Hello, I'm evaluating Foundation Models announced at WWDC 2026 and have a question regarding App Store distribution. My understanding is that Foundation Models are only available on supported devices and operating system versions. For apps that rely on Foundation Models as their primary functionality (rather than offering AI as an optional feature), I'm trying to understand the recommended distribution strategy. Currently, iOS provides Required Device Capabilities to prevent users from installing apps that require hardware features such as GPS, ARKit, or NFC. However, I couldn't find an equivalent Required Device Capability for Foundation Models. I also couldn't find a way to limit App Store availability by supported device models. My questions are: What is the recommended way to distribute an app whose primary functionality depends on Foundation Models? Is there currently any supported mechanism to prevent users with unsupported devices from downloading such an app? Is Apple planning to introduce a Required Device Capability (or a similar App Store filtering mechanism) for Foundation Models before public release? Without such a mechanism, users may be able to install the app successfully but then discover that its primary functionality is unavailable on their device. I'd appreciate any guidance on the recommended approach. Thank you.

I'm following along with the WWDC video on SpotlightSearchTool and hitting an error - looking for some guidance. I've configured SpotlightSearchTool and I'm sending it to the session. let session = LanguageModelSession(tools: [tool]) { spotlightSearchInstructions } let response = try await session.respond(to: prompt, options: GenerationOptions(toolCallingMode: .required)) I set the tool calling mode to required as a test - without it I don't get errors but the logging makes it seem like it's not calling the search tool and the responses would seem to confirm that (they're not grounded in my data). So, I figured I'd try forcing it to use the tool. When I do that, I get this console error: InferenceError::hostFailed::InferenceError::inferenceFailed::TokenGenerationCore.GuidedGenerationError.invalidConfiguration(errorMessage: "Tool Choice requires tools") in response to ExecuteRequest Error during session.respond. description="The operation couldn’t be completed. (FoundationModels.LanguageModelError error -1.)" Returning empty Spotlight result. elapsedMs=3254 toolReplies=0 totalSearchItems=0 uniqueSearchItems=0 What does that mean? I'm passing in a tool, everything compiles correctly, etc. Not sure how to debug - any advice appreciated! Testing this via the Simulator on beta 3.

Since beta 2 I think, it seems Foundation models are not accessible if Siri AI is not enabled. I'm on Mac OS, and not sure how it works on iOS, but does that mean that Foundation Models will not be usable if Siri AI is not enabled (Europe)?

I have a health app on the App Store that uses Foundation Models to generate brief narrative summaries from the user's own glucose and menstrual cycle data. No medical advice, just supportive summaries of their own numbers. This has been working reliably on iOS 26.x since early 2026. After updating to iOS 27 beta 2, every prompt is refused. The error is LanguageModelError ("The model refused to answer" / "May contain sensitive content"), not GenerationError.guardrailViolation. I've confirmed: Same device, same code, same prompts. Worked on iOS 26.x, fails on iOS 27 beta 2. Two independent features with different prompt structures and different service architectures are both affected. Using SystemLanguageModel(guardrails: .permissiveContentTransformations) does not help. The classifier passes. The model itself refuses. The prompts contain terms like "luteal phase," "progesterone," "glucose," "time in range," and "diabetes" in the system instructions. This appears to be a model-level sensitivity change in the iOS 27 on-device model that broadly blocks health/medical terminology, even when the use case is summarizing the user's own data. Filed as FB23513774 with the full prompt text, instructions, and source file attached. Is anyone else seeing model-level refusals (not guardrailViolation) on iOS 27 beta for health or medical content? Related threads from iOS 26 betas: Model Guardrails Too Restrictive? Model w/ Guardrails Disabled Still Refusing Using Past Versions of Foundation Models As They Progress

Apologies, if I've missed the answer already here, I've searched around but can't find it. Foundation models and Private Cloud Compute require Apple Intelligence to be enabled in Settings as mentioned here. At the same time it says that Foundation Models PCC calls are supported on all Apple Watch models that run WatchOS 27. So, will there be a seperate Apple Intelligence setting in WatchOS 27 for those devices? Otherwise if a user has an Apple Watch Series 11 (which does support Apple Intelligence) paired with an iPhone 15 (which doesn't support Apple Intelligence), will they be unable to use the Foundation Models PCC calls from WatchOS in my app? Despite the fact the iPhone isn't involved in these queries anyway?

On this talk: Bring an LLM provider to the Foundation Models framework URL: https://developer.apple.com/videos/play/wwdc2026/339/ on the coding examples a very peculiar framework is shown: import MLXFoundationModels However I am not able to find it nowhere, there is even a code section with this framework as part of an example. Where is this framework, there are no BETA branches on the MLX framework either. Thanks!

When importing FoundationModels in watchOS 27 Beta 2 this error appears: /Applications/Xcode-beta.app/Contents/Developer/Platforms/WatchOS.platform/Developer/SDKs/WatchOS27.0.sdk/System/Library/Frameworks/FoundationModels.framework/Modules/FoundationModels.swiftmodule/arm64e-apple-watchos.swiftinterface:6:15 Unable to resolve module dependency: 'CoreImage' Does anybody else have this issue?

I’ve been experimenting with Foundation Models and built a small Swift package that wraps LanguageModelSession with simple context management. The current approach checks the transcript token count using tokenCount(for:), compacts the transcript when it reaches a threshold, and retries once if exceededContextWindowSize is thrown. I’d appreciate feedback on whether this is a sensible use of Foundation Models APIs, especially around rebuilding a session from a compacted Transcript. GitHub: https://github.com/ricky-stone/FoundationContext

I have an app on the App Store that takes user content and creates a Generable struct out of it. In the last couple weeks I have started getting complains from my users that the part of the app leveraging FoundationModels isn't working properly. In my testing I noticed that the same request that would've worked a couple weeks ago is now getting errors with guardrails violation. I'm initializing my model this way LanguageModelSession(model: SystemLanguageModel(guardrails: .permissiveContentTransformations)) // I'm aware that .permissiveContentTransformations does not apply to Generable, but I'd really really really really love it, if it did!. This started around the iOS 26.5/macOS 26.5 releases and I wonder if there's a way to fix it.

Hi Apple Team, I'm trying to use the SkillActivation framework from the Foundation Models Utilities repository: https://github.com/apple/foundation-models-utilities Environment: Xcode 26 Beta iPadOS/macOS 26 Beta Apple Intelligence enabled Foundation Models Utilities: latest version from GitHub Issue: As soon as I import or use SkillActivation-related APIs, Xcode reports build errors and the project fails to compile. The rest of the Foundation Models framework works correctly, but the problem appears specifically when SkillActivation is added. Steps to Reproduce: Create a new project. Add foundation-models-utilities via Swift Package Manager. Import SkillActivation / follow the sample implementation. Build the project. Expected Result: The project should compile successfully and SkillActivation should be available. Actual Result: Xcode reports compilation errors and the build fails. Questions: Is there any additional entitlement, capability, or configuration required for SkillActivation? Is SkillActivation currently supported in Xcode 26 Beta? Are there any known issues with the current version of foundation-models-utilities? Thank you.

On iOS 27 Beta 1, it looks like the user must enable either "Siri"/"Hey Siri" or "Press Side Button for Siri" in iOS settings for SystemLanguageModel.default.availability to report true. Otherwise, it returns .appleIntelligenceNotEnabled. Is this expected behavior? This doesn't seem very intuitive. The user might very well want to use in-app AI functionalities without wanting to talk / press side button for Siri. Also, with the new "pull down for Siri" UX these are not the only way to interact with Siri anyway.

In the new Xcode we saw examples of Claude, OAI & Google coding agents that you can start conversations with inside your project, giving it access to your project files context. As far as I understand, this requires an API key for those models & the processing is run on Anthropic / Google servers, not locally nor on Private Cloud Compute. Is it possible to instead, use the LLM powering Foundation Models, for a “Siri Code Agent” which operates in the place of those models, but runs on device or in Private Cloud Compute? I like how this works for Siri AI requests, and would love to have a coding assistant agent that can operate in the same privacy preserving way! Is this possible with any of the open source frameworks or the command line tools? If not, what is the best way to request this feature?

Whisper and other STT APIs let you pass a custom vocabulary or initial_prompt to bias recognition toward domain-specific proper nouns. In the App Intents / Siri stack, is there an equivalent way to supply dynamic, per-user term lists — for example favorites or recently used items — to improve how spoken names are transcribed or resolved?

Is anything configurable for LanguageModelSession.Guardrails besides the default? I'm prototyping a camping app, and it's constantly slamming into guardrail errors when I use the new foundation model interface. Any subjects relating to fishing, survival, etc. won't generate. For example the prompt "How can I kill deer ticks using a clothing treatment?" returns a generation error. The results that I get are great when it works, but so far the local model sessions are extremely unreliable.

What is the proper way to intercept tool calls modify them or dynamically approve/reject them?

We understand that on-device Foundation Models (FMs) can evolve between OS releases. To help us accurately scope our application capabilities and performance expectations for multiplatform development, could you clarify the variation of these new on-device models across different hardware? Specifically: Within the same OS & device family: Do the architecture, parameters, or capabilities of the on-device models vary based on hardware tiers (e.g., iPhone vs. iPhone Pro, or MacBook Air M5 vs. MacBook Pro with M5 Pro)? Across different device form factors: Are there model variations between hardware families running equivalent OS releases (e.g., Mac vs. iPhone)? Knowing if we are targeting a uniform model baseline or a tiered model ecosystem will greatly help us optimise our App Intelligence features or at least set us with proper expectations in scope and capabilities. Thanks.

I am trying out the new PrivateCloudComputeLanguageModel but can't get it to work. When I call session.respond it throws the following error: Error Domain=FoundationModels.LanguageModelError Code=-1 "The operation couldn’t be completed. (FoundationModels.LanguageModelError error -1.)" UserInfo={NSMultipleUnderlyingErrorsKey=( "Error Domain=FoundationModels.LanguageModelError Code=-1 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n \"Error Domain=ModelManagerServices.ModelManagerError Code=1046 \\\"(null)\\\" UserInfo={NSMultipleUnderlyingErrorsKey=(\\n)}\"\n)}" ), NSLocalizedDescription=The operation couldn’t be completed. (FoundationModels.LanguageModelError error -1.)} Maybe error code 1046 means something, but I can't find a mention of it in the docs. My set-up: macOS Golden Gate on a MacBook Pro M1 Xcode 27.0.0 beta 1, calling the model as part of a test on a iPhone 17 simulator with iOS 27 beta 1. I do have the Private Cloud Compute entitlement (removing it triggers a fatalError). model.isAvailable returns true. I tried logging into iCloud on both the macOS host as well as the simulator, but no difference. Relevant code: calling the model: https://github.com/Thomvis/Construct/blob/feature/foundation-models/Sources/MechMuse/FoundationModels.swift#L20 the test to run to trigger the issue: https://github.com/Thomvis/Construct/blob/feature/foundation-models/App/UnitTests/DescribeCombatantsEvaluation.swift#L78

Given that Foundation Models focus on native Swift APIs, is there any supported bridge for a WebKit-based app to access the Language Model Protocol?

I work on an app called one sec which helps people reduce the amount of time they spend on social media by interrupting app openings with Shortcuts automations. With the release of iOS 26, we added a new Conversational Reflection interruption, a feature backed by Foundation Models. The user talks through their reasoning for wanting to use social media. A significant fraction of our users suffer from ADHD, ADD, and struggle with mental health. As a result, we try to show crisis support banners in our conversation UI. We do so with structured outputs, asking the language model If the user appears to be in deep distress. However, often times the guardrails are triggered and we don’t receive the response from the language model, meaning we can’t support our users and show them related resources if needed. We’d love to see more specific guardrails errors introduced in the framework to better support our users. Here’s a radar with further details: FB20828230 Thank you!

Apple demonstrated agentic behavior (e.g., the Passwords app changing credentials on the user's behalf), and Siri AI can now take systemwide actions in apps. Is there a first-class confirmation API for App Intents — a way to mark an action as requiring explicit user approval before execution, with a standard confirmation surface — or must developers build their own confirmation UI inside the intent? For irreversible or high-impact actions, what is Apple's recommended pattern to prevent the model from executing them autonomously, and can an intent declare a risk/sensitivity level the system respects? When Siri AI invokes an action, what authentication/authorization context is available to the intent (biometric gate, user-presence assertion), and how should an app require step-up auth for sensitive operations? Is there a supported audit trail for actions taken via Siri AI on the user's behalf, so an app can show the user what was done and when? How does the system handle an action that fails or partially completes during an agentic, multi-step flow?