runanywhere-sdks

RunAnywhere Swift SDK

A production-grade, on-device AI SDK for iOS and macOS. The SDK enables low-latency, privacy-preserving inference for large language models, speech recognition, and voice synthesis with modular backend support.

Table of Contents

Overview

The RunAnywhere Swift SDK enables developers to run AI models directly on Apple devices without requiring network connectivity for inference. By keeping data on-device, the SDK ensures minimal latency and maximum privacy for your users.

The SDK provides a unified interface to multiple AI capabilities, including large language models (LLMs), speech-to-text (STT), text-to-speech (TTS), and voice activity detection (VAD). These capabilities are delivered through pluggable backend modules that can be included as needed.

Key Capabilities

Features

Language Models (LLM)

Speech-to-Text (STT)

Text-to-Speech (TTS)

Voice Activity Detection (VAD)

Voice Agent Pipeline

Model Management

Observability

Requirements

Platform Minimum Version
iOS 17.5+
macOS 14.5+

Swift Version: 5.9+

Xcode: 15.2+

Some optional modules have higher runtime requirements:

Installation

Add the RunAnywhere SDK to your project using Xcode:

  1. Open your project in Xcode
  2. Go to File > Add Package Dependencies…
  3. Enter the repository URL: 
    https://github.com/RunanywhereAI/runanywhere-sdks
  4. Select the version (e.g., from: "1.0.0")
  5. Choose the products you need:
    • RunAnywhere (required) - Core SDK
    • RunAnywhereONNX - ONNX Runtime for STT/TTS/VAD
    • RunAnywhereLlamaCPP - LLM text generation with GGUF models

Package.swift

dependencies: [
    .package(url: "https://github.com/RunanywhereAI/runanywhere-sdks", from: "1.0.0")
],
targets: [
    .target(
        name: "YourApp",
        dependencies: [
            .product(name: "RunAnywhere", package: "runanywhere-sdks"),
            .product(name: "RunAnywhereLlamaCPP", package: "runanywhere-sdks"),
            .product(name: "RunAnywhereONNX", package: "runanywhere-sdks"),
        ]
    )
]

Package Structure

This repository contains two Package.swift files for different use cases:

File Location Purpose
Root Package.swift runanywhere-sdks/Package.swift For external SPM consumers. Downloads pre-built XCFrameworks from GitHub releases.
Local Package.swift runanywhere-sdks/sdk/runanywhere-swift/Package.swift For SDK developers. Uses local XCFrameworks from Binaries/ directory.

For app developers: Use the root-level package via the GitHub URL (as shown above).

For SDK contributors: Set useLocalNatives = true in the root Package.swift (NOT sdk/runanywhere-swift/Package.swift, which is always-local and has no flag) after building the XCFrameworks (see Local Development & Contributing below).

Quick Start

1. Initialize the SDK

import RunAnywhere
import LlamaCPPRuntime

@main
struct MyApp: App {
    init() {
        Task { @MainActor in
            // Register the LlamaCPP module for LLM support
            LlamaCPP.register()

            // Initialize the SDK
            do {
                try RunAnywhere.initialize(
                    apiKey: "<YOUR_API_KEY>",
                    baseURL: "https://api.runanywhere.ai",
                    environment: .production
                )
            } catch {
                print("SDK initialization failed: \(error)")
            }
        }
    }

    var body: some Scene {
        WindowGroup {
            ContentView()
        }
    }
}

2. Generate Text

// Build a proto-backed generate request via the v2 surface
var options = RALLMGenerationOptions.defaults()
options.maxTokens = 200
options.temperature = 0.7

var request = options.toRALLMGenerateRequest(
    prompt: "Explain quantum computing in simple terms"
)

let result = try await RunAnywhere.generate(request)
print("Response: \(result.text)")
print("Tokens generated: \(result.tokensGenerated)")

3. Load a Model

// Load an LLM through the canonical lifecycle (RAModelLoadRequest).
var loadRequest = RAModelLoadRequest()
loadRequest.modelID = "llama-3.2-1b-instruct-q4"
loadRequest.category = .language

let loadResult = await RunAnywhere.loadModel(loadRequest)
guard loadResult.success else {
    print("Load failed: \(loadResult.errorMessage)")
    return
}

// Check if a model is loaded for a given modality via the lifecycle service.
var current = RACurrentModelRequest()
current.category = .language
let snapshot = RunAnywhere.currentModel(current)
print("Loaded:", snapshot.found, "id=", snapshot.modelID)

Configuration

SDK Initialization Parameters

try RunAnywhere.initialize(
    apiKey: "<YOUR_API_KEY>",
    baseURL: "https://api.runanywhere.ai",
    environment: .production
)

Environment Modes

Environment Description
.development Verbose logging, mock services, local analytics
.staging Testing with real services
.production Minimal logging, full authentication, telemetry

Generation Options

var options = RALLMGenerationOptions.defaults()
options.maxTokens = 100
options.temperature = 0.8
options.topP = 1.0
options.stopSequences = ["END"]
options.streamingEnabled = false
options.systemPrompt = "You are a helpful assistant."

Module Registration

Register modules at app startup before using their capabilities:

import RunAnywhere
import LlamaCPPRuntime
import ONNXRuntime

@MainActor
func setupSDK() {
    LlamaCPP.register()   // LLM (priority: 100)
    ONNX.register()       // STT + TTS (priority: 100)
}

Usage Examples

Streaming Text Generation

var options = RALLMGenerationOptions.defaults()
options.maxTokens = 150
options.streamingEnabled = true
let request = options.toRALLMGenerateRequest(
    prompt: "Write a short poem about AI"
)

let stream = try await RunAnywhere.generateStream(request)
for await event in stream {
    if event.kind == .answer {
        print(event.token, terminator: "")
    }
}

Structured Output Generation

// Commons owns the full structured-output pipeline (prepare → generate →
// strip thinking tags → extract JSON → validate). Build a `RAJSONSchema`
// by populating its typed proto fields (the canonical JSON Schema text is
// produced by the read-only `jsonSchemaString` computed property).
var schema = RAJSONSchema()
schema.type = .object
schema.required = ["question", "options", "correctAnswer"]

var questionProp = RAJSONSchemaProperty()
questionProp.type = .string
schema.properties["question"] = questionProp

var optionsProp = RAJSONSchemaProperty()
optionsProp.type = .array
optionsProp.itemsSchema.type = .string
schema.properties["options"] = optionsProp

var correctAnswerProp = RAJSONSchemaProperty()
correctAnswerProp.type = .integer
schema.properties["correctAnswer"] = correctAnswerProp

let result = try await RunAnywhere.generateStructured(
    prompt: "Create a quiz question about Swift programming",
    schema: schema
)
let jsonString = String(data: result.parsedJson, encoding: .utf8) ?? ""
print("Validated JSON:", jsonString)

Speech-to-Text Transcription

import RunAnywhere
import ONNXRuntime

ONNX.register()

// Load the STT model through the canonical lifecycle.
var loadRequest = RAModelLoadRequest()
loadRequest.modelID = "whisper-base-onnx"
loadRequest.category = .speechRecognition
_ = await RunAnywhere.loadModel(loadRequest)

let audioData: Data = // your audio data (16kHz, mono, Float32)
let transcription = try await RunAnywhere.transcribe(audio: audioData)
print("Transcribed: \(transcription.text)")

Text-to-Speech Synthesis

// Load a TTS voice through the canonical lifecycle.
var loadRequest = RAModelLoadRequest()
loadRequest.modelID = "piper-en-us-amy"
loadRequest.category = .speechSynthesis
_ = await RunAnywhere.loadModel(loadRequest)

var options = RATTSOptions.defaults()
options.speakingRate = 1.0
options.pitch = 1.0
options.volume = 0.8

let output = try await RunAnywhere.synthesize(
    "Hello! Welcome to RunAnywhere.",
    options: options
)

Voice Agent Pipeline

// Once STT, LLM, and TTS models are loaded via RAModelLoadRequest, compose
// the voice agent from the lifecycle snapshots:
try await RunAnywhere.initializeVoiceAgentWithLoadedModels()

let audioData: Data = // recorded audio
let result = try await RunAnywhere.processVoiceTurn(audioData)
print("User said:", result.transcription)
print("AI response:", result.assistantResponse)

Subscribing to Events

import Combine

class ViewModel: ObservableObject {
    private var cancellables = Set<AnyCancellable>()

    init() {
        RunAnywhere.events.events
            .receive(on: DispatchQueue.main)
            .sink { event in
                print("Event: \(event.category)")
            }
            .store(in: &cancellables)

        RunAnywhere.events.events(for: .llm)
            .sink { event in
                print("LLM Event: \(event.category)")
            }
            .store(in: &cancellables)
    }
}

Model Download with Progress

// List registered models via the public proto-backed registry API.
let listResult = await RunAnywhere.listModels()
guard let model = listResult.models.models.first(where: { $0.id == "llama-3.2-1b-instruct-q4" }) else {
    return
}

// Download with the closure-based progress callback. Commons owns plan →
// start → progress polling → registry import; the closure receives each
// `RADownloadProgress` snapshot in real time.
let final = try await RunAnywhere.downloadModel(model) { progress in
    let percent = Int(progress.overallProgress * 100)
    print("\(progress.stage): \(percent)%")
}
print("Local path: \(final.localPath)")

Architecture

The RunAnywhere SDK follows a modular, provider-based architecture that separates core functionality from specific backend implementations:

+------------------------------------------------------------------+
|                         Public API                                |
|        RunAnywhere.generate() / transcribe() / synthesize()       |
+------------------------------------------------------------------+
                               |
+------------------------------------------------------------------+
|                      Capability Layer                             |
|     LLMCapability  |  STTCapability  |  TTSCapability  |  ...    |
+------------------------------------------------------------------+
                               |
+------------------------------------------------------------------+
|                     ServiceRegistry                               |
|          Routes requests to registered service providers          |
+------------------------------------------------------------------+
                               |
          +--------------------+--------------------+
          v                    v                    v
+------------------+  +------------------+  +------------------+
|  LlamaCPP Module |  |   ONNX Module    |  | AppleAI Module   |
|   (LLM: GGUF)    |  |  (STT + TTS)     |  |  (LLM: iOS 26+)  |
+------------------+  +------------------+  +------------------+
          |                    |                    |
          v                    v                    v
+------------------------------------------------------------------+
|               Native Runtime / XCFramework                        |
|          RunAnywhereCore (C++ with Metal acceleration)            |
+------------------------------------------------------------------+

Key Components:

Logging and Observability

Configure Log Level

RunAnywhere.setLogLevel(.debug)
RunAnywhere.setLocalLoggingEnabled(true)
RunAnywhere.setDebugMode(true)
RunAnywhere.flushLogs()

Log Levels

Level Description
.debug Detailed information for debugging
.info General operational information
.warning Potential issues that don’t prevent operation
.error Errors that affect specific operations
.fault Critical errors indicating serious problems

Analytics

The SDK automatically tracks key metrics:

Error Handling

All SDK errors are thrown as SDKException, which carries a typed error code (RASDKErrorCode), a developer-facing message, and a category identifying which subsystem produced the error.

Error Codes

RASDKErrorCode covers the v2 surface, including:

(See SDKException.swift and Generated/sdk_errors.pb.swift for the full list of codes and categories.)

Handling Errors

do {
    var options = RALLMGenerationOptions.defaults()
    options.maxTokens = 64
    let request = options.toRALLMGenerateRequest(prompt: "Hello")
    let result = try await RunAnywhere.generate(request)
    print(result.text)
} catch let error as SDKException {
    switch error.code {
    case .notInitialized:
        print("Please call RunAnywhere.initialize() first")
    case .modelNotLoaded:
        print("Model not loaded. Call RunAnywhere.loadModel(_:) first.")
    case .generationFailed:
        print("Generation failed: \(error.message)")
    default:
        print("Error (\(error.category)): \(error.message)")
    }
}

Performance Guidelines

Model Selection

Memory Management

// Unload a model through the canonical lifecycle.
var unloadRequest = RAModelUnloadRequest()
unloadRequest.category = .language
_ = await RunAnywhere.unloadModel(unloadRequest)

// Check storage before downloading
let storageInfo = await RunAnywhere.getStorageInfo()
if storageInfo.availableBytes > model.downloadSize ?? 0 {
    // Safe to download
}

// Clean up temporary files periodically
try await RunAnywhere.cleanTempFiles()

Threading

Streaming for Responsiveness

var options = RALLMGenerationOptions.defaults()
options.streamingEnabled = true
let request = options.toRALLMGenerateRequest(prompt: prompt)

let stream = try await RunAnywhere.generateStream(request)
for await event in stream where event.kind == .answer {
    await MainActor.run { self.text += event.token }
}

FAQ

Do I need an internet connection to use the SDK?

No, once models are downloaded, all inference happens on-device. You only need internet for:

Which models are supported?

The SDK supports:

How much storage do models require?

Model sizes vary significantly:

Can I use multiple models simultaneously?

Currently, one LLM can be loaded at a time. STT and TTS models can be loaded alongside LLM models. Use RunAnywhere.unloadModel(RAModelUnloadRequest()) before loading a different LLM.

How do I handle model updates?

Call RunAnywhere.listModels() (or RunAnywhere.queryModels(_:) / RunAnywhere.downloadedModels()) to refresh the in-memory model catalog from the registry, then call RunAnywhere.downloadModel(_:onProgress:) to fetch any new or updated entries alongside existing models. Model assignment discovery runs automatically as part of the SDK’s Phase-2 initialization.

Is user data sent to the cloud?

By default, only anonymous analytics (latency, error rates) are collected. Actual prompts, responses, and audio data never leave the device.

How do I debug issues?

  1. Enable debug mode: RunAnywhere.setDebugMode(true)
  2. Check logs with Pulse integration
  3. Subscribe to error events: RunAnywhere.events.on(.error) { ... }

How do I send a generation request?

Build a RALLMGenerationOptions, call toRALLMGenerateRequest(prompt:) to produce a RALLMGenerateRequest, and pass it to RunAnywhere.generate(_:) or RunAnywhere.generateStream(_:). There is no longer a separate chat() convenience — the proto-backed generate(_:) API returns a full RALLMGenerationResult with metrics.

Local Development & Contributing

We welcome contributions to the RunAnywhere Swift SDK. This section explains how to set up your development environment to build the SDK from source and test your changes with the sample app.

Prerequisites

First-Time Setup (Build from Source)

The SDK depends on native C++ libraries from runanywhere-commons. Build XCFrameworks locally so you can develop and test the SDK end-to-end.

# 1. Clone the repository
git clone https://github.com/RunanywhereAI/runanywhere-sdks.git
cd runanywhere-sdks

# 2. Build XCFrameworks (~5-15 minutes)
./sdk/runanywhere-swift/scripts/build-core-xcframework.sh

What the build script does:

  1. Downloads dependencies (ONNX Runtime, Sherpa-ONNX)
  2. Builds RACommons.xcframework (core infrastructure)
  3. Builds RABackendLLAMACPP.xcframework (LLM backend)
  4. Builds RABackendONNX.xcframework (STT/TTS/VAD backend)
  5. Copies frameworks to sdk/runanywhere-swift/Binaries/

Understanding useLocalNatives

The SDK has two modes controlled by useLocalNatives in the root Package.swift (the flag does not exist in sdk/runanywhere-swift/Package.swift, which is always-local):

Mode Setting Description
Local useLocalNatives = true Uses XCFrameworks from Binaries/ (for development)
Remote useLocalNatives = false Downloads XCFrameworks from GitHub releases (for end users)

Testing with the iOS Sample App

The recommended way to test SDK changes is with the sample app:

# 1. Ensure XCFrameworks are built (from previous step)

# 2. Navigate to the sample app
cd examples/ios/RunAnywhereAI

# 3. Open in Xcode
open RunAnywhereAI.xcodeproj

# 4. If Xcode shows package errors, reset caches:
#    File > Packages > Reset Package Caches

# 5. Build and Run (Cmd+R)

The sample app’s Package.swift references the local SDK, which in turn uses the local frameworks from Binaries/. This creates a complete local development loop:

Sample App → Local Swift SDK → Local XCFrameworks (Binaries/)
                                      ↑
                         Built by sdk/runanywhere-swift/scripts/build-core-xcframework.sh

Development Workflow

After modifying Swift SDK code:

After modifying runanywhere-commons (C++ code):

./sdk/runanywhere-swift/scripts/build-core-xcframework.sh

Running Tests

swift test

Code Style

The project uses SwiftLint for code style enforcement:

brew install swiftlint
swiftlint

Pull Request Process

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/my-feature
  3. Make your changes with tests
  4. Ensure all tests pass: swift test
  5. Run linter: swiftlint
  6. Commit with a descriptive message
  7. Push and open a Pull Request

Reporting Issues

Open an issue on GitHub with:

Contact

License

Copyright 2025 RunAnywhere AI. All rights reserved.

See the repository for license terms. For commercial licensing inquiries, contact san@runanywhere.ai.

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