Hi, I have an app that uses Core Data to store user information and display it in various views. I want to know if it's possible to easily integrate this setup with FoundationModels to make it easier for the user to query and manipulate the information, and if so, how would I go about it? Can the model be pointed to the database schema file and the SQLite file sitting in the user's app group container to parse out the information needed? And/or should the NSManagedObjects be made @Generable for better output? Any guidance about this would be useful.

Seeing this error from time to time: Context(debugDescription: "Content contains 4089 tokens, which exceeds the maximum allowed context size of 4096.", underlyingErrors: []) Of course, 4089 is less than 4096 so what is this telling me and how do I work around it? Is the limit actually lower than 4096?

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

In this online session, you can code along with us as we build generative AI features into a sample app live in Xcode. We'll guide you through implementing core features like basic text generation, as well as advanced topics like guided generation for structured data output, streaming responses for dynamic UI updates, and tool calling to retrieve data or take an action. Check out these resources to get started: Download the project files: https://developer.apple.com/events/re... Explore the code along guide: https://developer.apple.com/events/re... Join the live Q&A: https://developer.apple.com/videos/pl... Agenda – All times PDT 10 a.m.: Welcome and Xcode setup 10:15 a.m.: Framework basics, guided generation, and building prompts 11 a.m.: Break 11:10 a.m.: UI streaming, tool calling, and performance optimization 11:50 a.m.: Wrap up All are welcome to attend the session. To actively code along, you'll need a Mac with Apple silicon that supports Apple Intelligence running the latest release of macOS Tahoe 26 and Xcode 26. If you have questions after the code along concludes please share a post here in the forums and engage with the community.

In the name of God, please allow initializing GeneratedContent from an array of key-value pairs. It’s literally the same thing KeyValuePairs uses internally, but it would let us initialize structure-like GeneratedContent from dynamic data without resorting to unsafeBitCast hacks.

Hi, testing latest tensorflow-metal plugin with tensorflow 2.20 doesn't work.. using python Python 3.12.11 (main, Jun 3 2025, 15:41:47) [Clang 17.0.0 (clang-1700.0.13.3)] on darwin simple testing shows error: import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file) tf.config.experimental.list_physical_devices('GPU') Traceback (most recent call last): File "", line 1, in NameError: name 'tf' is not defined I fixed this error by copying _pywrap_tensorflow_internal.so where it's searched.. 1)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64 2)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ 3)cp /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ then fails symbol not found: Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E in libmetal_plugin.dylib full log: with import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <2FF91C8B-0CB6-3E66-96B7-092FDF36772E> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so

Hi all, I’m encountering an issue when trying to run Apple Foundation Models in a blank project targeting iOS 26. Below are the details: Xcode: Latest version with iOS 26 SDK macOS: macOS 26 Tahoe (installed on main disk) Mac: 16” MacBook Pro with M2 Pro chip Apple Intelligence: Available and functional on this machine Problem: I created a new blank iOS project, set the deployment target to iOS 26, and ran the following minimal code using Foundation Models. However, I get no response at all in the output - not even an error. The app runs, but the model does not produce any output. #Playground { let session = LanguageModelSession() let response = try await session.respond(to: "Tell me a story") } Then, I tried to catch an error with this code: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "Tell me a story") print(response) } catch { print("Failed to get response:", error) } print("This line, never gets executed") } And got these results: I’ve done further testing and discovered something important: I tried running the Code Along sample project, and there the #Playground macro worked without issues. The only significant difference I noticed was the Canvas run destination: In my original project, I was using iPhone 16 Pro (iOS 26) as the run target in Canvas. Apple Intelligence was enabled on the simulator, but no response was returned when executing the prompt. In the sample project, the Canvas was running on My Mac. I attempted to match that setup, but at first, my destination was My Mac (Designed for iPad), which still didn’t work. The macro finally executed properly once I switched to My Mac (AppKit). So the question is ... it seems that for now, Foundation Models and the #Playground macro only run correctly when the canvas or destination is set to “My Mac (AppKit)”?

Hello, I am studying macOS26 Apple Intelligence features. I have created a basic swift program with Xcode. This program is sending prompts to FoundationModels.LanguageModelSession. It works fine but this model is not trained for programming or code completion. Xcode has an AI code completion feature. It is called "Predictive Code completion model". So, there are multiple on-device models on macOS26 ? Are there others ? Is there a way for me to send prompts to this "Predictive Code completion model" from my program ? Thanks

The documentation for the Create ML tool ("Building an object detector data source") mentions that there are options for using normalized values instead of pixels and also different anchor point origins ("MLBoundingBoxCoordinatesOrigin") instead of always using "center". However, the JSON format for these does not appear in any examples. Does anyone know the format for these options?

Bear with me, please. Please make sure a highly skilled technical person reads and understands this. I want to describe my vision for (AI/Algorithmically) Optimised Operating Systems. To explain it properly, I will describe the process to build it (pseudo). Required Knowledge (no particular order): Processor Logic Circuits, LLM models, LLM tool usage, Python OO coding, Procedural vs OO, NLP fuzzy matching, benchmarking, canvas/artefacts/dynamic HTML interfaces, concepts of how AI models are vastly compressed and miniaturised forms of full data, Algorithmic vs AI. First, take all OO Python code (example) on GitHub (example), then separate each function from each object into its own procedure (procedural logic) by making a logical procedural list of actions to perform only that function based on its entire dependency chain (i.e. all other objects it relies on). Relate all compiled functions using (for example) fuzzy matching on the name, or AI-based functional profiling to get multiple instances of each function. Starting with the most used function, test each one against the others that perform the same task for bugs and completeness. Determine the fastest, most optimal version of that function (and every function). Add a single instance of each most optimal function to the centralised tool codebase, which will later be utilised by the language models. This ensures we rely only on the most optimised function for each and every use case — with every program using one shared instance of that function instead of compiling it separately. In this way, we optimise all existing (Python OO) code based on maximum availability of certain resources. Use the optimised code to teach the AI how to map each user task directly to optimised code (i.e. each user-facing function in unoptimised program code to its optimised version). Later, teach the codebase how to optimise these functions directly to the logic circuits on the processor — as an Operating System — using essentially the same process. Eventually, use the most optimised form for tasks via a dynamic canvas/artefacts UI interface generated by the Start Menu using the language model. Allow custom interfaces to emerge dynamically from the available functions based on language understanding. Grow and optimise the algorithmic codebase until the entire operating system — with versioning — evolves based on the AI-generated user interfaces (compressing them into new versions of the tools codebase). Please don’t give up until you understand this concept completely and nothing in the description is beyond your full comprehension. It’s a highly significant step forward.

WWDC25: Combine Metal 4 machine learning and graphics Demonstrated a way to combine neural network in the graphics pipeline directly through the shaders, using an example of Texture Compression. However there is no mention of using which ML technique texture is compressed. Can anyone point me to some well known model/s for this particular use case shown in WWDC25.

Pretty much as per the title and I suspect I know the answer. Given that Foundation Models run on device, is it possible to use Foundation Models framework inside of a DeviceActivityReport? I've been tinkering with it, and all I get is errors and "Sandbox restrictions". Am I missing something? Seems like a missed trick to utilise on device AI/ML with other frameworks.

Hi, I just upgraded to macOS Tahoe Beta 2 and now I'm getting this error when I try to initialize my Foundation Models' session: Error Resource (Local Sanitizer Asset) unavailable error. import FoundationModels #Playground { let session = LanguageModelSession() do { let result = try await session.respond(to: "Tell me 3 colors") print(result.content) } catch { print("Error", error) } } I couldn't find any resource guiding me on how to solve this. Any help/workaround? Thank you!

Hey dear developers! This post should be available for the future Siri updates and improvements but also for wishes in this forum so that everyone can share their opinion and idea please stay friendly. have fun! I had already thought about developing a demo app to demonstrate my idea for a better Siri. My change of many: Wish Update: Siri's language recognition capabilities have been significantly enhanced. Instead of manually setting the language, Siri can now automatically recognize the language you intend to use, making language switching much more efficient. Simply speak the language you want to communicate in, and Siri will automatically recognize it and respond accordingly. Whether you speak English, German, or Japanese, Siri will respond in the language you choose.

I'd love to add a feature based on FoundationModels to the Mac Catalyst version of my iOS app. Unfortunately I get an error when importing FoundationModels: No such module 'FoundationModels'. Documentation says Mac Catalyst is supported: https://developer.apple.com/documentation/foundationmodels I can create iOS builds using the FoundationModels framework without issues. Hope this will be fixed soon! Config: Xcode 26.0 beta (17A5241e) macOS 26.0 Beta (25A5279m) 15-inch, M4, 2025 MacBook Air

Hey Tried using a few regular expressions and all fail with an error: Unhandled error streaming response: A generation guide with an unsupported pattern was used. Is there are a list of supported features? I don't see it in docs, and it takes RegExp. Anything with e.g. [A-Z] fails.

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!

In this WWDC25 session, it is explictely mentioned that apps should support AttributedString for text parameters to their App Intents. However, I have not gotten this to work. Whenever I pass rich text (either generated by the new "Use Model" intent or generated manually for example using "Make Rich Text from Markdown"), my Intent gets an AttributedString with the correct characters, but with all attributes stripped (so in effect just plain text). struct TestIntent: AppIntent { static var title = LocalizedStringResource(stringLiteral: "Test Intent") static var description = IntentDescription("Tests Attributed Strings in Intent Parameters.") @Parameter var text: AttributedString func perform() async throws -> some IntentResult & ReturnsValue<AttributedString> { return .result(value: text) } } Is there anything else I am missing?

I was able to open a new project and play around with the Foundation Model, but when I dropped this class in a production app (with a lot of files) I'm running into Safety Guardrail errors for this very small prompt. Specifically it's "Safety guardrail was triggered after consecutive failures during streaming." Does it have something to do with the size of the app? I don't know what else to try to get it to work? import FoundationModels import Playgrounds @available(iOS 26.0, *) #Playground { Task { do { let session = LanguageModelSession() let prompt = "Write a short story about a talking cat." let response = try await session.respond(to: prompt) print(response) } catch { print("Error: \(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?