litertlm 0.0.3

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

Overview

This package is a lightweight bridge to the official LiteRT-LM runtimes. It uses the official, platform-optimized distribution for each platform, such as 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

Basic Inference

1. Create and initialize Engine.

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

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. Tool descriptions use the OpenAI-style function schema expected by LiteRT-LM.

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};
	}
}

Pass tools when creating the conversation. Automatic tool calling is enabled by default, so the model can request a tool, Dart executes it, and the final model response is returned.

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 and model capabilities. Even when the APIs are used correctly, some actions may fail because of hardware or model compatibility. Upstream LiteRT-LM tends to fail silently with NULL while logging the underlying issue in native logs. Since those logs are not practical to catch and handle directly, 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 entire engine lifecycle and handle it accordingly.

UnsupportedError

UnsupportedError means the selected platform runtime does not support the requested feature. This is different from LiteRtLmException: the call is rejected before LiteRT-LM runs because the underlying SDK has no matching capability.

Common cases:

• Web does not support per-message extraContext. Use ConversationConfig.extraContext instead.
• Web does not support Backend.npu, LoRA config, log-level configuration, or renderMessageIntoString.
• Android does not support enableConstrainedDecoding yet because the LiteRT-LM Android SDK does not expose that config field.

UnsupportedError is a Dart Error, not an Exception. If you need to catch it, use catch (error) rather than on Exception.