litert-lm

This skill provides step-by-step guidance for building an Android app that runs an open text LLM through the LiteRT-LM Kotlin API (com.google.ai.edge.litertlm) on the CPU or the GPU. The model is one .litertlm file. Images, audio, tool calling, and NPU backends aren't covered. For vision and audio models with .tflite files, suggest the litert-runtime skill.

When a LiteRT-LM class or method that this skill uses doesn't compile, you must read the LiteRT-LM Android guide and the LiteRT-LM README before changing the code.

Prerequisites

• A Kotlin Android project (the Android Studio Empty Activity template is enough). The litertlm-android 0.18.0 AAR declares minSdk 24.
• The dependency in the app-level build.gradle.kts file from Google Maven: implementation("com.google.ai.edge.litertlm:litertlm-android:0.18.0"). Google Maven lists the later releases.
• For the GPU backend, two uses-native-library entries inside <application> in AndroidManifest.xml. Add the INTERNET permission if the app downloads the model.
• The model file lives on the device's file system (hundreds of MB to a few GB), never in assets/ or in the APK.

Critical mandates

• Dependency first : Before writing code that imports com.google.ai.edge.litertlm, add the litertlm-android dependency from Step 1 if the project lacks it. Verify with ./gradlew assembleDebug.
• Background initialization : Engine(config).initialize() loads the weights and blocks for seconds. Call it on a background thread, never on the main thread.
• Single owner : One single-thread executor owns the Engine and the Conversation. Creation, streaming, cancellation, and closing run on it.
• Close once : Engine and Conversation are AutoCloseable. A second close on either throws IllegalStateException. So does close on an engine whose initialize threw. Store the engine only after initialize returns.
• Model outside the APK : Pass an absolute path to EngineConfig. Never bundle a .litertlm file in assets/.

Detailed steps

Step 1: Configure Gradle and the manifest

Search the project for libs.versions.toml, app/build.gradle.kts, and AndroidManifest.xml before editing build files. Configure minSdk and add the LiteRT-LM dependency in app/build.gradle.kts:

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android { defaultConfig { minSdk = 24 } }

dependencies { implementation("com.google.ai.edge.litertlm:litertlm-android:0.18.0") }

If the project uses Hilt, check the Dagger version first. litertlm-android 0.18.0 carries Kotlin 2.4 metadata, which Hilt 2.59.2 can't read. The build then fails in hiltJavaCompileDebug with Provided Metadata instance has version 2.4.0, while maximum supported version is 2.3.0. Pick one fix:

• Path A : Upgrade the Hilt version (com.google.dagger) to 2.60.1 or later. Ask the user before editing gradle/libs.versions.toml.
• Path B : Keep the Hilt version and add a later version of the metadata reader to the annotation processor path in app/build.gradle.kts:

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dependencies { ksp("org.jetbrains.kotlin:kotlin-metadata-jvm:2.4.21") }

For the GPU backend, add both lines inside <application> in AndroidManifest.xml. Add the INTERNET permission outside <application> when the app downloads the model:

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<uses-permission android:name="android.permission.INTERNET"/>

<application> <uses-native-library android:name="libvndksupport.so" android:required="false"/> <uses-native-library android:name="libOpenCL.so" android:required="false"/> </application>

Step 2: Pick a model from litert-community

Choose a .litertlm file from litert-community on Hugging Face. Start with litert-community/gemma-4-E2B-it-litert-lm (the file gemma-4-E2B-it.litertlm, 2.6 GB). For a 0.6 GB model, start with litert-community/Qwen3-0.6B (the file Qwen3-0.6B.litertlm). Both files run on the CPU and the GPU. A SoC suffix such as _Google_Tensor_G5 or .mediatek.mt6993 marks an NPU build, which this skill doesn't cover. The model card names the tested backends and the size.

You must read get a model before you choose a model file other than these two, before you write the in-app download code, and when no .litertlm file exists for the model.

Step 3: Put the model file on the device

Pick one path:

• Path A (local testing with `adb`) : Run the following commands only when

all three are true: adb devices lists a device, the debug build of the app is installed on it, and the user supplied the local .litertlm path. If any of them is false, don't run the commands. Report them to the user as a post-implementation step instead. Replace <path> with the user's local path and <package> with the app's application ID:

1. adb push <path> /data/local/tmp/model.litertlm
2. adb shell run-as <package> cp /data/local/tmp/model.litertlm files/

• Path B (in-app download) : Write the download code in the app. Use the

app's HTTP client to write into filesDir, or DownloadManager to write into getExternalFilesDir(null). DownloadManager can't write to internal storage. Show the download progress and check the file size against the model card before loading. Never download a model file in the agent's own session.

• Path C ([Play for On-Device AI](references/android/google/play/on-device-ai.md)) : Write the code that resolves the

path of a fast-follow or on-demand AI pack with the AI Delivery library (com.google.android.play:ai-delivery): AiPackManagerFactory.getInstance(context).getPackLocation(name)?.assetsPath(). The call returns null until the pack has finished downloading, so show a download state in that case. Creating the AI pack module and uploading the app to Play Console are post-implementation steps for the user.

On every path, pass the absolute path of the file to the engine.

Step 4: Initialize the engine off the main thread

Create the engine with EngineConfig(modelPath = ..., backend = ..., cacheDir = ...) and call initialize on a background thread:

• Backend.CPU() is the default of EngineConfig and needs nothing from the device.
• Backend.GPU() needs the two manifest lines from Step 1. Without them, the engine initializes, and the first reply ends in the error `Can not find

OpenCL library on this device`.

• cacheDir speeds up the second load.

Step 5: Implement the conversation ViewModel

Drive the engine and the conversation from a single-thread executor inside an AndroidViewModel, stream the reply, and expose immutable state to the UI:

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import android.app.Application import androidx.lifecycle.AndroidViewModel import com.google.ai.edge.litertlm.Backend import com.google.ai.edge.litertlm.Contents import com.google.ai.edge.litertlm.Conversation import com.google.ai.edge.litertlm.ConversationConfig import com.google.ai.edge.litertlm.Engine import com.google.ai.edge.litertlm.EngineConfig import java.util.concurrent.Executors import kotlin.coroutines.cancellation.CancellationException import kotlinx.coroutines.CoroutineScope import kotlinx.coroutines.SupervisorJob import kotlinx.coroutines.asCoroutineDispatcher import kotlinx.coroutines.cancel import kotlinx.coroutines.delay import kotlinx.coroutines.flow.MutableStateFlow import kotlinx.coroutines.flow.StateFlow import kotlinx.coroutines.flow.asStateFlow import kotlinx.coroutines.flow.update import kotlinx.coroutines.launch import kotlinx.coroutines.sync.Mutex import kotlinx.coroutines.sync.withLock

data class ChatState( val ready: Boolean = false, val busy: Boolean = false, val reply: String = "", val error: String? = null )

class ChatViewModel(app: Application) : AndroidViewModel(app) { private val executor = Executors.newSingleThreadExecutor() private val scope = CoroutineScope( SupervisorJob() + executor.asCoroutineDispatcher() ) private val mutex = Mutex() private var engine: Engine? = null private var conversation: Conversation? = null private val state = MutableStateFlow(ChatState()) val state: StateFlow<ChatState> = state.asStateFlow()

fun load(modelPath: String, backend: Backend = Backend.CPU()) { scope.launch { mutex.withLock { if (engine?.engineConfig?.modelPath == modelPath) return@launch release() try { val config = EngineConfig( modelPath = modelPath, backend = backend, cacheDir = getApplication<Application>().cacheDir.path ) engine = Engine(config).also { it.initialize() } conversation = checkNotNull(engine).createConversation( ConversationConfig( systemInstruction = Contents.of("You are a helpful assistant.") ) ) state.value = ChatState(ready = true) } catch (e: Exception) { release() state.value = ChatState(error = e.message) } } } }

fun send(text: String) { scope.launch { if (!state.value.ready || state.value.busy) return@launch state.update { it.copy(busy = true, reply = "", error = null) } try { val replies = checkNotNull(conversation).sendMessageAsync(text) replies.collect { message -> state.update { it.copy(reply = it.reply + message) } } } catch (e: CancellationException) { throw e } catch (e: Exception) { state.update { it.copy(error = e.message) } } finally { state.update { it.copy(busy = false) } } } }

private suspend fun release() { state.update { it.copy(ready = false) } while (state.value.busy) { conversation?.cancelProcess() delay(100) } conversation?.close() conversation = null engine?.close() engine = null _state.value = ChatState() }

override fun onCleared() { scope.launch { mutex.withLock { release() } executor.shutdown() }.invokeOnCompletion { scope.cancel() } } }

sendMessageAsync(text) returns a Flow<Message> of chunks, and toString gives a chunk's text. sendMessage(text) blocks and returns the whole reply. The conversation keeps its history, so the next send continues the chat. busy keeps the button disabled until the flow completes.

To set the sampling, pass SamplerConfig(topK = 10, topP = 0.95, temperature = 0.8) in ConversationConfig. All three parameters are required, and topP and temperature are Double.

load with the model path that is already loaded does nothing, whatever backend it asks for. A second call after a rotation keeps the loaded model, also after the app has fallen back to another backend. send runs only when the state is ready and not busy, the same test as the button. A tap that lands while release stops a reply does nothing. A tap queued behind a load reaches the model that load loaded when no reply was streaming.

Step 6: Wire the Compose screen

viewModel and collectAsStateWithLifecycle come from androidx.lifecycle:lifecycle-viewmodel-compose and androidx.lifecycle:lifecycle-runtime-compose (2.10.0):

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import androidx.compose.foundation.layout.Column import androidx.compose.foundation.rememberScrollState import androidx.compose.foundation.verticalScroll import androidx.compose.material3.Button import androidx.compose.material3.Text import androidx.compose.material3.TextField import androidx.compose.runtime.Composable import androidx.compose.runtime.LaunchedEffect import androidx.compose.runtime.getValue import androidx.compose.runtime.mutableStateOf import androidx.compose.runtime.remember import androidx.compose.runtime.setValue import androidx.compose.ui.Modifier import androidx.lifecycle.compose.collectAsStateWithLifecycle import androidx.lifecycle.viewmodel.compose.viewModel import com.google.ai.edge.litertlm.Backend

@Composable fun ChatScreen(modelPath: String, viewModel: ChatViewModel = viewModel()) { val state by viewModel.state.collectAsStateWithLifecycle() var input by remember { mutableStateOf("") } LaunchedEffect(modelPath) { viewModel.load(modelPath, Backend.GPU()) } Column { Text( state.reply, Modifier.weight(1f).verticalScroll(rememberScrollState()) ) state.error?.let { Text(it) } TextField(value = input, onValueChange = { input = it }) Button( onClick = { viewModel.send(input); input = "" }, enabled = state.ready && !state.busy ) { Text("Send") } } }

Step 7: Manage the engine lifecycle

• Keep the engine in the ViewModel for as long as its screen lives. When several screens share the engine, keep it in an application-scoped holder.
• The ViewModel keeps its own thread and scope. viewModelScope is cancelled before onCleared runs, so a close launched there wouldn't run. onCleared cancels scope once the close is done.
• release turns ready off and calls cancelProcess until the streaming reply's flow has ended. The flow ends at the latest when the reply ends on its own. release then closes the conversation and the engine, each once.
• onCleared and a load that replaces the model both call release under the Mutex, one at a time, because release suspends while a reply stops.
• To start a fresh chat, close the conversation and create a new one with createConversation. Keep the engine loaded.

Post-implementation steps for the user

Report the steps that apply to the user after the code compiles:

• When Path A in Step 3 didn't run, copy the model to the device with the two adb commands from Path A.
• When the app uses Path C in Step 3, create the AI pack module that holds the .litertlm file and upload the app to Play Console.
• When no .litertlm file exists for the model, convert the model on a workstation. This skill doesn't convert models.
• When the user asks for a complete reference app, point them to Google AI Edge Gallery.

Troubleshooting

Checklist

• \[ \] Is minSdk set to 24 or higher and is com.google.ai.edge.litertlm:litertlm-android:0.18.0 added in app/build.gradle.kts?
• \[ \] For the GPU backend, does AndroidManifest.xml declare libvndksupport.so and libOpenCL.so with android:required="false" inside <application>?
• \[ \] Is the .litertlm file loaded from an absolute path on the device's file system, never from assets/?
• \[ \] Do initialize, sendMessageAsync, cancelProcess, and close all run on the same single-thread background executor?
• \[ \] Is the conversation closed before the engine, each exactly once?
• \[ \] Does the project compile with ./gradlew assembleDebug?

Antipatterns

• Never call Engine.initialize on the Android main thread.
• Never place a .litertlm file in assets/ or ship it inside the APK.
• Never call close on an engine whose initialize threw.
• Never close the engine while a reply is streaming; call cancelProcess until the flow ends first.
• Never close Engine or Conversation from viewModelScope in onCleared; that scope is already cancelled.