {
    let response = try await session.respond(to: prompt)
    print(response.content)
} catch LanguageModelSession.GenerationError.exceededContextWindowSize {
    // New session, no history
    session = LanguageModelSession()
}

From WWDC 301:3:37

Strategy 2: Condensed Session

do {
    let response = try await session.respond(to: prompt)
} catch LanguageModelSession.GenerationError.exceededContextWindowSize {
    // New session with some history
    session = newSession(previousSession: session)
}

private func newSession(previousSession: LanguageModelSession) -> LanguageModelSession {
    let allEntries = previousSession.transcript.entries
    var condensedEntries = [Transcript.Entry]()

    if let firstEntry = allEntries.first {
        condensedEntries.append(firstEntry) // Instructions

        if allEntries.count > 1, let lastEntry = allEntries.last {
            condensedEntries.append(lastEntry) // Recent context
        }
    }

    let condensedTranscript = Transcript(entries: condensedEntries)
    // Note: transcript includes instructions
    return LanguageModelSession(transcript: condensedTranscript)
}

From WWDC 301:3:55

Fallback Architecture

When Foundation Models is unavailable (older device, user opted out, unsupported region), provide graceful degradation:

func summarize(_ text: String) async throws -> String {
    let model = SystemLanguageModel.default
    switch model.availability {
    case .available:
        let session = LanguageModelSession()
        let response = try await session.respond(to: "Summarize: \(text)")
        return response.content
    case .unavailable:
        // Fallback: truncate with ellipsis, or call server API
        return String(text.prefix(200)) + "..."
    }
}

Architecture pattern: Wrap Foundation Models behind a protocol so you can swap implementations:

protocol TextSummarizer {
    func summarize(_ text: String) async throws -> String
}

struct OnDeviceSummarizer: TextSummarizer { /* Foundation Models */ }
struct ServerSummarizer: TextSummarizer { /* Server API fallback */ }
struct TruncationSummarizer: TextSummarizer { /* Simple truncation */ }

Nested @Generable Troubleshooting

Nested @Generable types must each independently conform to @Generable:

// ✅ Both types marked @Generable
@Generable struct Itinerary {
    var days: [DayPlan]
}

@Generable struct DayPlan {
    var activities: [String]
}

// ❌ Will fail — nested type not @Generable
@Generable struct Itinerary {
    var days: [DayPlan]  // DayPlan must also be @Generable
}
struct DayPlan { var activities: [String] }

Common issue: Arrays of non-Generable types compile but fail at runtime. Check all types in the graph.

Availability

Checking Availability

struct AvailabilityExample: View {
    private let model = SystemLanguageModel.default

    var body: some View {
        switch model.availability {
        case .available:
            Text("Model is available").foregroundStyle(.green)
        case .unavailable(let reason):
            Text("Model is unavailable").foregroundStyle(.red)
            Text("Reason: \(reason)")
        }
    }
}

From WWDC 286:19:56

Supported Languages

let supportedLanguages = SystemLanguageModel.default.supportedLanguages
guard supportedLanguages.contains(Locale.current.language) else {
    // Show message
    return
}

From WWDC 301:7:06

Requirements

Device Requirements:

• Apple Intelligence-enabled device
• iPhone 15 Pro or later
• iPad with M1+ chip
• Mac with Apple silicon

Region Requirements:

• Supported region (check Apple Intelligence availability)

User Requirements:

• User opted in to Apple Intelligence in Settings

Performance & Profiling

Foundation Models Instrument

Access: Instruments app → Foundation Models template

Metrics:

• Initial model load time
• Token counts (input/output)
• Generation time per request
• Latency breakdown
• Optimization opportunities

From WWDC 286: “New Instruments profiling template lets you observe areas of optimization and quantify improvements.”

Optimization: Prewarming

Problem: First request takes 1-2s to load model

Solution: Create session before user interaction

class ViewModel: ObservableObject {
    private var session: LanguageModelSession?

    init() {
        // Prewarm on init
        Task {
            self.session = LanguageModelSession(instructions: "...")
        }
    }

    func generate(prompt: String) async throws -> String {
        let response = try await session!.respond(to: prompt)
        return response.content
    }
}

From WWDC 259: “Prewarming session before user interaction reduces initial latency.”

Time saved: 1-2 seconds off first generation

Optimization: includeSchemaInPrompt

Problem: Large @Generable schemas increase token count

Solution: Skip schema insertion for subsequent requests

// First request - schema inserted
let first = try await session.respond(
    to: "Generate first person",
    generating: Person.self
)

// Subsequent requests - skip schema
let second = try await session.respond(
    to: "Generate another person",
    generating: Person.self,
    options: GenerationOptions(includeSchemaInPrompt: false)
)

From WWDC 259: “Setting includeSchemaInPrompt to false decreases token count and latency for subsequent requests.”

Time saved: 10-20% per request

Optimization: Property Order

Declare important properties first in @Generable structs. With streaming, perceived latency drops from 2.5s to 0.2s when title appears before full text. See Streaming Best Practices for examples.

Feedback & Analytics

LanguageModelFeedbackAttachment lets you report model quality issues to Apple via Feedback Assistant. Create with input, output, sentiment (.positive/.negative), issues (category + explanation), and desiredOutputExamples. Encode as JSON and attach to a Feedback Assistant report.

From WWDC 286:22:13

Xcode Playgrounds

Overview

Xcode Playgrounds enable rapid iteration on prompts without rebuilding entire app.

Basic Usage

import FoundationModels
import Playgrounds

#Playground {
    let session = LanguageModelSession()
    let response = try await session.respond(
        to: "What's a good name for a trip to Japan? Respond only with a title"
    )
}

From WWDC 286:2:28

Playgrounds can also access types defined in your app (like @Generable structs).

API Quick Reference

• LanguageModelSession — Main interface: respond(to:) → Response<String>, respond(to:generating:) → Response<T>, streamResponse(to:generating:) → AsyncSequence<T.PartiallyGenerated>. Properties: transcript, isResponding.
• SystemLanguageModel — default.availability (.available/.unavailable(reason)), default.supportedLanguages, init(useCase:)
• GenerationOptions — sampling (.greedy/.random), temperature, includeSchemaInPrompt
• @Generable — Macro enabling structured output with constrained decoding
• @Guide — Property constraints: description:, .range(), .count(), .maximumCount(), Regex
• Tool protocol — name, description, Arguments: Generable, call(arguments:) → ToolOutput
• DynamicGenerationSchema — Runtime schema definition with GeneratedContent output
• GenerationError — .exceededContextWindowSize, .guardrailViolation, .unsupportedLanguageOrLocale

Migration Strategies

From Server LLMs

• Migrate when: Privacy required, offline needed, per-request costs are a concern, and use case fits (summarization/extraction/classification)
• Stay on server when: Need world knowledge, complex reasoning, or >4096 token context

From Manual JSON Parsing

Use @Generable with respond(to:generating:) instead of prompting for JSON and parsing manually. See axiom-foundation-models Scenario 2 for the complete migration pattern.

Resources

WWDC: 286, 259, 301

Skills: axiom-foundation-models, axiom-foundation-models-diag

Last Updated: 2025-12-03 Version: 1.0.0 Skill Type: Reference Content: All WWDC 2025 code examples included