According to the Tool documentation, the arguments to the tool are specified as a static struct type T, which is given to tool.call(argument: T) However, if the arguments are not known until runtime, is it possible to still create a Tool object with the proper parameters? Let's say a JSON-style dictionary is passed into the Tool init function to specify T, is this achievable?

I am experimenting with Foundation Models in my time tracking app to analyze users tracked events, but I am finding that the model struggles with even basic computation of time. Specifically converting from seconds to hours and minutes. To give just one example, when I prompt: "Convert 3672 seconds to hours, minutes, and seconds. Don't include the calculations in the resulting output" I get this: "3672 seconds is equal to 1 hour, 0 minutes, and 36 seconds". Which is clearly wrong - it should be 1 hour, 1 minute, and 12 seconds. Another issue that I saw a lot is that seconds were considered to be minutes, or that the hours were just completely off. What can I do to make the support for math better? Or is that just something that the model is not meant to be used for?

This is my code: witch SystemLanguageModel.default.availability { case .available: ContentView() .popover(isPresented: $showSettings) { SettingsView().presentationCompactAdaptation(.popover) } case .unavailable(.modelNotReady): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please come back later.")) case .unavailable(.appleIntelligenceNotEnabled): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please turn on Apple Intelligence.")) case .unavailable(.deviceNotEligible): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("This device is not eligible for Apple Intelligence.")) case .unavailable: ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark") } When I switch off Apple Intelligence, I expected "Please turn on Apple Intelligence.", but instead I get "Please come back later." This seems to be wrong error?

I'm interested in using Foundation Models to act as an AI support agent for our extensive in-app documentation. We have many pages of in-app documents, which the user can currently search, but it would be great to use Foundation Models to let the user get answers to arbitrary questions. Is this possible with the current version of Foundation Models? It seems like the way to add new context to the model is with the instructions parameter on LanguageModelSession. As I understand it, the combined instructions and prompt need to consume less than 4096 tokens. That definitely wouldn't be enough for the amount of documentation I want the agent to be able to refer to. Is there another way of doing this, maybe as a series of recursive queries? If there is a solution based on multiple queries, should I expect this to be fast enough for interactive use?

Hi, One can configure the languages of a (VN)RecognizeTextRequest with either: .automatic: language to be detected a specific language, say Spanish If the request is configured with .automatic and successfully detects Spanish, will the results be exactly equivalent compared to a request made with Spanish set as language? I could not find any information about this, and this is very important for the core architecture of my app. Thanks!

How reliable is the Models, to use as a comparison, such as a cholesterol test, to inform, for example, whether it is worth it to go see a doctor? I would like to use Tool to attach the simple blood test data to the session and with this the Model can analyse and made a simple suggestion if is necessary to see a doctor etc.. ? ps.: Local model

Testing Foundation Models framework with a health-focused recipe generation app. The on-device approach is appealing but performance is rough. Taking 20+ seconds just to get recipe name and description. Same content from Claude API: 4 seconds. I know it's beta and on-device has different tradeoffs, but this is approaching unusable territory for real-time user experience. The streaming helps psychologically but doesn't mask the underlying latency.The privacy/cost benefits are compelling but not if users abandon the feature before it completes. Anyone else seeing similar performance? Is this expected for beta, or are there optimization techniques I'm missing?

Access to VisionPro cameras is required for a research project. The project is on mixed reality software development for healthcare applications in dentistry.

Hello Apple Team, Thank you for the recent Group Lab and for your continued work on advancing Xcode and developer tools. I’d like to submit a feature request: Are there any plans to introduce support for Agentic AI Mode (MCP protocol) in future versions of iOS or Xcode? As developer tools evolve toward more intelligent and context-aware environments, the integration of agentic AI capabilities could significantly enhance productivity and unlock new creative workflows. Looking forward to your consideration, and thank you again for the excellent session. Best regards

Hi all, I'm capturing a photo using AVCapturePhotoOutput, and I've set: let photoSettings = AVCapturePhotoSettings() photoSettings.isDepthDataDeliveryEnabled = true Then I create the handler like this: let data = photo.fileDataRepresentation() let handler = try ImageRequestHandler(data: data, orientation: .right) Now I’m wondering: If depth data delivery is enabled, is it actually included and used when I pass the Data to ImageRequestHandler? Or do I need to explicitly pass the depth data using the other initializer? let handler = try ImageRequestHandler( cvPixelBuffer: photo.pixelBuffer!, depthData: photo.depthData, orientation: .right ) In short: Does ImageRequestHandler(data:) make use of embedded depth info from AVCapturePhoto.fileDataRepresentation() — or is the pixel buffer + explicit depth data required? Thanks for any clarification!

I have a mac (M4, MacBook Pro) running Tahoe 26.0 beta. I am running Xcode beta. I can run code that uses the LLM in a #Preview { }. But when I try to run the same code in the simulator, I get the 'device not ready' error and I see the following in the Settings app. Is there anything I can do to get the simulator to past this point and allowing me to test on it with Apple's LLM?

I'm developing a macOS application using the FoundationModels framework (LanguageModelSession) and encountering issues with the content sanitizer blocking legitimate text input. ** Issue Description:** The content sanitizer is flagging text strings that contain certain substrings, even when they represent legitimate technical content. For example: F_SEEL_SEX1S.wav (sE Electronics SEX1S microphone model) Technical product identifiers Serial numbers and version codes ** Broader Concern:** The content sanitizer appears to be applying restrictions that seem inappropriate for user-owned content. Even if a filename were something like "human sex.wav", users should have the right to process their own legitimate files on their own devices without content filtering interference. ** Error Messages:** SensitiveContentSettings: Sanitizer model found unsafe content in value FoundationModels.LanguageModelSession.GenerationError error 2 ** Questions:** Is there a way to disable content sanitization for processing user-owned content? 2. What's the recommended approach for applications that need to handle arbitrary user text? 3. Are there APIs to process personal content without filtering restrictions? ** Environment:** macOS 26.0 FoundationModels framework LanguageModelSession Any guidance would be appreciated.

I get the following dyld error on an iPad Pro with Xcode 26 beta 4: Symbol not found: _$s16FoundationModels20LanguageModelSessionC7prewarm12promptPrefixyAA6PromptVSg_tF Any advice?

When calling NLTagger.requestAssets with some languages, it hangs indefinitely both in the simulator and a device. This happens consistently for some languages like greek. An example call is NLTagger.requestAssets(for: .greek, tagScheme: .lemma). Other languages like french return immediately. I captured some logs from Console and found what looks like the repeated attempts to download the asset. I would expect the call to eventually terminate, either loading the asset or failing with an error.

Has Apple made any commitment to versioning the Foundation Models on device? What if you build a feature that works great on 26.0 but they change the model or guardrails in 26.1 and it breaks your feature, is your only recourse filing Feedback or pulling the feature from the app? Will there be a way to specify a model version like in all of the server based LLM provider APIs? If not, sounds risky to build on.

Hi Apple product owners. I am missing a unified concept which might be derived from the use cases for mail categories and mail spam for the app "Mail" on Mac. I need a recommendation on how to use categories in combination with the spam filter to get most out of it. So I was looking for the use cases for the 2 functionality areas in order to figure out how to organise my mails by using as much automation as possible before I start creating intelligent folders in addition. What can you recommend where I get this information from? I don't want to guess or read a lot of forum contributions which are based on guesses.

I am trying to create a slightly different version of the content tagging code in the documentation: https://developer.apple.com/documentation/foundationmodels/systemlanguagemodel/usecase/contenttagging In the playground I am getting an "Inference Provider crashed with 2:5" error. I have no idea what that means or how to address the error. Any assistance would be appreciated.

Hey, I receive GenerableContent as follows: let response = try await session.respond(to: "", schema: generationSchema) And it wraps GeneratedJSON which seems to be private. What is the best way to get a string / raw value out of it? I noticed it could theoretically be accessed via transcriptEntries but it's not ideal.

Hi everyone, I am using Xcode 16.4 in MacOS Sequoia 15.5 with Apple Intelligence turned on. The following code gives the error message in the title: import NaturalLanguage @available(iOS 18.0, *) func testSystemModel() { let model = SystemLanguageModel.default print(model) } What am I missing?

I've been successfully integrating the Foundation Models framework into my healthcare app using structured generation with @Generable schemas. While my initial testing (20-30 iterations) shows promising results, I need to validate consistency and reliability at scale before production deployment. Question Is there a recommended approach for automated, large-scale testing of Foundation Models responses? Specifically, I'm looking to: Automate 1000+ test iterations with consistent prompts and structured schemas Measure response consistency across identical inputs Validate structured output reliability (proper schema adherence, no generation failures) Collect performance metrics (TTFT, TPS) for optimization Specific Questions Framework Limitations: Are there any undocumented rate limits or thermal throttling considerations for rapid session creation/destruction? Performance Tools: Can Xcode's Foundation Models Instrument be used programmatically, or only through Instruments UI? Automation Integration: Any recommendations for integrating with testing frameworks? Session Reuse: Is it better to reuse a single LanguageModelSession or create fresh sessions for each test iteration? Use Case Context My wellness app provides medically safe activity recommendations based on user health profiles. The Foundation Models framework processes health context and generates structured recommendations for exercises, nutrition, and lifestyle activities. Given the safety implications of providing health-related guidance, I need rigorous validation to ensure the model consistently produces appropriate, well-formed recommendations across diverse user scenarios and health conditions. Has anyone in the community built similar large-scale testing infrastructure for Foundation Models? Any insights on best practices or potential pitfalls would be greatly appreciated.