<img src="https://raw.githubusercontent.com/ferranpons/llamatik/main/assets/llamatik-new-logo.png" alt="Llamatik Logo" width="150"/> <h1 align="center">Llamatik</h1> Run AI locally on Android, iOS, Desktop and WASM — using a single Kotlin API. Offline-first · Privacy-preserving · True Kotlin Multiplatform <a href="https://central.sonatype.com/artifact/com.llamatik/library"><img src="https://img.shields.io/maven-central/v/com.llamatik/library.svg" alt="maven central badge"/></a> <img src="https://img.shields.io/badge/Kotlin-Multiplatform-blueviolet" alt="kmp badge"/> <img src="https://img.shields.io/badge/Platforms-Android%20%7C%20iOS%20%7C%20Desktop%20%7C%20WASM-green" alt="platforms badge"/> <img src="https://img.shields.io/badge/LLM-llama.cpp-orange" alt="llama.cpp badge"/> <img src="https://img.shields.io/badge/STT-whisper.cpp-blue" alt="whisper.cpp badge"/> <img src="https://img.shields.io/badge/Image-stablediffusion.cpp-purple" alt="stablediffusion badge"/> <img src="https://img.shields.io/badge/License-MIT-lightgrey" alt="license badge"/>

✨ What is Llamatik?

Llamatik is a true Kotlin Multiplatform AI library that lets you run:

🧠 Large Language Models (LLMs) via llama.cpp
🎙 Speech-to-Text (STT) via whisper.cpp
🎨 Image Generation via stable-diffusion.cpp

Fully on-device, optionally remote — all behind a unified Kotlin API.

No Python.
No required servers.
Your models, your data, your device.

Designed for privacy-first, offline-capable, and cross-platform AI applications.

🚀 Features

🔐 On-device & Private

✅ Fully offline inference via llama.cpp
✅ On-device speech recognition via whisper.cpp
✅ No network required
✅ No data exfiltration
✅ Works with GGUF (LLMs) and BIN (Whisper) models

🧠 LLM (llama.cpp)

✅ Text generation (non-streaming & streaming)
✅ Context-aware generation (system + history)
✅ Schema-constrained JSON generation
✅ Embeddings for vector search & RAG

🎙 Speech-to-Text (whisper.cpp)

✅ On-device transcription
✅ Works fully offline
✅ 16kHz mono WAV support
✅ Selectable Whisper models
✅ Integrated model download + management

🎨 Image Generation (stable-diffusion.cpp)

✅ On-device Stable Diffusion inference
✅ Text-to-image generation
✅ Fully offline
✅ Works with optimized SD models
✅ Native C++ integration

🧩 Kotlin Multiplatform

✅ Shared API across Android, iOS, Desktop
✅ Native C++ integration via Kotlin/Native
✅ Static frameworks for iOS
✅ JNI for Desktop

🌐 Hybrid & Remote

✅ Optional HTTP client for remote inference
✅ Drop-in backend server (llamatik-backend)
✅ Seamlessly switch between local and remote inference

📱 Try it now (No setup required)

Want to see Llamatik in action before integrating it?

The Llamatik App showcases:

On-device inference
Streaming generation
Speech-to-text (Whisper)
Privacy-first AI (no cloud required)
Downloadable models

🔧 Use Cases

🧠 On-device chatbots & assistants
📚 Local RAG systems
🛰️ Hybrid AI apps (offline-first, online fallback)
🎮 Game AI & procedural dialogue

🧱 Architecture (WIP)

Your App
│
▼
LlamaBridge (shared Kotlin API)
│
├─ llamatik-core     → Native llama.cpp, whisper.cpp and stablediffusion.cpp (on-device)
├─ llamatik-client   → Remote HTTP inference
└─ llamatik-backend  → llama.cpp-compatible server

Switching between local and remote inference requires no API changes — only configuration.

🔧 Requirements

iOS Deployment Target: 16.6+
Android MinSDK API: 26
Desktop: JVM 21+
WASM: Modern browser with WebAssembly support

📦 Current Versions

llama.cpp version: b7815
whisper.cpp version v1.8.3
stablediffusion.cpp version master-504-636d3cb

📦 Installation

Llamatik is published on Maven Central and follows semantic versioning.

No custom Gradle plugins
No manual native toolchain setup
Works with standard Kotlin Multiplatform projects

Repository setup

dependencyResolutionManagement {
    repositories {
        google()
        mavenCentral()
    }
}

commonMain.dependencies {
    implementation("com.llamatik:library:0.17.0")
}

⚡ Quick Start

// Resolve model path (place GGUF in assets / bundle)
val modelPath = LlamaBridge.getModelPath("phi-2.Q4_0.gguf")

// Load model
LlamaBridge.initGenerateModel(modelPath)

// Generate text
val output = LlamaBridge.generate(
"Explain Kotlin Multiplatform in one sentence."
)

🧑‍💻 Library Usage

The public Kotlin API is defined in LlamaBridge (an expect object with platform-specific actual implementations).

API surface (LlamaBridge)

@Suppress("EXPECT_ACTUAL_CLASSIFIERS_ARE_IN_BETA_WARNING")
expect object LlamaBridge {
    // Utilities
    @Composable
    fun getModelPath(modelFileName: String): String   // copy asset/bundle model to app files dir and return absolute path
    fun shutdown()                                    // free native resources

    // Embeddings
    fun initModel(modelPath: String): Boolean         // load embeddings model
    fun embed(input: String): FloatArray              // return embedding vector

    // Text generation (non-streaming)
    fun initGenerateModel(modelPath: String): Boolean // load generation model
    fun generate(prompt: String): String
    fun generateWithContext(
        systemPrompt: String,
        contextBlock: String,
        userPrompt: String
    ): String

    // Text generation (streaming)
    fun generateStream(prompt: String, callback: GenStream)
    fun generateStreamWithContext(
        systemPrompt: String,
        contextBlock: String,
        userPrompt: String,
        callback: GenStream
    )

    // Text generation with JSON schema (non-streaming)
    fun generateJson(prompt: String, jsonSchema: String? = null): String
    fun generateJsonWithContext(
        systemPrompt: String,
        contextBlock: String,
        userPrompt: String,
        jsonSchema: String? = null
    ): String

    // Convenience streaming overload (callbacks)
    fun generateStream(prompt: String, callback: GenStream)
    fun generateStreamWithContext(
        system: String,
        context: String,
        user: String,
        onDelta: (String) -> Unit,
        onDone: () -> Unit,
        onError: (String) -> Unit
    )
    
    // Text generation with JSON schema (streaming)
    fun generateJsonStream(prompt: String, jsonSchema: String? = null, callback: GenStream)
    fun generateJsonStreamWithContext(
        systemPrompt: String,
        contextBlock: String,
        userPrompt: String,
        jsonSchema: String? = null,
        callback: GenStream
    )

    fun nativeCancelGenerate()                        // cancel generation
}

interface GenStream {
    fun onDelta(text: String)
    fun onComplete()
    fun onError(message: String)
}

Speech-to-Text (WhisperBridge)

WhisperBridge exposes a small, platform-friendly wrapper around whisper.cpp for on-device speech-to-text.

The workflow is:

Download a Whisper ggml model (e.g. ggml-tiny-q8_0.bin) to local storage (the app does this for you).
Initialize Whisper once with the local model path.
Record audio to a WAV file and transcribe it.

Whisper API surface

object WhisperBridge {
    /** Returns a platform-specific absolute path for the model filename. */
    fun getModelPath(modelFileName: String): String

    /** Loads the model at [modelPath]. Returns true if loaded. */
    fun initModel(modelPath: String): Boolean

    /**
     * Transcribes a WAV file and returns text.
     * Tip: record WAV as 16 kHz, mono, 16-bit PCM for best compatibility.
     */
    fun transcribeWav(wavPath: String, language: String? = null): String

    /** Frees native resources. */
    fun release()
}

Example

import com.llamatik.library.platform.WhisperBridge

val modelPath = WhisperBridge.getModelPath("ggml-tiny-q8_0.bin")

// 1) Init once (e.g. app start)
WhisperBridge.initModel(modelPath)

// 2) Record to a WAV file (16kHz mono PCM16) using your own recorder
val wavPath: String = "/path/to/recording.wav"

// 3) Transcribe
val text = WhisperBridge.transcribeWav(wavPath, language = null).trim()
println(text)

// 4) Optional: release on app shutdown
WhisperBridge.release()

Note: WhisperBridge expects a WAV file path. Llamatik’s app uses AudioRecorder + AudioPaths.tempWavPath() to generate the WAV before calling transcribeWav(...).

🎨 Image Generation (StableDiffusionBridge)

Llamatik exposes Stable Diffusion through StableDiffusionBridge.

Workflow

Download or bundle a Stable Diffusion model.
Initialize once.
Generate images from text prompts.

Stable-Diffusion API surface

object StableDiffusionBridge {

    /** Returns absolute model path (copied from assets/bundle if needed). */
    fun getModelPath(modelFileName: String): String

    /** Loads the Stable Diffusion model. */
    fun initModel(modelPath: String): Boolean

    /**
     * Generates an image from a prompt.
     *
     * @param prompt Text prompt
     * @param width Output width
     * @param height O

Llamatik

Install / Use

README