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

Whisper and other STT APIs let you pass a custom vocabulary or initial_prompt to bias recognition toward domain-specific proper nouns. In the App Intents / Siri stack, is there an equivalent way to supply dynamic, per-user term lists — for example favorites or recently used items — to improve how spoken names are transcribed or resolved?

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.

During WWDC26 Keynote a second generation on-device model was announced with better speech generation capabilities. Is there a new API available for developers to generate speech?

What is the proper way to intercept tool calls modify them or dynamically approve/reject them?

We understand that on-device Foundation Models (FMs) can evolve between OS releases. To help us accurately scope our application capabilities and performance expectations for multiplatform development, could you clarify the variation of these new on-device models across different hardware? Specifically: Within the same OS & device family: Do the architecture, parameters, or capabilities of the on-device models vary based on hardware tiers (e.g., iPhone vs. iPhone Pro, or MacBook Air M5 vs. MacBook Pro with M5 Pro)? Across different device form factors: Are there model variations between hardware families running equivalent OS releases (e.g., Mac vs. iPhone)? Knowing if we are targeting a uniform model baseline or a tiered model ecosystem will greatly help us optimise our App Intelligence features or at least set us with proper expectations in scope and capabilities. Thanks.

I am trying out the new PrivateCloudComputeLanguageModel but can't get it to work. When I call session.respond it throws the following error: Error Domain=FoundationModels.LanguageModelError Code=-1 "The operation couldn’t be completed. (FoundationModels.LanguageModelError error -1.)" UserInfo={NSMultipleUnderlyingErrorsKey=( "Error Domain=FoundationModels.LanguageModelError Code=-1 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n \"Error Domain=ModelManagerServices.ModelManagerError Code=1046 \\\"(null)\\\" UserInfo={NSMultipleUnderlyingErrorsKey=(\\n)}\"\n)}" ), NSLocalizedDescription=The operation couldn’t be completed. (FoundationModels.LanguageModelError error -1.)} Maybe error code 1046 means something, but I can't find a mention of it in the docs. My set-up: macOS Golden Gate on a MacBook Pro M1 Xcode 27.0.0 beta 1, calling the model as part of a test on a iPhone 17 simulator with iOS 27 beta 1. I do have the Private Cloud Compute entitlement (removing it triggers a fatalError). model.isAvailable returns true. I tried logging into iCloud on both the macOS host as well as the simulator, but no difference. Relevant code: calling the model: https://github.com/Thomvis/Construct/blob/feature/foundation-models/Sources/MechMuse/FoundationModels.swift#L20 the test to run to trigger the issue: https://github.com/Thomvis/Construct/blob/feature/foundation-models/App/UnitTests/DescribeCombatantsEvaluation.swift#L78

Given that Foundation Models focus on native Swift APIs, is there any supported bridge for a WebKit-based app to access the Language Model Protocol?

In the new Xcode we saw examples of Claude, OAI & Google coding agents that you can start conversations with inside your project, giving it access to your project files context. As far as I understand, this requires an API key for those models & the processing is run on Anthropic / Google servers, not locally nor on Private Cloud Compute. Is it possible to instead, use the LLM powering Foundation Models, for a “Siri Code Agent” which operates in the place of those models, but runs on device or in Private Cloud Compute? I like how this works for Siri AI requests, and would love to have a coding assistant agent that can operate in the same privacy preserving way! Is this possible with any of the open source frameworks or the command line tools? If not, what is the best way to request this feature?

I work on an app called one sec which helps people reduce the amount of time they spend on social media by interrupting app openings with Shortcuts automations. With the release of iOS 26, we added a new Conversational Reflection interruption, a feature backed by Foundation Models. The user talks through their reasoning for wanting to use social media. A significant fraction of our users suffer from ADHD, ADD, and struggle with mental health. As a result, we try to show crisis support banners in our conversation UI. We do so with structured outputs, asking the language model If the user appears to be in deep distress. However, often times the guardrails are triggered and we don’t receive the response from the language model, meaning we can’t support our users and show them related resources if needed. We’d love to see more specific guardrails errors introduced in the framework to better support our users. Here’s a radar with further details: FB20828230 Thank you!

Apple demonstrated agentic behavior (e.g., the Passwords app changing credentials on the user's behalf), and Siri AI can now take systemwide actions in apps. Is there a first-class confirmation API for App Intents — a way to mark an action as requiring explicit user approval before execution, with a standard confirmation surface — or must developers build their own confirmation UI inside the intent? For irreversible or high-impact actions, what is Apple's recommended pattern to prevent the model from executing them autonomously, and can an intent declare a risk/sensitivity level the system respects? When Siri AI invokes an action, what authentication/authorization context is available to the intent (biometric gate, user-presence assertion), and how should an app require step-up auth for sensitive operations? Is there a supported audit trail for actions taken via Siri AI on the user's behalf, so an app can show the user what was done and when? How does the system handle an action that fails or partially completes during an agentic, multi-step flow?

When SpotlightSearchTool is used with a custom LanguageModel backend (for example Apple’s ChatCompletionsLanguageModel from apple/foundation-models-utilities, pointed at an OpenAI-compatible server), the tool can never be successfully invoked. The model produces tool-call arguments that exactly match the format documented in the tool’s own description, but those arguments fail validation against the tool’s generated parameters JSON Schema, throwing LanguageModelSession.ToolCallError with underlying error “Failed to parse generated content.” The root cause is a mismatch between two things the framework sends to the model in the same tool definition: the human-readable description (“Call format”), which presents the top-level arguments as { root, modelComposition, … }, and the parameters JSON Schema (FullArguments), which requires { "query": { "type": "search", "value": { root, modelComposition, … } } }. A model that follows the description is guaranteed to fail the schema. Secondary observation (may be a separate issue or intended) CoreSpotlightSource.fetchAttributes appears to have no effect on which attributes are returned to the model on this agentic-search path. Even with fetchAttributes: [.title, .contentDescription] set on the source, results contain only default metadata (kMDItemTitle, kMDItemDisplayName, dates, identifiers) and omit kMDItemDescription. The description is returned only when the in-query SearchArguments.fetchAttributes explicitly lists it. The searchableIndexDelegate was never invoked in any configuration tried (including .dynamic). If the source-level fetchAttributes is meant to drive returned attributes, that also seems incorrect; otherwise, clarifying the docs would help. Therefore my question, is this just not supported or does the scheme need an update? Or is There a different way that should be done?

Should static domain documentation live in on-device RAG (local embeddings + FM), while time-sensitive data always comes from network tools — and are there practical size/latency budgets for on-device embedding indexes?

If your app creates an Apple Intelligence schema conforming App Entity, Siri AI can only reason over the schema defined properties. (see this thread). But as a developer, I can add more optional properties on my App Entity with additional metadata about the entity. If my app contributes these App Entities to Spotlight as indexed entities, is SpotlightSearchTool also limited to reasoning over just the schema defined properties, or are these unrelated concepts? Will these additional optional properties on my App Entity enable a deeper SpotlightSearchTool powered search experience around these entities?

For assistants that need multi-step tool use (search → fetch → compare → respond), should third-party apps expose capabilities as App Intents for on-device model selection, or keep tool orchestration on the server and use on-device models only for speech and summarization? What breaks when the same action exists in both places?

With different VLMs supporting different size and background color if padding is needed… and iOS 27 AFM being the most flexible… the previous talk mentioned that the context size suffers for this flexibility… so what’s the best format/size/background for the app to pre-process to minimize token use… much thanks

Visual Intelligence lets users ask Siri about what the camera or screen shows, and the screenshot tool can extract structured data into system apps. Can a third-party app contribute results or actions when the user invokes Visual Intelligence over the app's own content or a screenshot of it (analogous to how a schedule becomes calendar events), and what API surfaces that? For the Image Playground API, what are the content, rate, and style constraints, and can generated assets be used in commercial app contexts? Is there a supported way for an app to provide its own visual understanding to the system rather than relying solely on Apple's model — for domain-specific imagery the on-device model may not recognize?

The Foundation Models framework is clearly designed around language, but there's a large class of on-device AI tasks that are not language tasks at all. Time series forecasting is one example think energy consumption modeling, or sensor anomaly detection. These models take sequences of numeric data and output probabilistic forecasts. No text involved at any layer. Is there any intention to extend Foundation Models or a sibling framework to non-language modalities specifically structured numeric and time series inference

When using the 'Summarize History' modifier, can we configure the summarization prompt to specifically preserve certain metadata like tool call IDs so that a resumed conversation can still reference previously executed app actions?

Are the on-device Apple foundation models like AFM 3 Core shipped as CoreAI models or do they use some different technology? Is it possible to open them in the Core AI Debugger to understand them in detail?

Any guidance if we want the iOS 27 SystemLanguageModel to always defer to our app for all answers and not its built-in training for responses