At WWDC25 we launched a new type of Lab event for the developer community - Group Labs. A Group Lab is a panel Q&A designed for a large audience of developers. Group Labs are a unique opportunity for the community to submit questions directly to a panel of Apple engineers and designers. Here are the highlights from the WWDC25 Group Lab for Apple Intelligence. Can I integrate writing tools in my own text editor? UITextView, NSTextView, and SwiftUI TextEditor automatically get Writing Tools on devices that support Apple Intelligence. For custom text editors, check out Enhancing your custom text engine with Writing Tools. Given that Foundation Models are on-device, how will Apple update the models over time? And how should we test our app against the model updates? Model updates are in sync with OS updates. As for testing with updated models, watch our WWDC session about prompt engineering and safety, and read the Human Interface Guidelines to understand best practices in prompting the on-device model. What is the context size of a session in Foundation Models Framework? How to handle the error if a session runs out of the context size? Currently the context size is about 4,000 tokens. If it’s exceeded, developers can catch the .exceededContextWindowSize error at runtime. As discussed in one of our WWDC25 sessions, when the context window is exceeded, one approach is to trim and summarize a transcript, and then start a new session. Can I do image generation using the Foundation Models Framework or is only text generation supported? Foundation Models do not generate images, but you can use the Foundation Models framework to generate prompts for ImageCreator in the Image Playground framework. Developers can also take advantage of Tools in Foundation Models framework, if appropriate for their app. My app currently uses a third party server-based LLM. Can I use the Foundation Models Framework as well in the same app? Any guidance here? The Foundation Models framework is optimized for a subset of tasks like summarization, extraction, classification, and tagging. It’s also on-device, private, and free. But at 3 billion parameters it isn’t designed for advanced reasoning or world knowledge, so for some tasks you may still want to use a larger server-based model. Should I use the AFM for my language translation features given it does text translation, or is the Translation API still the preferred approach? The Translation API is still preferred. Foundation Models is great for tasks like text summarization and data generation. It’s not recommended for general world knowledge or translation tasks. Will the TranslationSession class introduced in ios18 get any new improvments in performance or reliability with the new live translation abilities in ios/macos/ipados 26? Essentially, will we get access to live translation in a similar way and if so, how? There's new API in LiveCommunicationKit to take advantage of live translation in your communication apps. The Translate framework is using the same models as used by Live Communication and can be combined with the new SpeechAnalyzer API to translate your own audio. How do I set a default value for an App Intent parameter that is otherwise required? You can implement a default value as part of your parameter declaration by using the @Parameter(defaultValue:) form of the property wrapper. How long can an App Intent run? On macOS there is no limit to how long app intents can run. On iOS, there is a limit of 30 seconds. This time limit is paused when waiting for user interaction. How do I vary the options for a specific parameter of an App Intent, not just based on the type? Implement a DynamicOptionsProvider on that parameter. You can add suggestedEntities() to suggest options. What if there is not a schema available for what my app is doing? If an app intent schema matching your app’s functionality isn’t available, take a look to see if there’s a SiriKit domain that meets your needs, such as for media playback and messaging apps. If your app’s functionality doesn’t match any of the available schemas, you can define a custom app intent, and integrate it with Siri by making it an App Shortcut. Please file enhancement requests via Feedback Assistant for new App intent schemas that would benefit your app. Are you adding any new app intent domains this year? In addition to all the app intent domains we announced last year, this year at WWDC25 we announced that Visual Intelligence will be added to iOS 26 and macOS Tahoe. When my App Intent doesn't show up as an action in Shortcuts, where do I start in figuring out what went wrong? App Intents are statically extracted. You can check the ExtractMetadata info in Xcode's build log. What do I need to do to make sure my App Intents work well with Spotlight+? Check out our WWDC25 sessions on App Intents, including Explore new advances in App Intents and Develop for Shortcuts and Spotlight with App Intents. Mostly, make sure that your intent can run from the parameter summary alone, no required parameters without default values that are not already in the parameter summary. Does Spotlight+ on macOS support App Shortcuts? Not directly, but it shows the App Intents your App Shortcuts are sitting on top of. I’m wondering if the on-device Foundation Models framework API can be integrated into an app to act strictly as an app in-universe AI assistant, responding only within the boundaries of the app’s fictional context. Is such controlled, context-limited interaction supported? FM API runs inside the process of your app only and does not automatically integrate with any remaining part of the system (unless you choose to implement your own tool and utilize tool calling). You can provide any instructions and prompts you want to the model. If a country does not support Apple Intelligence yet, can the Foundation Models framework work? FM API works on Apple Intelligence-enabled devices in supported regions and won’t work in regions where Apple Intelligence is not yet supported

Hello, I have a CoreML model and I want to convert it to a PyTorch model. Any ideas if this is possible and if so how?

Almost everywhere else you see Apple Intelligence, you get to select whether it's on device, private cloud compute, or ChatGPT. Is there a way to do that via code in the Foundation Model? I searched through the docs and couldn't find anything, but maybe I missed it.

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?

Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!

I'm the creator of an app that helps users learn Arabic. Inside of the app users can save words, engage in lessons specific to certain grammar concepts etc. I'm looking for a way for Siri to 'suggest' my app when the user asks to define any Arabic words. There are other questions that I would like for Siri to suggest my app for, but I figure that's a good start. What framework am I looking for here? I think AppItents? I remember I played with it for a bit last year but didn't get far. Any suggestions would be great. Would the new Foundations model be any help here?

Hello everybody! I’m encountering an unexpected guardrailViolation error when using Foundation Models on macOS Beta 3 (Tahoe) with an Apple M2 Pro chip. This issue didn’t occur on Beta 1 or Beta 2 using the same codebase. Reproduction Context I’m developing an app that leverages Foundation Models for structured generation, paired with a local database tool. After upgrading to macOS Beta 3, I started receiving this error consistently, despite no changes in the generation logic. To isolate the issue, I opened the official WWDC sample project from the Adding intelligent app features with generative models and the same guardrailViolation error appeared without any modifications. Simplified Working Example I attempted to narrow down the issue by starting with a minimal prompt structure. This basic case works fine: import Foundation import Playgrounds import FoundationModels @Generable struct GeneableLandmark { @Guide(description: "Name of the landmark to visit") var name: String } final class LandmarkSuggestionGenerator { var landmarkSuggestion: GeneableLandmark.PartiallyGenerated? private var session: LanguageModelSession init(){ self.session = LanguageModelSession( instructions: Instructions { """ generate a list of landmarks to visit """ } ) } func createLandmarkSuggestion(location: String) async throws { let stream = session.streamResponse( generating: GeneableLandmark.self, options: GenerationOptions(sampling: .greedy), includeSchemaInPrompt: false ) { """ Generate a list of landmarks to viist in \(location) """ } for try await partialResponse in stream { landmarkSuggestion = partialResponse } } } #Playground { let generator = LandmarkSuggestionGenerator() Task { do { try await generator.createLandmarkSuggestion(location: "New york") if let suggestion = generator.landmarkSuggestion { print("Suggested landmark: \(suggestion)") } else { print("No suggestion generated.") } } catch { print("Error generating landmark suggestion: \(error)") } } } But as soon as I use the Sample ItineraryPlanner: #Playground { // Example landmark for demonstration let exampleLandmark = Landmark( id: 1, name: "San Francisco", continent: "North America", description: "A vibrant city by the bay known for the Golden Gate Bridge.", shortDescription: "Iconic Californian city.", latitude: 37.7749, longitude: -122.4194, span: 0.2, placeID: nil ) let planner = ItineraryPlanner(landmark: exampleLandmark) Task { do { try await planner.suggestItinerary(dayCount: 3) if let itinerary = planner.itinerary { print("Suggested itinerary: \(itinerary)") } else { print("No itinerary generated.") } } catch { print("Error generating itinerary: \(error)") } } } The error pops up: Multiline Error generating itinerary: guardrailViolation(FoundationModels.LanguageModelSession. >GenerationError.Context(debug Description: "May contain sensitive or unsafe content", >underlyingErrors: [FoundationModels. LanguageModelSession. Gene >rationError.guardrailViolation(FoundationMo dels. >LanguageModelSession.GenerationError.C ontext (debugDescription: >"May contain unsafe content", underlyingErrors: []))])) Based on my tests: The error may not be tied to structure complexity (since more nested structures work) The issue may stem from the tools or prompt content used inside the ItineraryPlanner The guardrail sensitivity may have increased or changed in Beta 3, affecting models that worked in earlier betas Thank you in advance for your help. Let me know if more details or reproducible code samples are needed - I’m happy to provide them. Best, Sasha Morozov

Just tried to write a very simple test of using foundation models, but it gave me the error like this "ModelManager received unentitled request. Expected entitlement com.apple.modelmanager.inference establishment of session failed with Missing entitlement: com.apple.modelmanager.inference" The simple code is listed below: let session: LanguageModelSession = LanguageModelSession() let response = try? await session.respond(to: "What is the capital of France?") print("Response: (response)") So what's the problem of this one?

About the Core ML model encryption mention in:https://developer.apple.com/documentation/coreml/encrypting-a-model-in-your-app Ｗhen I encrypted the model, if the machine is M chip, the model will load perfectly. One the other hand, when I test the executable on an Intel chip macbook, there will be an error: Error Domain=com.apple.CoreML Code=9 "Operation not supported on this platform." UserInfo={NSLocalizedDescription=Operation not supported on this platform.} Intel test machine is 2019 macbook air with CPU: Intel i5-8210Y, OS: 14.7.6 23H626, With Apple T2 Security Chip. The encrypted model do load on M2 and M4 macbook air. If the model is NOT encrypted, it will also load on the Intel test machine. I did not find in Core ML document that suggest if the encryption/decryption support Intel chips. May I check if the decryption indeed does NOT support Intel chip?

Based on the documentation, it appears that MLTensor can be used to perform tensor operations using the ANE (Apple Neural Engine) by wrapping the tensor operations with withMLTensorComputePolicy with a MLComputePolicy initialized with MLComputeUnits.cpuAndNeuralEngine (it can also be initialized with MLComputeUnits.all to let the OS spread the load between the Neural Engine, GPU and CPU). However, when using the Instruments app, it appears that the tensor operations never get executed on the Neural Engine. It would be helpful if someone can guide me on the correct way to ensure that the Nerual Engine is used to perform the tensor operations (not as part of a CoreML model file). based on this example, I've created a simple code to try it: import Foundation import CoreML print("Starting...") let semaphore = DispatchSemaphore(value: 0) Task { await withMLTensorComputePolicy(.init(MLComputeUnits.cpuAndNeuralEngine)) { let v1 = MLTensor([1.0, 2.0, 3.0, 4.0]) let v2 = MLTensor([5.0, 6.0, 7.0, 8.0]) let v3 = v1.matmul(v2) await v3.shapedArray(of: Float.self) // is 70.0 let m1 = MLTensor(shape: [2, 3], scalars: [ 1, 2, 3, 4, 5, 6 ], scalarType: Float.self) let m2 = MLTensor(shape: [3, 2], scalars: [ 7, 8, 9, 10, 11, 12 ], scalarType: Float.self) let m3 = m1.matmul(m2) let result = await m3.shapedArray(of: Float.self) // is [[58, 64], [139, 154]] // Supports broadcasting let m4 = MLTensor(randomNormal: [3, 1, 1, 4], scalarType: Float.self) let m5 = MLTensor(randomNormal: [4, 2], scalarType: Float.self) let m6 = m4.matmul(m5) print("Done") return result; } semaphore.signal() } semaphore.wait() Here's what I get on the Instruments app: Notice how the Neural Engine line shows no usage. Ive run this test on an M1 Max MacBook Pro.

I couldn't find information about this in the documentation. Could someone clarify if this API is available and how to access it?

Hi, I am developing an iOS application that utilizes Apple’s Foundation Models to perform certain summarization tasks. I would like to understand whether user data is transferred to Private Cloud Compute (PCC) in cases where the computation cannot be performed entirely on-device. This information is critical for our internal security and compliance reviews. I would appreciate your clarification on this matter. Thank you.

When context window size exceeded, this error is not called (instead another error has shown up) to handle new session. LanguageModelSession.GenerationError.exceededContextWindowSize Or am I doing things wrong?

It is vital for Apple to refine its OCR models to correctly distinguish between Khmer and Thai scripts. Incorrectly labeling Khmer text as Thai is more than a technical bug; it is a culturally insensitive error that impacts national identity, especially given the current geopolitical climate between Cambodia and Thailand. Implementing a more robust language-detection threshold would prevent these harmful misidentifications. There is a significant logic flaw in the VNRecognizeTextRequest language detection when processing Khmer script. When the property automaticallyDetectsLanguage is set to true, the Vision framework frequently misidentifies Khmer characters as Thai. While both scripts share historical roots, they are distinct languages with different alphabets. Currently, the model’s confidence threshold for distinguishing between these two scripts is too low, leading to incorrect OCR output in both developer-facing APIs and Apple’s native ecosystem (Preview, Live Text, and Photos). import SwiftUI import Vision class TextExtractor { func extractText(from data: Data, completion: @escaping (String) -> Void) { let request = VNRecognizeTextRequest { (request, error) in guard let observations = request.results as? [VNRecognizedTextObservation] else { completion("No text found.") return } let recognizedStrings = observations.compactMap { observation in let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } completion(recognizedStrings.joined(separator: "\n")) } request.automaticallyDetectsLanguage = true // <-- This is the issue. request.recognitionLevel = .accurate let handler = VNImageRequestHandler(data: data, options: [:]) DispatchQueue.global(qos: .background).async { do { try handler.perform([request]) } catch { completion("Failed to perform OCR: \(error.localizedDescription)") } } } } Recognizing Khmer Confidence Score is low for Khmer text. (The output is in Thai language with low confidence score) Recognizing English Confidence Score is high expected. Recognizing Thai Confidence Score is high as expected Issues on Preview, Photos Khmer text Copied text Kouk Pring Chroum Temple [19121 รอาสายสุกตีนานยารรีสใหิสรราภูชิตีนนสุฐตีย์ [รุก เผือชิษาธอยกัตธ์ตายตราพาษชาณา ถวเชยาใบสราเบรถทีมูสินตราพาษชาณา ทีมูโษา เช็ก อาษเชิษฐอารายสุกบดตพรธุรฯ ตากร"สุก"ผาตากรธกรธุกเยากสเผาพศฐตาสาย รัอรณาษ"ตีพย" สเผาพกรกฐาภูชิสาเครๆผู:สุกรตีพาสเผาพสรอสายใผิตรรารตีพสๆ เดียอลายสุกตีน ธาราชรติ ธิพรหณาะพูชุบละเาหLunet De Lajonquiere ผารูกรสาราพารผรผาสิตภพ ตารสิทูก ธิพิ คุณที่นสายเระพบพเคเผาหนารเกะทรนภาษเราภุพเสารเราษทีเลิกสญาเราหรุฬารชสเกาก เรากุม สงสอบานตรเราะากกต่ายภากายระตารุกเตียน Recommended Solutions 1. Set a Threshold Filter out the detected result where the threshold is less than or equal to 0.5, so that it would not output low quality text which can lead to the issue. For example, let recognizedStrings = observations.compactMap { observation in if observation.confidence <= 0.5 { return nil } let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } 2. Add Khmer Language Support This issue would never happen if the model has the capability to detect and recognize image with Khmer language. Doc2Text GitHub: https://github.com/seanghay/Doc2Text-Swift

I'm experimenting with Foundation Models and I'm trying to understand how to define a Tool whose input argument is defined at runtime. Specifically, I want a Tool that takes a single String parameter that can only take certain values defined at runtime. I think my question is basically the same as this one: https://developer.apple.com/forums/thread/793471 However, the answer provided by the engineer doesn't actually demonstrate how to create the GenerationSchema. Trying to piece things together from the documentation that the engineer linked to, I came up with this: let citiesDefinedAtRuntime = ["London", "New York", "Paris"] let citySchema = DynamicGenerationSchema( name: "CityList", properties: [ DynamicGenerationSchema.Property( name: "city", schema: DynamicGenerationSchema( name: "city", anyOf: citiesDefinedAtRuntime ) ) ] ) let generationSchema = try GenerationSchema(root: citySchema, dependencies: []) let tools = [CityInfo(parameters: generationSchema)] let session = LanguageModelSession(tools: tools, instructions: "...") With the CityInfo Tool defined like this: struct CityInfo: Tool { let name: String = "getCityInfo" let description: String = "Get information about a city." let parameters: GenerationSchema func call(arguments: GeneratedContent) throws -> String { let cityName = try arguments.value(String.self, forProperty: "city") print("Requested info about \(cityName)") let cityInfo = getCityInfo(for: cityName) return cityInfo } func getCityInfo(for city: String) -> String { // some backend that provides the info } } This compiles and usually seems to work. However, sometimes the model will try to request info about a city that is not in citiesDefinedAtRuntime. For example, if I prompt the model with "I want to travel to Tokyo in Japan, can you tell me about this city?", the model will try to request info about Tokyo, even though this is not in the citiesDefinedAtRuntime array. My understanding is that this should not be possible – constrained generation should only allow the LLM to generate an input argument from the list of cities defined in the schema. Am I missing something here or overcomplicating things? What's the correct way to make sure the LLM can only call a Tool with an input parameter from a set of possible values defined at runtime? Many thanks!

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.

When I use the FoundationModel framework to generate long text, it will always hit an error. "Passing along Client rate limit exceeded, try again later in response to ExecuteRequest" And stop generating. eg. for the prompt "Write a long story", it will almost certainly hit that error after 17 seconds of generation. do{ let session = LanguageModelSession() let prompt: String = "Write a long story" let response = try await session.respond(to: prompt) }catch{} If possible, I want to know how to prevent that error or at least how to handle it.

We are developing Apple AI for overseas markets and adapting it for iPhone 17 and later models. When the system language and Siri language do not match—such as the system being in English while Siri is in Chinese—it may result in Apple AI being unusable. So, I would like to ask, how can this issue be resolved, and are there other reasons that might cause it to be unusable within the app?

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.

My app lets you create images with Image Playground. When the user approves an image I move it to the documents dir from the temp storage. With over a year of usage I’ve created a lot of images over time. Out of nowhere the app stopped loading my custom creations from Image Playground saying it couldn’t find the files. It still had my VoiceOver strings I had added for each image and still had the custom categories I assigned them. Debug code to look in the docs dir doesn’t find them. I downloaded the app’s container and only see the images I created as a test after the problem started. But my ~70MB app is still taking up 300MB on my iPhone so it feels like they’re there but not accessible. Is there anything else I can try?