Is there an API that allows iOS app developers to leverage Apple Foundation Models to authorize a user's Apple Intelligence extension, chatGPT login account? I'm trying to provide a real-time question feature for chatGPT, a logged-in extension account, while leveraging Apple Intelligence's LLM. Is there an API that also affects the extension login account?

Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49

I used Yolo5-11 and while performing great detecting balls lets say 5-10ft away in 1920 resolution and even in 640 it really is taking toll on my app performance. When I use Create ML it outputs all in 415x which is probably the reason why it does not detect objects from far. What can I do to preserve some energy ? My model is used with about 1K pictures 200 each test and validate, and from close up and far.

My iOS app supports iOS 18, and I’m using an encrypted CoreML model secured with a key generated from Xcode. Every few months (around every 3 months), the encrypted model fails to load for both me and my users. When I investigate, I find this error: coreml Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID To temporarily fix it, I delete the old key, generate a new one, re-encrypt the model, and submit an app update. This resolves the issue, but only for a while. This is a terrible experience for users and obviously not a sustainable solution. I want to understand: Why is this happening? Is there a known expiration or invalidation policy for CoreML encryption keys? How can I prevent this issue permanently? Any insights or official guidance would be really appreciated.

I have recently been having trouble with my iOS 18.2 beta update. It has been 2 weeks since I have updated to iOS 18.2 beta and joined the Genmoji and image playground waitlist. I am wondering how much longer I have to wait till my request is approved.

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.

We are evaluating various AI frameworks to use within our code, and are hoping to use some of the build-in frameworks in macOS including CoreML and Vision. However, we need to use these frameworks in a background process (system extension) that has no user session attached to it. (To be pedantic, we'll be using an XPC service that is spawned by the system extension, but neither would have an associated user session). Saying the daemon-safe frameworks list has not been updated in a while is an understatement, but it's all we have to go on. CoreGraphics isn't even listed--back then it part of ApplicationServices (I think?) and ApplicationServices is a no go. Vision does use CoreGraphics symbols and data types so I have doubts. We do have a POC that uses both frameworks and they seem to function fine but obviously having something official is better. Any Apple engineers that can comment on this?

Whats to code to warm it up once? Saw this in a developer video but cannot find it. Prevent cold run within an application. Thank you in advance!

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.

I'm working on a to-do list app that uses SpeechTranscriber and Foundation Models framework to transcribe a user's voice into text and create to-do items based off of it. After about 30 minutes looking at my code, I couldn't figure out why I was failing to generate a to-do for "I need to go to Six Flags Great America tomorrow at 3pm." It turns out, I was consistently firing the Foundation Models's safety filter violation for unsafe content ("May contain unsafe content"). Lesson learned: consider comprehensively logging Foundation Models error states to quickly identify when safety filters are unexpectedly triggered.

When I ran the following code on a physical iPhone device that supports Apple Intelligence, I encountered the following error log. What does this internal error code mean? Image generation failed with NSError in a different domain: Error Domain=ImagePlaygroundInternal.ImageGeneration.GenerationError Code=11 “(null)”, returning a generic error instead let imageCreator = try await ImageCreator() let style = imageCreator.availableStyles.first ?? .animation let stream = imageCreator.images(for: [.text("cat")], style: style, limit: 1) for try await result in stream { // error: ImagePlayground.ImageCreator.Error.creationFailed _ = result.cgImage }

Hello, I'm using videotoolbox superresolution API in MACOS 26: https://aninterestingwebsite.com/documentation/videotoolbox/vtsuperresolutionscalerconfiguration/downloadconfigurationmodel(completionhandler:)?language=objc, when using swift, it's ok, when using objective-c, I get error when downloading model with downloadConfigurationModelWithCompletionHandler: [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:MissingReference(6111)] [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:UnderlyingError(6107)_1_com.apple.MobileAssetError.Download:47] Download completion handler called with error: The operation couldnxe2x80x99t be completed. (VTFrameProcessorErrorDomain error -19743.)

We're in the process of migrating our app's custom intents from the older SiriKit Custom Intents framework to App Intents. The migration has been straightforward for our app-specific actions, and we appreciate the improved discoverability and Apple Intelligence integration that App Intents provides. However, we also implement SiriKit domain intents for calling and messaging: INStartCallIntent / INStartCallIntentHandling INSendMessageIntent / INSendMessageIntentHandling These require us to maintain an Intents Extension to handle contact resolution and the actual call/message operations. Our questions: Is there a planned App Intents equivalent for these SiriKit domains (calling, messaging), or is the Intents Extension approach still the recommended path? If we want to support phrases like "Call [contact] on [AppName]" or "Send a message to [contact] on [AppName]" with Apple Intelligence integration, is there any way to achieve this with App Intents today? Are there any WWDC sessions or documentation we may have missed that addresses the migration path for SiriKit domain intents? What we've reviewed: "Migrate custom intents to App Intents" Tech Talk "Bring your app's core features to users with App Intents" (WWDC24) App Intents documentation These resources clearly explain custom intent migration but don't seem to address the system domain intents. Our current understanding: Based on our research, it appears SiriKit domain intents should remain on the older framework, while custom intents should migrate to App Intents. We'd like to confirm this is correct and understand if there's a future direction we should be planning for. Thank you!

I am writing an app that parses text and conducts some actions. I don't want to give too much away ;) However, I am having a huge problem with token sizes. LanguageModelSession will of course give me the on device model 4096 available, but when you go over 4096, my code doesn't seem to be falling back to PCC, or even the system configured ChatGPT. Can anyone assist me with this? For some reason, after reading the docs, it's very unclear how this transition between the three takes place.

When using Foundation Models, is it possible to ask the model to produce output in a specific language, apart from giving an instruction like "Provide answers in ." ? (I tried that and it kind of worked, but it seems fragile.) I haven't noticed an API to do so and have a use-case where the output should be in a user-selectable language that is not the current system language.

We are trying to implement a custom encryption scheme for our Core ML models. Our goal is to bundle encrypted models, decrypt them into memory at runtime, and instantiate the MLModel without the unencrypted model file ever touching the disk. We have looked into the native apple encryption described here https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app but it has limitations like not working on intel macs, without SIP, and doesn’t work loading from dylib. It seems like most of the Core ML APIs require a file path, there is MLModelAsset APIs but I think they just write a modelc back to disk when compiling but can’t find any information confirming that (also concerned that this seems to be an older API, and means we need to compile at runtime). I am aware that the native encryption will be much more secure but would like not to have the models in readable text on disk. Does anyone know if this is possible or any alternatives to try to obfuscate the Core ML models, thanks

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 Machine Learning and AI Frameworks. What are you most excited about in the Foundation Models framework? The Foundation Models framework provides access to an on-device Large Language Model (LLM), enabling entirely on-device processing for intelligent features. This allows you to build features such as personalized search suggestions and dynamic NPC generation in games. The combination of guided generation and streaming capabilities is particularly exciting for creating delightful animations and features with reliable output. The seamless integration with SwiftUI and the new design material Liquid Glass is also a major advantage. When should I still bring my own LLM via CoreML? It's generally recommended to first explore Apple's built-in system models and APIs, including the Foundation Models framework, as they are highly optimized for Apple devices and cover a wide range of use cases. However, Core ML is still valuable if you need more control or choice over the specific model being deployed, such as customizing existing system models or augmenting prompts. Core ML provides the tools to get these models on-device, but you are responsible for model distribution and updates. Should I migrate PyTorch code to MLX? MLX is an open-source, general-purpose machine learning framework designed for Apple Silicon from the ground up. It offers a familiar API, similar to PyTorch, and supports C, C++, Python, and Swift. MLX emphasizes unified memory, a key feature of Apple Silicon hardware, which can improve performance. It's recommended to try MLX and see if its programming model and features better suit your application's needs. MLX shines when working with state-of-the-art, larger models. Can I test Foundation Models in Xcode simulator or device? Yes, you can use the Xcode simulator to test Foundation Models use cases. However, your Mac must be running macOS Tahoe. You can test on a physical iPhone running iOS 18 by connecting it to your Mac and running Playgrounds or live previews directly on the device. Which on-device models will be supported? any open source models? The Foundation Models framework currently supports Apple's first-party models only. This allows for platform-wide optimizations, improving battery life and reducing latency. While Core ML can be used to integrate open-source models, it's generally recommended to first explore the built-in system models and APIs provided by Apple, including those in the Vision, Natural Language, and Speech frameworks, as they are highly optimized for Apple devices. For frontier models, MLX can run very large models. How often will the Foundational Model be updated? How do we test for stability when the model is updated? The Foundation Model will be updated in sync with operating system updates. You can test your app against new model versions during the beta period by downloading the beta OS and running your app. It is highly recommended to create an "eval set" of golden prompts and responses to evaluate the performance of your features as the model changes or as you tweak your prompts. Report any unsatisfactory or satisfactory cases using Feedback Assistant. Which on-device model/API can I use to extract text data from images such as: nutrition labels, ingredient lists, cashier receipts, etc? Thank you. The Vision framework offers the RecognizeDocumentRequest which is specifically designed for these use cases. It not only recognizes text in images but also provides the structure of the document, such as rows in a receipt or the layout of a nutrition label. It can also identify data like phone numbers, addresses, and prices. What is the context window for the model? What are max tokens in and max tokens out? The context window for the Foundation Model is 4,096 tokens. The split between input and output tokens is flexible. For example, if you input 4,000 tokens, you'll have 96 tokens remaining for the output. The API takes in text, converting it to tokens under the hood. When estimating token count, a good rule of thumb is 3-4 characters per token for languages like English, and 1 character per token for languages like Japanese or Chinese. Handle potential errors gracefully by asking for shorter prompts or starting a new session if the token limit is exceeded. Is there a rate limit for Foundation Models API that is limited by power or temperature condition on the iPhone? Yes, there are rate limits, particularly when your app is in the background. A budget is allocated for background app usage, but exceeding it will result in rate-limiting errors. In the foreground, there is no rate limit unless the device is under heavy load (e.g., camera open, game mode). The system dynamically balances performance, battery life, and thermal conditions, which can affect the token throughput. Use appropriate quality of service settings for your tasks (e.g., background priority for background work) to help the system manage resources effectively. Do the foundation models support languages other than English? Yes, the on-device Foundation Model is multilingual and supports all languages supported by Apple Intelligence. To get the model to output in a specific language, prompt it with instructions indicating the user's preferred language using the locale API (e.g., "The user's preferred language is en-US"). Putting the instructions in English, but then putting the user prompt in the desired output language is a recommended practice. Are larger server-based models available through Foundation Models? No, the Foundation Models API currently only provides access to the on-device Large Language Model at the core of Apple Intelligence. It does not support server-side models. On-device models are preferred for privacy and for performance reasons. Is it possible to run Retrieval-Augmented Generation (RAG) using the Foundation Models framework? Yes, it is possible to run RAG on-device, but the Foundation Models framework does not include a built-in embedding model. You'll need to use a separate database to store vectors and implement nearest neighbor or cosine distance searches. The Natural Language framework offers simple word and sentence embeddings that can be used. Consider using a combination of Foundation Models and Core ML, using Core ML for your embedding model.

After installing iOS 26.4 the Foundation Models instruction following and tool calling capabilities have been degraded significantly. The model is not usable anymore. Examples: This works: "Is the car plugged in?" This does not work: "Tell me if the car is plugged in" Anything with the work "frunk" (front trunk) triggers Guardrail Violation. Phrases like "Lock Pride" also trigger Guardrail Violation (Pride is the name of the car). Tool calling only works half the time for really obvious things.

After watching the What's new in App Intents session I'm attempting to create an intent conforming to URLRepresentableIntent. The video states that so long as my AppEntity conforms to URLRepresentableEntity I should not have to provide a perform method . My application will be launched automatically and passed the appropriate URL. This seems to work in that my application is launched and is passed a URL, but the URL is in the form: FeatureEntity/{id}. Am I missing something, or is there a trick that enables it to pass along the URL specified in the AppEntity itself? struct MyExampleIntent: OpenIntent, URLRepresentableIntent { static let title: LocalizedStringResource = "Open Feature" static var parameterSummary: some ParameterSummary { Summary("Open \(\.$target)") } @Parameter(title: "My feature", description: "The feature to open.") var target: FeatureEntity } struct FeatureEntity: AppEntity { // ... } extension FeatureEntity: URLRepresentableEntity { static var urlRepresentation: URLRepresentation { "https://myurl.com/\(.id)" } }

I am calling into an app extension from a Safari Web Extension (sendNativeMessage, which in turn results in a call to NSExtensionRequestHandling’s beginRequest). My Safari extension aims to make use of the new foundation models for some of the features it provides. In my testing, I hit the rate limit by sending 4 requests, waiting 30 seconds between each. This makes the FoundationModels framework (which would otherwise serve my use case perfectly well) unusable in this context, because the model is called in response to user input, and this rate of user input is perfectly plausible in a real world scenario. The error thrown as a result of the rate limit is “Safety guardrail was triggered after consecutive failures during streaming.", but looking at the system logs in Console.app shows the rate limit as the real culprit. My suggestions: Please introduce sensible rate limits for app extensions, through an entitlement if need be. If it is rate limited to 1 request per every couple of seconds, that would already fix the issue for me. Please document the rate limit. Please make the thrown error reflect that it is the result of a rate limit and not a generic guardrail violation. IMPORTANT: please indicate in the thrown error when it is safe to try again. Filed a feedback here: FB18332004