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

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
```
:

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

For the GPU backend, add both lines inside

<application>

AndroidManifest.xml

. Add the

INTERNET

permission outside

<application>

when the app downloads the model:

<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

.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) : 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:

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)

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):

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

Symptom	Cause	Fix
First reply is slow.	`initialize` ran on first use.	Load at app start and set `cacheDir` .
First GPU reply ends in `Can not find OpenCL library on this device` .	The manifest lacks the `uses-native-library` entries.	Add both entries from Step 1 inside `<application>` .
`close` throws `IllegalStateException` .	The engine or conversation was closed twice, or `initialize` threw.	Store the engine only after `initialize` returns, and set each field to `null` after `close` .
Build fails with `[Hilt] Provided Metadata instance has version 2.4.0` .	Hilt 2.59.2 can't read the Kotlin 2.4 metadata in `litertlm-android` 0.18.0.	Follow Path A or Path B in Step 1.

Checklist

[ ] Is

minSdk

set to

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