litertlm 0.0.4

Native LiteRT-LM bindings for Flutter across mobile, desktop, and web.

litertlm

Native LiteRT-LM bindings for Flutter across mobile, desktop, and web.

Pub: https://pub.dev/packages/litertlm

API reference: https://pub.dev/documentation/litertlm/latest/

Matches upstream LiteRT-LM v0.13.1.

Overview

This package is a lightweight bridge to the official LiteRT-LM runtimes. Powered by native LiteRT-LM distributions, it provides the same hardware acceleration and capabilities as each platform's optimized distribution, including the Swift package on iOS and macOS, the Gradle package on Android, and CLI packages on Windows.

Platform Support

Platform	Runtime artifact	Integration
iOS	Official Swift package	FFI
Android	Official Gradle package	JNI
macOS	Official Swift package	FFI
Windows *	Official CLI package	FFI
Linux	Official CLI package	FFI
Web	Official NPM package	JS interop

Note: Windows ARM64 is not currently supported because there is no official upstream package available.

Usage

Installation

$ flutter pub add litertlm

Prepare Model

You can download .litertlm models from LiteRT Community on Hugging Face or other distributors. Models such as Gemma 4 E2B/E4B are great starting points and run on most devices and platforms, including web.

Note: LiteRT-LM expects a valid file path or URL. Flutter asset identifiers are not directly supported.

Basic Inference

1. Create and initialize Engine.

final engine = Engine(
  engineConfig: const EngineConfig(
    modelPath: '/path/to/model.litertlm',
    backend: Backend.gpu(),
  ),
);

await engine.initialize();

2. Create a Conversation.

final conversation = await engine.createConversation(
  ConversationConfig(
    systemMessage: Message.system('You are concise and helpful.'),
  ),
);

3. Send a Message and read the response.

final response = await conversation.sendMessage(
  Message.user('Write one sentence about Flutter.'),
);

print(response.text);

4. Dispose native resources.

await conversation.dispose();
await engine.dispose();

Multimodal

Use Contents with multiple Content values for text, image, and audio inputs. File paths are passed through to the native runtime.

final conversation = await engine.createConversation();

final response = await conversation.sendMessage(
  Message.userContents(
    Contents([
      Content.text('Describe this image.'),
      Content.imageFile('/path/to/photo.jpg'),
    ]),
  ),
);

print(response.text);

You can also pass in-memory bytes:

final response = await conversation.sendMessage(
  Message.userContents(
    Contents([
      Content.text('Transcribe this audio.'),
      Content.audioBytes(audioBytes),
    ]),
  ),
);

Tool Use

Define tools by implementing Tool.

class WeatherTool implements Tool {
  @override
  Map<String, Object?> getToolDescription() {
    return {
      'type': 'function',
      'function': {
        'name': 'get_weather',
        'description': 'Gets the current weather for a city.',
        'parameters': {
          'type': 'object',
          'properties': {
            'city': {'type': 'string'},
          },
          'required': ['city'],
        },
      },
    };
  }

  @override
  Future<Object?> execute(Map<String, Object?> arguments) async {
    final city = arguments['city'];
    return {'city': city, 'condition': 'sunny', 'temperature_c': 22};
  }
}

Create a Conversation with the tools. Automatic tool calling is enabled by default.

final conversation = await engine.createConversation(
  ConversationConfig(
    tools: [WeatherTool()],
  ),
);

final response = await conversation.sendMessage(
  Message.user('What is the weather in Seattle?'),
);

print(response.text);

To handle tool calls yourself, disable automatic tool calling and inspect response.toolCalls.

Troubleshooting

LiteRtLmException

LiteRT-LM depends heavily on device hardware, runtime support, and model capabilities. Even when the APIs are used correctly, some actions may fail because of the user's hardware or model selection. Upstream LiteRT-LM may fail silently while logging the underlying issue in native logs. Since those logs are not practical to catch and handle internally, this package throws LiteRtLmException when LiteRT-LM clearly refuses to work. Developers can set LogSeverity to inspect detailed device logs. It is recommended to catch LiteRtLmException around the engine lifecycle and handle it as a recoverable runtime failure.

UnsupportedError

LiteRT-LM is under fast development, and not all features are immediately available on every platform. This package throws UnsupportedError when the selected platform runtime does not support the requested feature.