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!

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

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)

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?

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

Is there anywhere we can reference error codes? I'm getting this error: "The operation couldn’t be completed. (FoundationModels.LanguageModelSession.GenerationError error 4.)" and I have no idea of what it means or what to attempt to fix.

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?

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

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.

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.

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?

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

Do we know what a safe max token limit is? After some iterating, I have come to believe 4096 might be the limit on device. Could you help me out by answering any of these questions: Is 4096 the correct limit? Do all devices have the same limit? Will the limit change over time or by device? The errors I get when going over the limit do not seem to say, hey you are over, so it's just by trial and error that I figure these issues out. Thanks for the fun new toys. Regards, Rob

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!

I’m developing an activity classifier that I’d like to input using the JSON format of CoreMotion data. I am getting the error: Unable to parse /Users/DewG/Downloads/Testing/Step1/Testing.json. It does not appear to be in JSON record format. A SequenceType of dictionaries is expected I've verified that the format I am using is JSON via various JSON validators, so I am expecting I'm just holding it wrong. Is there an example of a JSON file with CoreMotion data that I can model after?

Problem: We trained a LoRA adapter for Apple's FoundationModels framework using their TAMM (Training Adapter for Model Modification) toolkit v0.2.0 on macOS 26 beta 4. The adapter trains successfully but fails to load with: "Adapter is not compatible with the current system base model." TAMM 2.0 contains export/constants.py with: BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Findings: Adapter Export Process: In export_fmadapter.py def write_metadata(...): self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE # Hardcoded value The Compatibility Check: - When loading an adapter, Apple's system compares the adapter's baseModelSignature with the current system model - If they don't match: compatibleAdapterNotFound error - The error doesn't reveal the expected signature Questions: - How is BASE_SIGNATURE derived from the base model? - Is it SHA-1 of base-model.pt or some other computation? - Can we compute the correct signature for beta 4? - Or do we need Apple to release TAMM v0.3.0 with updated signature?

I have integrated Apple’s Foundation Model into my iOS application. As known, Foundation Model is only supported starting from iOS 26 on compatible devices. To maintain compatibility with older iOS versions, I wrapped the API calls with the condition if #available(iOS 26, *). The application works normally on an iPad running iOS 18 and on a Mac running macOS 26. However, when running the same build on a MacBook Air M1 (macOS 15) through iPad app compatibility, the app crashes immediately upon launch. The main issue is that I cannot debug directly on macOS 15, since the app can only be built on macOS 26 with Xcode beta. I then have to distribute it via TestFlight and download it on the MacBook Air M1 for testing. This makes identifying the detailed cause of the crash very difficult and time-consuming. Nevertheless, I have confirmed that the crash is caused by the Foundation Model APIs.

Does CoreML object detection only support AABB (Axis-Aligned Bounding Boxes) or also OBB (Oriented Bounded Boxes)? If not, any way to do it using Apple frameworks?

Hi, Are there rules around using Foundation Models: In a background task/session? Concurrently, i.e. a bunch simultaneously using Swift Concurrency? I couldn't find this in the docs (sorry if I missed it) so wondering what's supported and what the best practice is here. In case it matters, my primary platform is Vision Pro (so, M2).

I've tried creating a Lora adapter using the example dataset, scripts as part of the adapter_training_toolkit_v26_0_0 (last available) on MacOs 26 Beta 6. import SwiftUI import FoundationModels import Playgrounds #Playground { // The absolute path to your adapter. let localURL = URL(filePath: "/Users/syl/Downloads/adapter_training_toolkit_v26_0_0/train/test-lora.fmadapter") // Initialize the adapter by using the local URL. let adapter = try SystemLanguageModel.Adapter(fileURL: localURL) // An instance of the the system language model using your adapter. let customAdapterModel = SystemLanguageModel(adapter: adapter) // Create a session and prompt the model. let session = LanguageModelSession(model: customAdapterModel) let response = try await session.respond(to: "hello") } I get Adapter assets are invalid error. I've added the entitlements Is adapter_training_toolkit_v26_0_0 up to date?