Apple Foundation Models

On-device AI using Apple's Foundation Models framework (iOS 26+, now shipping). FM is the primary AI provider; MLX and pre-authored templates serve as fallbacks.

When to Use

• Generating personalized coaching content on-device
• Structured output from on-device language models
• Managing the 4096-token context limit
• Building AI features that work offline with no cloud dependency

Core Concepts

Availability Checking

Foundation Models is available now on supported iOS 26+ / watchOS 26+ devices. Availability varies by device capability and region. A two-layer check — #if canImport(FoundationModels) at compile time plus LanguageModelSession.isAvailable at runtime — covers both Xcode compatibility and device detection. See reference.md for detailed checking patterns.

Basic Text Generation

LanguageModelSession accepts system-level instructions and produces text responses from a prompt. Instructions set the persona and guardrails (e.g., prohibiting medical advice). See examples.md for a complete BreathCoach implementation.

Structured Output with @Generable

The @Generable macro paired with @Guide constraints produces typed Swift structs instead of free-form text. Field-level constraints (options lists, numeric ranges) steer the model toward valid output. See examples.md for the BreathingRecommendation pattern.

@Guide constraint gap: @Guide(.anyOf(...)) and @Guide(.range(...)) are NOT available in the current FoundationModels SDK (as of iOS 26.2). Use @Guide(description: "...") strings that enumerate valid values, add redundant enumeration in the prompt body, and validate output after respond(to:generating:). Cross-field constraints ("stage 1-4 for ujjayi, 1-2 for viloma") cannot be expressed in @Generable — handle them in code with allowlists, range checks, and length caps. Fall through to the deterministic fallback on any validation miss.

@Generable shadow type pattern: Keep the FM-coupled @Generable struct guarded by #if canImport(FoundationModels) + @available(iOS 26, *). Convert at the call site to a plain Sendable struct that crosses the protocol boundary. The protocol type has zero FM dependency, so mocks, fallbacks, and tests stay simple:

#if canImport(FoundationModels)
import FoundationModels

@available(iOS 26, *)
@Generable
struct ReadinessRecommendationDraft {
    // internal (not private) — @Generable macro expansion requires non-private access
    @Guide(description: "Technique ID. Valid: ujjayi, viloma, box, resonance")
    var techniqueID: String
    @Guide(description: "Session length in minutes. Valid: 5, 7, 10, 15, 20")
    var sessionMinutes: Int
}
#endif

// Plain Sendable crosses the protocol boundary — no FM import needed in consumers
struct ReadinessRecommendation: Sendable {
    let techniqueID: String
    let sessionMinutes: Int
    let rationale: String
}

@Generable access requirement: The macro expansion references the type by name and fails with private types. Promote to file-scope internal (still safe inside #if canImport(FoundationModels)).

Token Management (4096 Limit)

The on-device model has a 4096-token context window. A summarize-and-reset strategy keeps multi-turn conversations within budget by periodically condensing history into a fresh session. See reference.md for the rationale and examples.md for the ManagedCoachSession implementation.

Tool Calling

Structured output doubles as a tool-calling mechanism: the model produces a typed request (e.g., RequestBiometrics), the app fulfills it locally, and feeds the result back. See reference.md for mechanics and examples.md for the pattern.

MLX Fallback (Secondary)

MLX is the secondary fallback for devices that run Apple Silicon but lack Foundation Models support (iOS 18-25). A ~1GB quantized Llama fits on iPhone but is marginal on Apple Watch due to memory constraints. For new projects, FM is the primary implementation target; MLX serves devices that can't run FM. See reference.md for when to prefer MLX vs. templates.

Provider Abstraction

A protocol-based provider chain (AICoachProvider) abstracts over Foundation Models, MLX, and pre-written templates. The chain tries each provider in order and falls back automatically. See examples.md for the full implementation.

AI Tier Detection

A detectAITier() pattern selects the best available AI backend at runtime: FM (primary) → MLX (secondary) → pre-authored templates (fallback). Guarding FM-specific code with #if canImport(FoundationModels) ensures compilation on Xcode versions that predate the framework. See examples.md for the implementation and reference.md for the tier selection rationale.

Anti-Patterns

Key References

• WWDC25: Foundation Models deep dive
• AzamSharp: Foundation Models Guide
• Apple ML Research: Core ML Llama

Sibling Files

• reference.md -- Detailed explanations of availability, tokens, tool calling, MLX rationale, provider design
• examples.md -- All Swift code examples with section headers for navigation