CoreAIKit

Use your model with FoundationModels

KitLanguageModel adapts chat bundles to Apple’s LanguageModelSession. The catalog also contains other task kinds; vision uses KitVisionModel, and speech has its own API. For a repeatable two-turn example, select this release’s built-in pin:

import FoundationModels
import CoreAIKit

guard let modelID = ModelCatalog.builtin.entry(id: "qwen3-0.6b")?.modelID else {
    throw CoreAIKitError.modelNotAvailableOnPlatform(id: "qwen3-0.6b")
}
let model = try await KitLanguageModel(model: modelID)
let session = LanguageModelSession(model: model)
let first = try await session.respond(to: "Remember: my secret word is ORCHID. Confirm briefly.")
let second = try await session.respond(to: "What is my secret word? Reply with only that word.")
print(first.content, second.content)

The second answer should recall ORCHID. Tool calling requires a compatible ChatML/Hermes model; guided generation requires a sequential engine. See the capability limits.

After chat, Speak adds one concrete capability: VoxCPM 0.5B text to speech, producing a mono WAV with a fixed voice. It has a separate model download.

Works with Apple’s FoundationModels API

KitLanguageModel plugs compatible Core AI chat bundles into the system LanguageModelSession — the same FoundationModels API you use for Apple’s built-in model — and adds what the stock CoreAILanguageModel adapter lacks: tool calling (ChatML/Hermes models and LFM3) and guided generation (sequential engines).

import FoundationModels
import CoreAIKit

guard let modelID = ModelCatalog.builtin.entry(id: "qwen3-0.6b")?.modelID else {
    throw CoreAIKitError.modelNotAvailableOnPlatform(id: "qwen3-0.6b")
}
let model = try await KitLanguageModel(model: modelID)   // downloads once, then cached
let session = LanguageModelSession(model: model, tools: [WeatherTool()])
let answer = try await session.respond(to: "What's the weather in Tokyo?")

KitVisionModel does the same for vision-language models — attach an image to the prompt:

let vlm = try await KitVisionModel(catalog: "qwen3-vl-2b")   // decoder + vision tower
let session = LanguageModelSession(model: vlm)
let answer = try await session.respond(to: Prompt {
    Attachment(cgImage)
    "What is in this photo?"
})

Your Tool implementations, @Generable types, streaming snapshots, and transcripts work unchanged. See Examples/FMToolDemo, Examples/GuidedDemo, and Examples/VLChat.

What each provider honestly advertises:

	`KitLanguageModel` (text)	`KitVisionModel` (VL)
Tool calling	ChatML/Hermes models — the qwen3 family — and LFM3’s XML calls (LFM2.5’s pythonic dialect is not parsed)	not in v1, by design
Reasoning	thinking models stream `.reasoning`	Qwen3-VL thinks by default
Guided generation	sequential engines only (`engineVariant: .sequential`)	not in v1, by design
Vision	—	one image per session; every turn re-prefills the full prompt (the vision encode is reused while the image is unchanged)

Compared with Apple’s stock CoreAILanguageModel adapter, this provider adds tool calling, per-turn usage events (including Usage.Input.cachedTokenCount), and a KV fast path that rewinds to the longest shared prefix with the previous turn (reset(to:) + the engine’s implicit prefix caching) instead of re-prefilling the whole transcript — including across a divergence, e.g. a re-rendered transcript.

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