litertlm #

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

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

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

Powered by LiteRT-LM v0.13.1.

Overview #

This package is a lightweight bridge to the official LiteRT-LM runtimes. It uses each platform's optimized distribution, providing the same hardware acceleration and capabilities, including the Swift package on iOS and macOS, the Maven artifact on Android, and CLI distribution on Windows.

Platform Support #

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. On some platforms, assets are bundled into the app package and may be subject to size limits such as 1 GB. In general, .litertlm model files are not a good fit for Flutter assets.

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();

Streaming #

There are two ways to get streaming responses.

Use sendMessageStream and iterate over Stream<Message>.

final text = StringBuffer();

await for (final chunk in conversation.sendMessageStream(
  Message.user('Tell me a long story.'),
)) {
  text.write(chunk.text);
}

print(text);

Use sendMessageWithCallback for better control of lifecycle.

final callbackText = StringBuffer();

final callback = MessageCallback.from(
  onMessage: (message) {
    callbackText.write(message.text);
  },
  onDone: () {
    print(callbackText);
  },
  onError: (error, stackTrace) {
    print(error);
  },
);

await conversation.sendMessageWithCallback(
  Message.user('Tell me a long story.'),
  callback,
);

Message #

The examples above use Message.text, a convenience getter for common text-only responses. For agent-style workflows, inspect the structured fields on Message directly. contents preserves multimodal content, and toolCalls exposes model-requested tool invocations.

A real-life message might look like this:

Message(
  role: Role.model,
  contents: Contents([
    Content.text('Let me check the weather in Seattle.'),
  ]),
  toolCalls: [
    ToolCall(
      name: 'get_weather',
      arguments: {'city': 'Seattle'},
    ),
  ],
);

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.