flutter_edge_ai_mediapipe 1.2.2

MediaPipe (.task) on-device inference engine for flutter_edge_ai (Android/iOS/Web). Opt-in package; provides an InferenceEngineProvider. Bundles Google's MediaPipe Pod/Gradle deps.

flutter_edge_ai_mediapipe

Renamed from flutter_gemma_mediapipe. Same package, new name: swap the dependency and the package:flutter_gemma_mediapipe/ imports; nothing on the device changes. See the migration guide.

MediaPipe (.task) on-device inference engine for flutter_edge_ai. Opt-in package — add it only if you run MediaPipe .task models. Android, iOS, and Web.

Google moved MediaPipe LLM Inference to maintenance-only. New features and optimizations focus on LiteRT-LM, so a new app should use .litertlm models with flutter_edge_ai_litertlm. On iOS, do not load a .task model on the GPU: Gemma 3 1B reached the app's memory limit while loading and iOS terminated the app (details).

Teach your AI assistant this package

dart run skills@ get --all

Installs the agent skills flutter_edge_ai bundles — this package depends on it, so they come with it. One of them, flutter-edge-ai-mediapipe, covers .task and .bin models on Android, iOS and web.

Usage

import 'package:flutter_edge_ai/flutter_edge_ai.dart';
import 'package:flutter_edge_ai_mediapipe/flutter_edge_ai_mediapipe.dart';

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

MediaPipeEngine handles ModelFileType.task / ModelFileType.binary (.bin, .tflite) models; pass it alongside other engines (e.g. LiteRtLmEngine from flutter_edge_ai_litertlm) if your app uses both formats.

maxTokens is the whole context (input plus output), and a small value is not raised as it is on .litertlm. maxOutputTokens is ignored (only a debug log says so); stop early with session.stopGeneration().

On Android and iOS, pass supportImage: true to createChat as well as to getActiveModel, or the image is dropped. The Gemma 3n .task files carry no audio encoder; use a .litertlm model for audio.

Sampling

A .task file ships no sampler, so an unset temperature, topK or topP comes from the model family's defaults (SamplingParams.forModelType), with an unset topK capped at 40, MediaPipe's own default; an unset randomSeed is 1. topK: 1 or temperature: 0 decodes greedily, as Phi and Hammer do by default; before 1.2.0 every family did. On Android and iOS each session starts from its seed, so vary randomSeed for a different answer to the same prompt. On web the model keeps its first session, so the sampling of a later createSession is not applied; web also has no top-p, and a topP you pass is ignored with a one-time warning.

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 your Flutter bootstrap), exposing the symbols on window:

<script type="module">
import { FilesetResolver, LlmInference } from 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai@0.10.29';
window.FilesetResolver = FilesetResolver;
window.LlmInference = LlmInference;
</script>

Load the model's -web.task build (e.g. gemma3-1b-it-int4-web.task); mobile .task files generally do not run on web. Also copy cache_api.js and opfs_helper.js from the flutter_edge_ai package's web/ directory into your app's web/ and load them with <script src="cache_api.js"></script> and <script src="opfs_helper.js"></script> — see Installation → Web. Web runs on the GPU only.

(Android needs no extra setup — the MediaPipe Gradle deps are bundled by this package.)

iOS setup

iOS needs a 16.0 minimum. MediaPipe GenAI requires it, and this is the only flutter_edge_ai package that does — core, flutter_edge_ai_litertlm, built-in AI and embeddings build from 15.0 (#441).

This package ships no Package.swift, so even an app on Swift Package Manager gets a Podfile for it. Set the floor in BOTH places: platform :ios, '16.0' in the Podfile, and iOS Deployment Target on the Runner target in Xcode. Below 16, pod install reports that specs satisfying the flutter_edge_ai_mediapipe dependency "required a higher minimum deployment target".

MediaPipe ships static xcframeworks, so the Podfile also needs use_frameworks! :linkage => :static — with the Flutter template's bare use_frameworks!, pod install fails on "transitive dependencies that include statically linked binaries".

Platforms

Platform	Support
Android	✅ com.google.mediapipe:tasks-genai 0.10.33 (Gradle)
iOS	✅ MediaPipeTasksGenAI 0.10.33 (CocoaPods) — requires iOS 16.0+; run .task models on PreferredBackend.cpu
Web	✅ @mediapipe/tasks-genai 0.10.29 (CDN, see above) — GPU only, -web.task models
Desktop	❌ (MediaPipe .task not supported on desktop — use flutter_edge_ai_litertlm)