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You can stream MediaPipe LLM Inference output into Jetpack Compose by sending each asynchronous result-listener callback into observable UI state, then collecting that state with collectAsStateWithLifecycle(). Keep the inference task and generation state in a ViewModel or another state holder—not in the rendering composable—and start generation in response to a user event.
First, note the API’s current status: Google says, “The MediaPipe LLM Inference API is in maintenance-only mode,” and recommends migrating Android projects to the LiteRT-LM Android Kotlin API. The integration below is useful when you need to maintain or extend a MediaPipe app; it is not Google’s preferred route for new Android work. Google’s Android LLM Inference guide
How MediaPipe delivers generated text
The Android API offers two different result patterns. generateResponse() returns one response, while generateResponseAsync(inputPrompt) delivers partial output through a result listener. That listener receives a partialResult and a done completion flag; the guide also documents an optional error listener. The asynchronous method is callback-based: it does not itself return a Flow or a suspending stream. Google’s Android LLM Inference guide
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To make those callbacks visible to Compose, adapt them in your app’s state holder to a MutableStateFlow or another observable state mechanism. Compose’s Android state guide recommends collecting a Flow with collectAsStateWithLifecycle(), which exposes its latest emission as Compose State while respecting the Android lifecycle. Wrapping the listener in a Flow is an app-level integration pattern, not a Flow API supplied by MediaPipe. Android Developers: State and Jetpack Compose
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Set up the model and inference task
Google’s Android guide currently specifies com.google.mediapipe:tasks-genai:0.10.27. Its quickstart uses Gemma-3 1B in a 4-bit quantized format, downloads the model artifact, and creates an inference task with LlmInference.createFromOptions(context, taskOptions) after configuring LlmInferenceOptions. These are guide-specific version and model details; check the live guide and your project’s toolchain before copying them into a build. Google’s Android LLM Inference guide
The guide says the model is too large to bundle in an APK. For development, it recommends pushing the .task artifact to a connected Android device with adb; for deployment, host the model and download it at runtime. Plan model availability and download handling as part of the app rather than treating the model as an ordinary bundled asset.
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Connect callbacks to observable UI state
Use a state holder to own the inference instance, the prompt action, and the UI state for the response, completion, and errors. The callback-to-Flow bridge below is pseudocode for the data flow, not a drop-in MediaPipe API wrapper: adapt the listener registration and error callback to the exact library version in your project.
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data class GenerationUiState(
val response: String = "",
val isGenerating: Boolean = false,
val error: String? = null
)
private val _uiState = MutableStateFlow(GenerationUiState())
val uiState: StateFlow<GenerationUiState> = _uiState
fun submit(prompt: String) {
_uiState.value = GenerationUiState(isGenerating = true)
inference.setResultListener { partialResult, done ->
_uiState.update { current ->
current.copy(
response = /* use verified snapshot-or-delta handling */,
isGenerating = !done
)
}
}
// Configure the optional error listener for the library version in use.
inference.generateResponseAsync(prompt)
}The key implementation decision is how the callback’s partialResult relates to earlier callbacks. The guide’s short example logs the value and receives done, but does not establish whether each value is a cumulative response snapshot or only a new fragment. Check the API reference or sample for the exact version before choosing to replace the displayed string or append to it. Do not assume an accumulation rule from the method name alone.
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Likewise, the consulted guide does not establish which thread invokes result or error listeners, how to cancel an in-flight generation safely, or whether one inference instance supports concurrent requests. Verify those behaviors for the version you use before adding dispatcher changes, cancellation controls, or overlapping submissions. Represent errors separately from the generated text so the UI can show failure without confusing it with a completed response.
Collect and render state in Compose
Have a composable collect the state and render it; send user actions back to the state holder. This follows Compose’s state-hoisting and unidirectional-data-flow guidance: state flows down to UI, and events flow up. Android Developers: State and Jetpack Compose
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@Composable
fun GenerationScreen(viewModel: GenerationViewModel) {
val state by viewModel.uiState.collectAsStateWithLifecycle()
Column {
Text(state.response)
if (state.isGenerating) {
CircularProgressIndicator()
}
state.error?.let { message ->
Text(message)
}
Button(onClick = { viewModel.submit(/* current prompt */) }) {
Text("Generate")
}
}
}The Compose state guide also shows androidx.lifecycle:lifecycle-runtime-compose:2.11.0 as a dependency example for lifecycle-aware collection. Confirm that version’s availability and compatibility with your Android Gradle Plugin, Kotlin, and other project dependencies before adding it. For Android apps, prefer collectAsStateWithLifecycle() over the platform-agnostic collectAsState() when lifecycle-aware collection is appropriate.
Do not start generation as a side effect of composable execution. Recomposition can happen unpredictably, in different orders, or be discarded, so composables should ideally be side-effect free. A button should emit an event to the state holder, which calls the inference API. LaunchedEffect is appropriate only when work is intentionally tied to a composition lifetime: it starts on entering composition, is cancelled on leaving, and restarts when its keys change. Android Developers: Side-effects in Compose
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Check device and configuration constraints
Google describes this API as optimized for high-end Android devices, naming Pixel 8 and Samsung S23 or later as examples, and says emulator support is not reliable. Treat this as current documented guidance, not a guarantee of equivalent performance across devices or models. Test on the physical devices relevant to your app. The guide lists maxTokens 512, topK 40, and temperature 0.8 as configuration defaults; these are settings, not performance measurements. The official sources cited here provide no latency, throughput, or quality benchmark for this Compose integration. Google’s Android LLM Inference guide
Plan for the API’s maintenance-only status
For an existing app that must keep using MediaPipe, the callback-to-state pattern lets the UI display partial results without placing inference work in a composable. For a new Android implementation or a migration decision, account for Google’s explicit recommendation to move to the LiteRT-LM Android Kotlin API. The cited guide establishes that recommendation but does not provide a verified, like-for-like migration procedure, so do not assume the two APIs have identical setup or behavior. Google’s Android LLM Inference guide
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