Google ML Kit for Swift Package Manager

Google ships ML Kit only through CocoaPods. This project rebuilds those pods as XCFrameworks, publishes them as GitHub Release assets, and exposes every user-facing ML Kit iOS API as a SwiftPM library product.

Requirements

• iOS 15 or later
• Xcode 15 or later to consume the package; Xcode 26 or later to build it

Apple Silicon simulators are supported — see Simulator support.

Installation

dependencies: [
    .package(url: "https://github.com/d-date/google-mlkit-swiftpm", from: "9.0.2")
]

Then add the modules you need:

.target(
    name: "YourTarget",
    dependencies: [
        .product(name: "MLKitBarcodeScanning", package: "google-mlkit-swiftpm"),
        .product(name: "MLKitTextRecognition", package: "google-mlkit-swiftpm"),
    ]
)

Three steps are not optional. Skip any of them and the module fails at runtime rather than at build time:

1. Add the linker flags
2. Add the resource bundles your modules need
3. Nothing else — but read Simulator support if you are coming from an older release with EXCLUDED_ARCHS set.

Upgrading from 9.0.0 or 9.0.0-1? 9.0.2 is the same upstream ML Kit 9.0.0 build, repackaged. Since 9.0.1, text recognition, pose detection and selfie segmentation ship their models at all, a single product can be adopted without undefined symbols, the simulator slice covers arm64, and App Store Connect accepts the upload. If you set EXCLUDED_ARCHS[sdk=iphonesimulator*] = arm64 for an earlier release, remove it.

1. Add the linker flags

Add these to Other Linker Flags for your app target:

• -ObjC
• -all_load

ML Kit ships as static archives that register classes and models through Objective-C categories and static initialisers. Without these flags the linker drops the archive members that hold them, and you get unrecognized selector or a model that refuses to load.

2. Add the resource bundles

Every ML Kit model lives in a resource bundle, and Swift Package Manager cannot carry a resource bundle inside a binary target. Each bundle is published as a separate release asset: download the ones your modules need, add them to your Xcode project, and make sure they land in Copy Bundle Resources of your app target.

./scripts/download_bundles.sh 9.0.2

Bundle	Needed by
GoogleMVFaceDetectorResources.bundle	MLKitFaceDetection
LatinOCRResources.bundle	MLKitTextRecognition
ChineseOCRResources.bundle	MLKitTextRecognitionChinese
DevanagariOCRResources.bundle	MLKitTextRecognitionDevanagari
JapaneseOCRResources.bundle	MLKitTextRecognitionJapanese
KoreanOCRResources.bundle	MLKitTextRecognitionKorean
MLKitImageLabelingResources.bundle	MLKitImageLabeling
MLKitObjectDetectionResources.bundle	MLKitObjectDetection
MLKitObjectDetectionCommonResources.bundle	MLKitImageLabeling, MLKitImageLabelingCustom, MLKitObjectDetection, MLKitObjectDetectionCustom
MLKitPoseDetectionFastResources.bundle	MLKitPoseDetection
MLKitPoseDetectionAccurateResources.bundle	MLKitPoseDetectionAccurate
MLKitPoseDetectionCommonResources.bundle	MLKitPoseDetection, MLKitPoseDetectionAccurate
MLKitSegmentationSelfieResources.bundle	MLKitSegmentationSelfie
MLKitSegmentationCommonResources.bundle	MLKitSegmentationSelfie
MLKitXenoResources.bundle	MLKitPoseDetection, MLKitPoseDetectionAccurate, MLKitSegmentationSelfie
MLKitTranslate_resource.bundle	MLKitTranslate
MLKitDigitalInkRecognition_resource.bundle	MLKitDigitalInkRecognition
PredictOnDeviceResource.bundle	MLKitSmartReply

PredictOnDevice_resource.bundle is also published: it is the same content under the name ML Kit nests inside its own framework, which is what releases before 9.0.1 shipped. Add whichever your build already refers to; adding both is harmless.

A missing bundle shows up as a runtime throw — text recognition, for example, raises MLKTextRecognizerInternalErrorCreationFailure with "Invalid model path.".

Products

Add only the products you use; each one links just the frameworks it needs.

Vision

Product	What it does
MLKitBarcodeScanning	Scan and decode barcodes
MLKitFaceDetection	Detect faces, contours and facial features
MLKitTextRecognition	Recognise Latin-script text (v2)
MLKitTextRecognitionChinese	Chinese text
MLKitTextRecognitionDevanagari	Devanagari text
MLKitTextRecognitionJapanese	Japanese text
MLKitTextRecognitionKorean	Korean text
MLKitImageLabeling	Label objects, places and activities
MLKitImageLabelingCustom	Image labeling with your own model
MLKitObjectDetection	Detect and track objects
MLKitObjectDetectionCustom	Object detection with your own model
MLKitPoseDetection	Body pose detection
MLKitPoseDetectionAccurate	Body pose detection, accurate model
MLKitSegmentationSelfie	Separate people from the background

Language

Product	What it does
MLKitLanguageID	Identify the language of a string
MLKitTranslate	Translate between languages on device
MLKitSmartReply	Suggest contextual replies
MLKitEntityExtraction	Find addresses, dates, phone numbers and the like in text
MLKitDigitalInkRecognition	Recognise handwriting and drawn strokes

Simulator support

ML Kit's pre-built binaries contain no arm64 simulator code, and Xcode 26 dropped Rosetta simulators — so on Apple Silicon the published XCFrameworks used to be unusable in the Simulator entirely.

Since 9.0.1 the arm64 simulator slice is synthesised from the device slice by rewriting the Mach-O platform (scripts/postprocess_xcframeworks.rb). Published XCFrameworks carry arm64 for iphoneos and arm64 + x86_64 for iphonesimulator, so no EXCLUDED_ARCHS workaround is needed.

Known limitations

• No dSYMs. Google ships ML Kit without debug info, so Xcode's archive step reports "Upload Symbols Failed" for each ML Kit framework. The frameworks are linked statically, so their symbols end up in your app's own dSYM; the warnings are spurious and do not block submission. You could not symbolicate inside ML Kit anyway — it is closed source.
• Resource bundles are manual. Swift Package Manager has no way to ship them inside a binary target. See above.
• Digital ink recognition and entity extraction download their models at runtime from Google, so they ship no bundled model and need network access the first time you use them.
• The wrapper version is not the pod version. 9.0.2 repackages upstream ML Kit 9.0.0; the pod versions each framework reports are Google's own.

Example app

git submodule update --init
./scripts/download_bundles.sh 9.0.2
cd Example && open Example.xcworkspace

Set code signing to your own team. The app runs on a device and on the Apple Silicon Simulator, and exercises every module.

Maintaining this package

The build is driven by the Makefile; CLAUDE.md explains the pipeline stage by stage and AUTOMATION.md covers the release automation.

git submodule update --init          # xcframework-maker
make run                             # build everything into GoogleMLKit/
make verify                          # module lists, Info.plists, product closure
./scripts/verify_runtime.sh          # slices, Info.plists, symbol table
./scripts/verify_local_archive.sh    # run ML Kit on the Simulator, archive for device
./scripts/verify_app_store_upload.sh --upload   # Apple-side validation and delivery

verify_local_archive.sh is the gate that matters: it runs inference on the arm64 Simulator, launches the app, archives for device, and checks that every model bundle reached the app and that nothing was linked dynamically. A missing model bundle builds and archives perfectly and only fails when inference runs, so build-time checks alone are not enough.

To pick up a new upstream ML Kit version:

ruby scripts/check_mlkit_version.rb
./scripts/build_all.sh <version>

Or run the Build MLKit XCFrameworks workflow from the Actions tab.