MediaPipe

Run .task / .bin models on Android, iOS and Web through Google's MediaPipe (tasks-genai) engine — chat templates handled internally, GPU-only on Web.

flutter_gemma's engines are pluggable: you register them in FlutterGemma.initialize(...), and the registry picks one per model by its declared ModelFileType. flutter_gemma_mediapipe is the engine for Google's MediaPipe runtime (tasks-genai) — it runs .task bundles (and .bin). A .task archive packages the model's .tflite weights, tokenizer, and metadata together, and MediaPipe applies each model's chat template internally, so you feed plain messages and it handles the formatting.

Platforms#

Platform	Support	Runtime
Android	✅	`MediaPipeTasksGenAI` (Gradle)
iOS	✅	`MediaPipeTasksGenAI` (CocoaPods) — requires iOS 16.0+
Web	✅	`@mediapipe/tasks-genai` (CDN)
Desktop (macOS/Windows/Linux)	❌	no MediaPipe engine — use LiteRT-LM or ONNX

Mobile + Web only. There is no MediaPipe engine on desktop; for macOS/Windows/Linux run the .litertlm format via LiteRT-LM instead.

Setup#

Add the package and register MediaPipeEngine() at startup, alongside any other engines your app uses:

dependencies:
  flutter_gemma: latest_version
  flutter_gemma_mediapipe: latest_version   # MediaPipe .task engine

import 'package:flutter_gemma/flutter_gemma.dart';
import 'package:flutter_gemma_mediapipe/flutter_gemma_mediapipe.dart';

await FlutterGemma.initialize(
  inferenceEngines: [MediaPipeEngine()],
);

MediaPipeEngine handles ModelFileType.task / .bin models. Pass it alongside other engines (e.g. LiteRtLmEngine from flutter_gemma_litertlm) if your app uses both formats — the registry routes each model to the right one.

iOS: this package requires iOS 16.0+ (MediaPipe GenAI's floor — it is the only flutter_gemma package that needs it). Set platform :ios, '16.0' in the Podfile and the Runner's iOS Deployment Target in Xcode, and use use_frameworks! :linkage => :static (MediaPipe ships static xcframeworks). Android needs no extra setup — the MediaPipe Gradle deps are bundled.

Install a `.task` model#

installModel defaults fileType to ModelFileType.task, so a .task model needs no explicit file-type declaration:

await FlutterGemma.installModel(
  modelType: ModelType.gemmaIt,
).fromNetwork('https://.../gemma3-1b-it.task').install();

final model = await FlutterGemma.getActiveModel(maxTokens: 4096);
final session = await model.createSession();
await session.addQueryChunk(const Message(text: 'Hello!', isUser: true));
final response = await session.getResponse();

Models can also come from an asset, a bundled file, or a local path — see Models for every source and the model catalog.

Web setup#

On Web the MediaPipe runtime is loaded from a CDN. Add this to your app's web/index.html inside a <script type="module"> (before the Flutter bootstrap), exposing the symbols on window:

<script type="module">
import { FilesetResolver, LlmInference } from 'https://cdn.jsdelivr.net/npm/@mediapipe/[email protected]';
window.FilesetResolver = FilesetResolver;
window.LlmInference = LlmInference;
</script>

The pinned version is @mediapipe/[email protected]. Web runs GPU-only — there is no CPU backend in the browser, so PreferredBackend.gpu is required. Vision works on Web with Gemma 4's .task web build; thinking mode (extraContext) is not available through MediaPipe on Web.

Backends#

`PreferredBackend`	Android	iOS	Web
`cpu`	✅	✅	❌
`gpu`	✅	✅	✅ (required)

Pass the backend when you open the model:

final model = await FlutterGemma.getActiveModel(
  maxTokens: 4096,
  preferredBackend: PreferredBackend.gpu,
);