Dear Apple Foundation Models Development Team, I am a developer integrating Apple Foundation Models (AFM) into my app and encountered the exceededContextWindowSize error when exceeding the 4096-token limit. Proposal: I suggest Apple develop a tool to estimate the token count of a prompt before sending it to the model. This tool could be integrated into FoundationModels Framework for ease of use. Benefits: A token estimation tool would help developers manage the context window limit and optimize performance. I hope Apple considers this proposal soon. Thank you!

Was just wondering why the foundation model documentation is no longer available, thanks! https://developer.apple.com/documentation/FoundationModels

Due to our min iOS version, this is my first time using .xcstrings instead of .strings for AppShortcuts. When using the migrate .strings to .xcstrings Xcode context menu option, an .xcstrings catalog is produced that, as expected, has each invocation phrase as a separate string key. However, after compilation, the catalog changes to group all invocation phrases under the first phrase listed for each intent (see attached screenshot). It is possible to hover in blank space on the right and add more translations, but there is no 1:1 key matching requirement to the phrases on the left nor a requirement that there are the same number of keys in one language vs. another. (The lines just happen to align due to my window size.) What does that mean, practically? Do all sub-phrases in each language in AppShortcuts.xcstrings get processed during compilation, even if there isn't an equivalent phrase key declared in the AppShortcut (e.g., the ja translation has more phrases than the English)? (That makes some logical sense, as these phrases need not be 1:1 across languages.) In the AppShortcut declaration, if I delete all but the top invocation phrase, does nothing change with Siri? Is there something I'm doing incorrectly? struct WatchShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { AppShortcut( intent: QuickAddWaterIntent(), phrases: [ "\(.applicationName) log water", "\(.applicationName) log my water", "Log water in \(.applicationName)", "Log my water in \(.applicationName)", "Log a bottle of water in \(.applicationName)", ], shortTitle: "Log Water", systemImageName: "drop.fill" ) } }

I'm a bit new to the LLM stuff and with Foundation Models. My understanding is that there is a token limit of around 4K. I want to process the contents of files which may be quite large. I first tried going the Tool route but that didn't work out so I then tried manually chunking the text to keep things under the limit. It mostly works except that every now and then it'll exceed the limit. This happens even when the chunks are less than 100 characters. Instructions themselves are about 500 characters but still overall, well below 1000 characters per prompt, all told, which, in my limited understanding, should not result in 4K tokens being parsed. Any ideas on what is going on here?

I have a fairly basic prompt I've created that parses a list of locations out of a string. I've then created a tool, which for these locations, finds their latitude/longitude on a map and populates that in the response. However, I cannot get the language model session to see/use my tool. I have code like this passing the tool to my prompt: class Parser { func populate(locations: String, latitude: Double, longitude: Double) async { let findLatLonTool = FindLatLonTool(latitude: latitude, longitude: longitude) let session = LanguageModelSession(tools: [findLatLonTool]) { """ A prompt that populates a model with a list of locations. """ """ Use the findLatLon tool to populate the latitude and longitude for the name of each location. """ } let stream = session.streamResponse(to: "Parse these locations: \(locations)", generating: ParsedLocations.self) let locationsModel = LocationsModels(); do { for try await partialParsedLocations in stream { locationsModel.parsedLocations = partialParsedLocations.content } } catch { print("Error parsing") } } } And then the tool that looks something like this: import Foundation import FoundationModels import MapKit struct FindLatLonTool: Tool { typealias Output = GeneratedContent let name = "findLatLon" let description = "Find the latitude / longitude of a location for a place name." let latitude: Double let longitude: Double @Generable struct Arguments { @Guide(description: "This is the location name to look up.") let locationName: String } func call(arguments: Arguments) async throws -> GeneratedContent { let request = MKLocalSearch.Request() request.naturalLanguageQuery = arguments.locationName request.region = MKCoordinateRegion( center: CLLocationCoordinate2D(latitude: latitude, longitude: longitude), latitudinalMeters: 1_000_000, longitudinalMeters: 1_000_000 ) let search = MKLocalSearch(request: request) let coordinate = try await search.start().mapItems.first?.location.coordinate if let coordinate = coordinate { return GeneratedContent( LatLonModel(latitude: coordinate.latitude, longitude: coordinate.longitude) ) } return GeneratedContent("Location was not found - no latitude / longitude is available.") } } But trying a bunch of different prompts has not triggered the tool - instead, what appear to be totally random locations are filled in my resulting model and at no point does a breakpoint hit my tool code. Has anybody successfully gotten a tool to be called?

I keep getting the error “An unsupported language or locale was used.” Is there any documentation that specifies the accepted languages or locales in Foundation model?

Hello, My app fully relies on the new Foundation Models. Since Foundation Models require Apple Intelligence, I want to ensure that only devices capable of running Apple Intelligence can install my app. When checking the UIRequiredDeviceCapabilities property for a suitable value, I found that iphone-performance-gaming-tier seems the closest match. Based on my research: On iPhone, this effectively limits installation to iPhone 15 Pro or later. On iPad, it ensures M1 or newer devices. This exactly matches the hardware requirements for Apple Intelligence. However, after setting iphone-performance-gaming-tier, I noticed that on iPad, Game Mode (Game Overlay) is automatically activated, and my app is treated as a game. My questions are: Is there a more appropriate UIRequiredDeviceCapabilities value that would enforce the same Apple Intelligence hardware requirements without triggering Game Mode? If not, is there another way to restrict installation to devices meeting Apple Intelligence requirements? Is there a way to prevent Game Mode from appearing for my app while still using this capability restriction? Thanks in advance for your help.

We are really excited to have introduced the Foundation Models framework in WWDC25. When using the framework, you might have feedback about how it can better fit your use cases. Starting in macOS/iOS 26 Beta 4, the best way to provide feedback is to use #Playground in Xcode. To do so: In Xcode, create a playground using #Playground. Fore more information, see Running code snippets using the playground macro. Reproduce the issue by setting up a session and generating a response with your prompt. In the canvas on the right, click the thumbs-up icon to the right of the response. Follow the instructions on the pop-up window and submit your feedback by clicking Share with Apple. Another way to provide your feedback is to file a feedback report with relevant details. Specific to the Foundation Models framework, it’s super important to add the following information in your report: Language model feedback This feedback contains the session transcript, including the instructions, the prompts, the responses, etc. Without that, we can’t reason the model’s behavior, and hence can hardly take any action. Use logFeedbackAttachment(sentiment:issues:desiredOutput: ) to retrieve the feedback data of your current model session, as shown in the usage example, write the data into a file, and then attach the file to your feedback report. If you believe what you’d report is related to the system configuration, please capture a sysdiagnose and attach it to your feedback report as well. The framework is still new. Your actionable feedback helps us evolve the framework quickly, and we appreciate that. Thanks, The Foundation Models framework team

I'm building a new feature with Visual Intelligence framework. My implementation for IndexedEntity and IntentValueQuery worked as expected and I can see a list of objects in visual search result. However, my OpenIntent doesn't work. When I tap on the object, I got a message on screen "Sorry somethinf went wrong ...". and the breakpoint in perform() is never triggered. Things I've tried: I added @MainActor before perform(), this didn't change anything I set static let openAppWhenRun: Bool = true and static var supportedModes: IntentModes = [.foreground(.immediate)], still nothing I created a different intent for the see more button at the end of feed. This AppIntent with schema: .visualIntelligence.semanticContentSearch worked, perform() is executed

Whenever I try to initialize a LanguageModelSession (let session = LanguageModelSession()), my app crashes with EXC_BAD_ACCESS. SystemLanguageModel.default.availability returns available. I tried running the two sample projects I found that use Foundation Models, FoundationModelsTripPlanner and SwiftTranscriptionSampleApp, and they both also crash—immediately on launch. I commented out the Foundation Models logic from the SwiftTranscriptionSampleApp and ran it again, and it no longer crashed. I'm on macOS 26 Beta 4 on an M1 Pro device. I'm based in Austria (EU), if that matters.

Encountered a few times when the answer get "stuck" (I am now at beta 6). This is an example.

Hi all, I noticed on Friday that on the new Beta 5 using FoundationModels on a simulator LanguageModelSession.respond() neither resolves nor throws most of the time. The SwiftUI test app below was working perfectly in Xcode 16 Beta 4 and iOS 26 Beta 4 (simulator). import SwiftUI import FoundationModels struct ContentView: View { var body: some View { VStack { Image(systemName: "globe") .imageScale(.large) .foregroundStyle(.tint) Text("Hello, world!") } .padding() .onAppear { Task { do { let session = LanguageModelSession() let response = try await session.respond(to: "are cats better than dogs ???") print(response.content) } catch { print("error") } } } } } After updating to Xcode 16 Beta 5 and iOS 26 Beta 5 (simulator), the code now often hangs. Occasionally it will work if I toggle Apple Intelligence on and off in Settings, but it’s unreliable.

I can no longer achieve 100% ANE usage since upgrading to MacOS26 Beta 5. I used to be able to get 100%. Has Apple activated throttling or power saving features in the new Betas? Is there any new rate limiting on the API? I can hardly get above 3w or 40%. I have a M4 Pro mini (64GB) with High Power energy setting. MacOS 26 Beta 5.

I'm running MacOs 26 Beta 5. I noticed that I can no longer achieve 100% usage on the ANE as I could before with Apple Foundations on-device model. Has Apple activated some kind of throttling or power limiting of the ANE? I cannot get above 3w or 40% usage now since upgrading. I'm on the high power energy mode. I there an API rate limit being applied? I kave a M4 Pro mini with 64 GB of memory.

I'm using Xcode 26 Beta 5 and get errors on any generation I try, however harmless, when wrapped in the #Playground macro. #Playground { let session = LanguageModelSession() let topic = "pandas" let prompt = "Write a safe and respectful story about (topic)." let response = try await session.respond(to: prompt) Not seeing any issues on simulator or device. Anyone else seeing this or have any ideas? Thanks for any help! Version 26.0 beta 5 (17A5295f) macOS 26.0 Beta (25A5316i)

Hey, I've been trying to write an AI agent for OpenAI's GPT-5, but using the @Generable Tool types from the FoundationModels framework, which is super awesome btw! I'm having trouble implementing the tool calling, though. When I receive a tool call from the OpenAI api, I do the following: Find the tool in my [any Tool] array via the tool name I get from the model if let tool = tools.first(where: { $0.name == functionCall.name }) { // ... } Parse the arguments of the tool call via GeneratedContent(json:) let generatedContent = try GeneratedContent(json: functionCall.arguments) Pass the tool and arguments to a function that calls tool.call(arguments: arguments) and returns the tool's output type private func execute<T: Tool>(_ tool: T, with generatedContent: GeneratedContent) async throws -> T.Output { let arguments = try T.Arguments.init(generatedContent) return try await tool.call(arguments: arguments) } Up to this point, everything is working as expected. However, the tool's output type is any PromptRepresentable and I have no idea how to turn that into something that I can encode and send back to the model. I assumed there might be a way to turn it into a GeneratedContent but there is no fitting initializer. Am I missing something or is this not supported? Without a way to return the output to an external provider, it wouldn't really be possible to use FoundationModels Tool type I think. That would be unfortunate because it's implemented so elegantly. Thanks!

Is there any way to ensure iOS apps we develop using Foundation Models can only be purchasable/downloadable on App Store by folks with capable devices? I would've thought there would be a Required Capabilities that App Store would hook into, but I don't seem to see it in the documentation here: https://developer.apple.com/documentation/bundleresources/information-property-list/uirequireddevicecapabilities The closest seems to be iphone-performance-gaming-tier as that seems to target all M1 and above chips on iPhone & iPad. There is an ipad-minimum-performance-m1 that would more reasonably seem to ensure Foundation Models is likely available, but that doesn't help with iPhone. So far, it seems the only path would be to set Minimum Deployment to iOS 26 and add iphone-performance-gaming-tier as a required capability, but I'm a bit worried that capability might diverge in the future from what's Foundation Model / Apple Intelligence capable. While I understand for the majority of apps they'll want to just selectively add in Apple Intelligence features and so can be usable by folks whose devices don't support it, the app experience I'm building doesn't make sense without the Foundation Models being available and I'd rather not have a large number of users downloading the app to be told "Sorry, you're not Apple Intelligence capable"

My sample app has been working with the following code: func call(arguments: Arguments) async throws -&gt; ToolOutput { var temp:Int switch arguments.city { case .singapore: temp = Int.random(in: 30..&lt;40) case .china: temp = Int.random(in: 10..&lt;30) } let content = GeneratedContent(temp) let output = ToolOutput(content) return output } However in 26 beta 5, ToolOutput no longer available, please advice what has changed.

Context I trained a LoRA adapter for Apple’s on-device language model using the Foundation Models Adapter Training Toolkit v0.2.0 on macOS 26 beta 4. Although training completes successfully, loading the resulting .fmadapter package fails with: Adapter is not compatible with the current system base model. What I’ve Observed, Hard-coded Signature: In export/constants.py, the toolkit sets, BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Metadata Injection: The export_fmadapter.py script writes this value into the adapter’s metadata: self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE Compatibility Check: At runtime, the Foundation Models framework compares the adapter’s baseModelSignature against the OS’s system model signature, and reports compatibleAdapterNotFound if they don’t match—without revealing the expected signature. Questions Signature Generation - What exactly does the toolkit hash to derive BASE_SIGNATURE? Is it a straight SHA-1 of base-model.pt, or is there an additional transformation? Recomputing for Beta 4 - Is there a way to locally compute the correct signature for the macOS 26 beta 4 system model? Toolkit Updates - Will Apple release Adapter Training Toolkit v0.3.0 with an updated BASE_SIGNATURE for beta 4, or is there an alternative workaround to generate it myself? Any guidance on how the Foundation Models framework derives and verifies the base model signature—or how to regenerate it for beta 4—would be greatly appreciated.

Is it possible to expose a custom VirtIO device to a Linux guest running inside a VM — likely using QEMU backed by Hypervisor.framework. The guest would see this device as something like /dev/npu0, and it would use a kernel driver + userspace library to submit inference requests. On the macOS host, these requests would be executed using CoreML, MPSGraph, or BNNS. The results would be passed back to the guest via IPC. Does the macOS allow this kind of "fake" NPU / GPU