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Published 20 days ago • Latest: 0.3.0
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A Flutter AI package supporting cloud (Gemini) and offline local models — clean API, streaming, smart defaults, and built-in prompt engineering.

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flutter_mind

Any AI. One interface.

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Why flutter_mind? #

Most AI packages for Flutter just wrap the API — you still have to write the prompts, handle errors, manage tokens, and figure out streaming yourself.

flutter_mind does more:

  • 🔌 One API for all providers — same interface for cloud and local models
  • 💬 Multi-turn chat — conversation history with automatic token trimming
  • ⚡ Streaming — typing-effect UI out of the box
  • 🧠 Thinking models — built-in support for reasoning budgets
  • 🛡️ Safe by default — input validation, retry logic, and clear error messages
  • 🎯 Zero Firebase required — API key for cloud, or fully offline with no key at all

Supported Providers #

Provider Status Models
Google Gemini ✅ v1 Flash 2.5, Pro 2.5, Flash-Lite, and more
Local Model (offline) ✅ v1 Any .gguf — Qwen, Llama, Gemma, Phi, Mistral, and more
OpenAI 🔜 v2 GPT-4o, GPT-4o Mini
Anthropic Claude 🔜 v2 Sonnet, Opus, Haiku
Grok 🔜 v2 —
DeepSeek 🔜 v2 —

Installation #

dependencies:
  flutter_mind: ^0.1.0

flutter pub get

Quick Start #

import 'package:flutter_mind/flutter_mind.dart';

void main() {
  FlutterMind.init(
    engine: GeminiEngine(apiKey: 'YOUR_GEMINI_API_KEY'),
  );
  runApp(MyApp());
}

// Anywhere in your app — no imports, no passing around
final response = await FlutterMind.send(userMessage: 'suggest a game');
print(response.text);

Three lines in main(). Done.

Getting Your API Key #

Google Gemini — Free tier available #

  1. Go to aistudio.google.com/apikey
  2. Sign in with your Google account
  3. Click Create API Key — no credit card required

OpenAI (coming in v2) #

  1. Go to platform.openai.com → API Keys → Create new secret key

Anthropic Claude (coming in v2) #

  1. Go to console.anthropic.com → API Keys → Create Key

Local Model — No API key needed ✅ #

No account, no key, no internet required. Just a .gguf model file on the device. See the Local Model (Offline) section for full setup.

Usage #

Send a message #

final response = await FlutterMind.send(userMessage: 'what is Flutter?');

print(response.text);          // the response text
print(response.totalTokens);   // total tokens used
print(response.inputTokens);   // tokens in your message
print(response.outputTokens);  // tokens in the response

Streaming — typing effect UI #

FlutterMind.stream(userMessage: 'tell me a story').listen((chunk) {
  setState(() => text += chunk); // text appears word by word
});

Multi-turn chat — conversation with memory #

final history = <ChatMessage>[];

// First turn
final r1 = await FlutterMind.send(
  userMessage: 'my name is Osama',
  history: history,
);
history.add(ChatMessage.user('my name is Osama'));
history.add(ChatMessage.model(r1.text));

// Second turn — model remembers the name
final r2 = await FlutterMind.send(
  userMessage: 'what is my name?',
  history: history,
  maxHistoryMessages: 20, // oldest turns are dropped automatically
);
print(r2.text); // "Your name is Osama"

Engine configuration #

Set your defaults once — every call uses them automatically:

FlutterMind.init(
  engine: GeminiEngine(
    apiKey: 'YOUR_KEY',
    config: GeminiConfig(
      model: GeminiModel.flash25,
      systemPrompt: Prompt(role: 'game suggestion assistant'),
      temperature: 0.8,
      maxOutputTokens: 500,
    ),
  ),
);

Prompt engineering #

Control how the model behaves with the Prompt class — from one field to full expert config.

Tier 1 — Minimal

GeminiConfig(
  systemPrompt: Prompt(role: 'game suggestion assistant'),
)

Tier 2 — Standard

Prompt(
  role: 'game assistant',
  format: ResponseFormat.numberedList,
  maxItems: 3,
  language: ResponseLanguage.auto, // detects Arabic vs English per message
  constraints: ['mobile only', 'no violent games'],
)

Tier 3 — Advanced

Prompt(
  role: 'mobile game expert for Egyptian users',
  goal: 'suggest games that match the user mood and age',
  constraints: ['mobile only', 'no violent games', 'available in Egypt'],
  format: ResponseFormat.numberedList,
  maxItems: 3,
  language: ResponseLanguage.auto,
  tone: ResponseTone.friendly,
  audience: 'Egyptian teenagers',
  examples: [
    PromptExample(input: 'fun game', output: 'Hollow Knight — platformer'),
    PromptExample(input: 'relaxing', output: 'Stardew Valley — farming sim'),
  ],
)

Tier 4 — Expert

Prompt(
  role: 'game assistant',
  chainOfThought: true,
  chainSteps: ['identify user mood', 'match game genre', 'select 3 games'],
  preventInjection: true,        // resists jailbreak attempts
  responseAnchor: 'Here are your top 3 games:',
  negativePatterns: ['never suggest PC games'],
  compressed: false,             // verbose output for complex reasoning
)

Ready-made presets

// Use directly
GeminiConfig(systemPrompt: AiPreset.chat)
GeminiConfig(systemPrompt: AiPreset.summarizer)
GeminiConfig(systemPrompt: AiPreset.codeHelper)
GeminiConfig(systemPrompt: AiPreset.stepByStep)

// Customize one field
GeminiConfig(
  systemPrompt: AiPreset.chat.copyWith(role: 'Egyptian culture guide'),
)

Stop sequences — pair with the prompt

final prompt = Prompt(
  format: ResponseFormat.numberedList,
  maxItems: 3,
);

GeminiConfig(
  systemPrompt: prompt,
  stopSequences: prompt.stopSequences, // → ['[END]'] — model stops exactly here
)

Per-call config override #

Override only what changes for a single call — defaults stay untouched:

// Uses your default config
await FlutterMind.send(userMessage: 'suggest a game');

// Overrides just for this one call
await FlutterMind.send(
  userMessage: 'solve this complex math problem',
  config: GeminiConfig(
    model: GeminiModel.pro25,
    temperature: 0.1,
    thinkingLevel: ThinkingLevel.deep,
  ),
);

Thinking models #

Let the model reason before answering — better results on hard problems:

GeminiConfig(
  model: GeminiModel.pro25,
  thinkingLevel: ThinkingLevel.moderate,
)

// Or set an exact token budget
GeminiConfig(
  model: GeminiModel.pro25,
  thinkingLevel: CustomThinkingBudget(tokens: 4000),
)
Level Tokens Best For
ThinkingLevel.none 0 Fastest, cheapest
ThinkingLevel.light 512 Simple reasoning
ThinkingLevel.moderate 2,048 Coding, math
ThinkingLevel.deep 8,192 Complex problems
ThinkingLevel.max 24,576 Hardest problems

Access the model's reasoning in the response:

final response = await FlutterMind.send(
  userMessage: 'explain quantum entanglement simply',
  config: GeminiConfig(
    model: GeminiModel.pro25,
    thinkingLevel: ThinkingLevel.moderate,
  ),
);

print(response.text);         // the answer
print(response.thinkingText); // how it got there (null if not a thinking model)
print(response.hasThinking);  // true / false

Structured JSON output #

Force the model to always return valid, parseable JSON:

GeminiConfig(
  model: GeminiModel.flash25,
  responseMimeType: 'application/json',
  responseSchema: {
    'type': 'object',
    'properties': {
      'name':   {'type': 'string'},
      'genre':  {'type': 'string'},
      'rating': {'type': 'number'},
    },
    'required': ['name', 'genre', 'rating'],
  },
)

beforeSend hook — inject runtime context #

Enrich every message with user profile, location, or app state before it reaches the AI:

FlutterMind.init(
  engine: GeminiEngine(apiKey: 'YOUR_KEY'),
  beforeSend: (message) async {
    final user = await UserService.getProfile();
    final location = await LocationService.current();
    return 'User: ${user.name}, Location: $location\n\n$message';
  },
);

// User types: "what restaurants are near me?"
// Model receives: "User: Osama, Location: Cairo, Egypt\n\nwhat restaurants are near me?"

Token management #

// Accurate count — calls the API, always free
final tokens = await FlutterMind.countTokens(userMessage: longText);
if (tokens > 100000) print('Message too long');

// Rough estimate — instant, no API call
// Note: Arabic text uses 2–3× more tokens than English
final estimate = FlutterMind.estimateTokens(message);

Retry configuration #

GeminiEngine(
  apiKey: 'YOUR_KEY',

  // Default — 2 attempts on 429, 500, 503
  retry: RetryConfig(),

  // Custom
  retry: RetryConfig(
    maxAttempts: 5,
    delay: Duration(seconds: 2),
    retryOn: {429, 503},
  ),

  // Disable
  retry: RetryConfig.none,
)

Availability check #

if (!await FlutterMind.isAvailable()) {
  showDialog(context, 'AI is currently unavailable. Try again later.');
  return;
}

Multiple engines in one app #

Use FlutterMindClient directly when you need more than one engine:

final chatClient = FlutterMindClient(
  engine: GeminiEngine(
    apiKey: 'YOUR_KEY',
    config: GeminiConfig(
      model: GeminiModel.flash25,
      systemPrompt: Prompt(role: 'friendly chat assistant'),
    ),
  ),
);

final summaryClient = FlutterMindClient(
  engine: GeminiEngine(
    apiKey: 'YOUR_KEY',
    config: GeminiConfig(
      model: GeminiModel.pro25,
      systemPrompt: Prompt(role: 'document summarizer', tone: ResponseTone.concise),
      temperature: 0.1,
    ),
  ),
);

await chatClient.send(userMessage: 'hello');
await summaryClient.send(userMessage: longDocument);

Local Model (Offline) #

Run AI entirely on the user's device — no API key, no internet, no cost per request. Uses llama.cpp under the hood via Dart FFI.

Platform support #

Platform Support Notes
Android ✅ One-time build.gradle setup
iOS ✅ Manual Xcode setup required
Linux ✅ Manual cmake build required
macOS ✅ Manual cmake build required
Windows ✅ Manual cmake build required
Web ❌ Dart FFI not supported on web

Step 1 — Get a model file #

Models are .gguf files downloaded at runtime to the device. They are not bundled in the app (too large for app stores).

Recommended starter models from HuggingFace:

Model Size Speed Quality
Qwen2.5-1.5B-Instruct-Q4_K_M.gguf ~1 GB ⚡ Very fast Good
Qwen2.5-3B-Instruct-Q4_K_M.gguf ~2 GB Fast Better
gemma-3-1b-it-Q4_K_M.gguf ~0.8 GB ⚡ Very fast Good
Phi-3-mini-4k-instruct-q4.gguf ~2.2 GB Fast Better

Download in your app on first launch (show a progress bar):

import 'package:path_provider/path_provider.dart';
import 'dart:io';

Future<String> downloadModel() async {
  final dir = await getApplicationDocumentsDirectory();
  final modelPath = '${dir.path}/models/qwen2.5-1.5b.gguf';

  if (File(modelPath).existsSync()) return modelPath; // already downloaded

  await Directory('${dir.path}/models').create(recursive: true);

  final request = await HttpClient().getUrl(Uri.parse(
    'https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF/resolve/main/qwen2.5-1.5b-instruct-q4_k_m.gguf',
  ));
  final response = await request.close();
  await response.pipe(File(modelPath).openWrite());

  return modelPath;
}

Add path_provider to your pubspec.yaml dependencies.

Step 2 — Platform setup #

Android

2a. Download these three files and place them in your app as shown:

Download Save as
CMakeLists.txt android/app/CMakeLists.txt
local_model.h android/native/include/local_model.h
local_model.cpp android/native/src/local_model.cpp

2b. Add externalNativeBuild to your android/app/build.gradle:

android {
    defaultConfig {
        externalNativeBuild {
            cmake {
                abiFilters 'arm64-v8a', 'x86_64'
                arguments '-DANDROID_STL=c++_shared'
            }
        }
    }
    externalNativeBuild {
        cmake {
            path 'CMakeLists.txt'   // the file you copied in step 2a
            version '3.18.1'
        }
    }
}

2c. Run flutter build apk — Gradle downloads llama.cpp and compiles the library automatically. This takes 5–10 minutes on the first build, then it is cached.

Desktop (Linux / macOS / Windows)

2a. Navigate to the package source and build the library:

cd ~/.pub-cache/hosted/pub.dev/flutter_mind-0.1.0/lib/src/core/engines/local/native
cmake -B build
cmake --build build --config Release

2b. Copy the built library next to your app executable:

# Linux
cp build/liblocal_model.so /path/to/your/app/build/linux/x64/release/bundle/

# macOS
cp build/liblocal_model.dylib /path/to/your/app/build/macos/Build/Products/Release/

# Windows
cp build/Release/local_model.dll /path/to/your/app/build/windows/x64/runner/Release/

Run flutter build linux (or macos / windows) as normal after this.

iOS

2a. Build the static library from the package source on a Mac:

cd ~/.pub-cache/hosted/pub.dev/flutter_mind-0.1.0/lib/src/core/engines/local/ios/Classes
cmake -B build -DCMAKE_SYSTEM_NAME=iOS -DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
cmake --build build --config Release

2b. In Xcode:

  • Drag the built liblocal_model.a into your project
  • Add it to Link Binary with Libraries in your target's Build Phases
  • Add the include/ folder to Header Search Paths

Step 3 — Use it in Dart #

Minimal:

final modelPath = await downloadModel();

final engine = LocalEngine(
  config: LocalConfig(modelPath: modelPath),
);

final response = await engine.send(userMessage: 'Hello!');
print(response.text);

engine.dispose(); // free model memory when done

With full config:

final engine = LocalEngine(
  config: LocalConfig(
    modelPath: modelPath,
    systemPrompt: Prompt(role: 'helpful assistant'),
    modelType: LocalModelType.qwen,  // skip auto-detection
    temperature: 0.8,
    maxOutputTokens: 512,
    contextSize: 4096,
    repeatPenalty: 1.1,
    topP: 0.9,
    topK: 40,
    seed: 42,        // fixed seed for reproducible output
    threads: 4,      // CPU threads — 0 = auto-detect
  ),
);

Streaming (yields the full response at once — true token streaming coming in v2):

engine.stream(userMessage: 'Tell me a story').listen((chunk) {
  setState(() => text += chunk);
});

With conversation history:

final history = <ChatMessage>[];

final r1 = await engine.send(userMessage: 'My name is Osama');
history.add(ChatMessage.user('My name is Osama'));
history.add(ChatMessage.model(r1.text));

final r2 = await engine.send(
  userMessage: 'What is my name?',
  history: history,
);
print(r2.text); // "Your name is Osama"

LocalConfig reference #

Parameter Type Default Description
modelPath String required Absolute path to the .gguf file
systemPrompt Prompt? null Model persona and instructions
modelType LocalModelType auto Chat template format (auto-detected from file metadata)
temperature double? 0.7 Creativity — 0.0 deterministic, 2.0 very random
maxOutputTokens int? 512 Max tokens to generate per response
contextSize int? 2048 How many tokens of history the model remembers
repeatPenalty double? 1.1 Penalizes repeated words — range 1.0–2.0
topP double? 0.9 Nucleus sampling threshold
topK int? 40 Limits token pool size
seed int? random Fixed seed for reproducible output
threads int? auto CPU threads — 0 auto-detects from device

LocalModelType values #

Value Models
LocalModelType.auto Detects from .gguf metadata — recommended
LocalModelType.qwen Qwen 2, 2.5
LocalModelType.llama3 Llama 3, 3.1, 3.2
LocalModelType.gemma Gemma 1, 2, 3
LocalModelType.phi Phi 2, 3, 4
LocalModelType.mistral Mistral family
LocalModelType.deepSeek DeepSeek family

Capabilities #

Feature Status
Text chat ✅
System prompt ✅
Conversation history ✅
Streaming ✅ (full response at once — true token streaming coming in v2)
Vision / image input ❌ coming in v2
Audio ❌ coming in v2

Gemini Models #

Constant Model ID Status Best For
GeminiModel.flash25 gemini-2.5-flash ✅ Stable General use — recommended default
GeminiModel.flash25Lite gemini-2.5-flash-lite ✅ Stable High volume, lowest cost
GeminiModel.pro25 gemini-2.5-pro ✅ Stable Complex reasoning, analysis
GeminiModel.flash3Preview gemini-3-flash-preview ⚠️ Preview Frontier performance
GeminiModel.flash31Lite gemini-3.1-flash-lite ✅ Stable Fast, affordable, Gemini 3
GeminiModel.pro31Preview gemini-3.1-pro-preview ⚠️ Preview Most powerful available

Use CustomModel for any model not listed:

GeminiConfig(model: CustomModel('gemini-4.0-ultra'))

Error Handling #

try {
  final response = await FlutterMind.send(userMessage: message);
  print(response.text);
} on ValidationException catch (e) {
  // Bad input — empty message or exceeds 50,000 characters
  print(e.message);
} on EngineException catch (e) {
  // API error — invalid key, rate limit, network issue
  print(e.message);
  print(e.statusCode); // 401, 429, 500 ...
} on FlutterMindException catch (e) {
  // Any other flutter_mind error
  print(e.message);
}

Common status codes #

Code Meaning Fix
400 Bad request or invalid API key Check your key at aistudio.google.com/apikey
401 Unauthorized API key rejected
403 No permission Key may not have access to this model
404 Model not found Check model name or use CustomModel
429 Rate limit Add RetryConfig or upgrade your API plan
500 Server error Temporary — try again

API Key Security #

Never hardcode API keys in production apps. Anyone can extract them from your APK or IPA.

// During development — environment variable
GeminiEngine(
  apiKey: const String.fromEnvironment('GEMINI_KEY'),
)

// In production — proxy through your own backend
// Flutter app → Your server → Gemini API
// The key never leaves your server

Use flutter_dotenv for local .env files.

Roadmap #

v1 — Current #

  • ✅ Google Gemini engine
  • ✅ Local model engine (llama.cpp — offline, no API key)
  • ✅ Send and streaming
  • ✅ Multi-turn conversation history
  • ✅ Thinking model support (ThinkingLevel presets + custom budget)
  • ✅ Structured JSON output
  • ✅ Token management (accurate + estimate)
  • ✅ Retry configuration
  • ✅ Input validation
  • ✅ beforeSend hook
  • ✅ Prompt engineering system (Prompt, AiPreset, few-shot examples, chain of thought)

v2 — Coming Soon #

  • ❌ OpenAI engine
  • ❌ Anthropic Claude engine
  • ❌ Response parser (JSON → typed Dart objects)
  • ❌ True token streaming for local models
  • ❌ flutter_mind_vision (image generation)
  • ❌ flutter_mind_audio (TTS, STT)

Contributing #

Contributions are welcome. To contribute:

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/your-feature
  3. Commit your changes with a clear message
  4. Push and open a Pull Request

License #

MIT — see LICENSE for details.

Built by Mohamed Osama · Egypt 🇪🇬

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Metadata

A Flutter AI package supporting cloud (Gemini) and offline local models — clean API, streaming, smart defaults, and built-in prompt engineering.

Repository (GitHub)
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License

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dio, ffi, flutter

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