Quick Start

Install and run a model in your language of choice.

Each badge links to its platform setup. See the full Installation Guide for all options.

Flutter

Install in pubspec.yaml:

dependencies:
  xybrid_flutter: ^0.6.0

Run a model:

final model = await Xybrid.model('kokoro-82m').load();
final result = await model.run(XybridEnvelope.text('Hello world'));
// result → 24kHz WAV audio

Kotlin

Install in build.gradle.kts:

dependencies {
    implementation("ai.xybrid:xybrid-kotlin:0.6.0")
}

Run a model:

val model = Xybrid.model("kokoro-82m").load()
val result = model.runAsync(Envelope.text("Hello world"))
// result → 24kHz WAV audio

Swift

Install in Package.swift:

dependencies: [
    .package(url: "https://github.com/xybrid-ai/xybrid.git", from: "0.6.0")
]

Run a model:

let model = try await Xybrid.model("kokoro-82m").load()
let result = try await model.runAsync(envelope: Envelope.text("Hello world"))
// result → 24kHz WAV audio

Unity

Install via OpenUPM (recommended):

openupm add ai.xybrid.sdk

Or add https://package.openupm.com as a scoped registry for scope ai.xybrid.

Install manually — add the git subfolder as a UPM package:

https://github.com/xybrid-ai/xybrid.git?path=/bindings/unity

Native libraries download automatically on import. See the Unity SDK guide for details.

Run a model:

var model = XybridClient.LoadModel("kokoro-82m");
var result = model.Run(Envelope.Text("Hello world"));
// result → 24kHz WAV audio

Rust

Install in Cargo.toml:

[dependencies]
xybrid = "0.6.0"

Run a model:

let model = Xybrid::model("kokoro-82m").load()?;
let result = model.run(&Envelope::text("Hello world"))?;
// result → 24kHz WAV audio

CLI

Install:

# macOS / Linux
curl -sSL https://raw.githubusercontent.com/xybrid-ai/xybrid/master/install.sh | sh

# Windows (PowerShell)
irm https://raw.githubusercontent.com/xybrid-ai/xybrid/master/install.ps1 | iex

Run a model:

xybrid run --model kokoro-82m --input-text "Hello world" -o output.wav

# voice-assistant.yaml
name: voice-assistant
stages:
  - model: whisper-tiny-ggml  # Speech → text
  - model: qwen2.5-0.5b       # Process with LLM
  - model: kokoro-82m         # Text → speech

xybrid run --config voice-assistant.yaml --input-audio question.wav -o response.wav

final pipeline = Xybrid.pipeline(yaml: yamlString);
final result = await pipeline.run(XybridEnvelope.audio(bytes: audioBytes, sampleRate: 16000));

// Multi-model pipeline (MMP) support coming soon — use single model loading for now

// Multi-model pipeline (MMP) support coming soon — use single model loading for now

// Multi-model pipeline (MMP) support coming soon — use single model loading for now

let pipeline = Xybrid::pipeline(&yaml_string).load()?;
pipeline.load_models()?;
let result = pipeline.run(&Envelope::audio(audio_bytes))?;

Supported Models

All models run entirely on-device. No cloud, no API keys required. Browse the full catalogue at xybrid.ai/models, or run xybrid models list.

Speech-to-Text

Text-to-Speech

LLM

Vision-Language

Tool calling: see the Tool Calling guide.

# Install xybrid skills into your project
curl -sSL https://raw.githubusercontent.com/xybrid-ai/xybrid/master/tools/scripts/install-skills.sh | sh

# Generate model_metadata.json from a HuggingFace model
claude /xybrid-init hexgrad/Kokoro-82M-v1.0-ONNX

{
  "model_id": "my-model",
  "version": "1.0",
  "execution_template": { "type": "Onnx", "model_file": "model.onnx" },
  "preprocessing": [],
  "postprocessing": [],
  "files": ["model.onnx"],
  "metadata": { "task": "text-generation" }
}

Features

SDK MMP support: Flutter ✅ · Rust ✅ · Kotlin 🔜 · Swift 🔜 · Unity 🔜

Tool calling: local models call functions you define — your tools are plain data and the loop is your code. See the Tool Calling guide.

Why Xybrid?

• Private / offline — inference runs on-device and keeps working with no network after the first model download.
• One API, five platforms — iOS, Android, macOS, Linux, Windows.
• Many backends, one API — ONNX Runtime, llama.cpp (GGUF), whisper.cpp, Candle and CoreML.
• Multi-model pipelines — chain ASR → LLM → TTS in one call.
• Tool calling — local models call functions you define, on every SDK.
• Swap models without shipping an app — models resolve from the registry at runtime and cache on device.
• Cloud fallback — opt in per run (docs).
• Telemetry you control — opt-in behind an API key (docs).
• Hardware acceleration — Metal and the Apple Neural Engine on Apple, opt-in Vulkan on Linux; Android and Windows are CPU today (docs).

How it compares

Community

• Documentation
• Discord
• X (Twitter)
• GitHub Issues

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines on setting up your development environment, submitting pull requests, and adding new models.

New here? Browse the good first issue label for small, self-contained tasks. Tasks are also grouped by area: area: core, area: sdk, area: examples, area: bindings, area: tests. Medium-difficulty tasks live under help wanted.

Star History

License

Apache License 2.0 — see LICENSE for details.