Welcome to Dartantic!
Hello and welcome to Dartantic!
The dartantic_ai package is an agent framework inspired by pydantic-ai and designed to make building client and server-side apps in Dart with generative AI easier and more fun!
Why Dartantic?
Dartantic was born out of frustration with not being able to easily use generative AI in my Dart and Flutter apps without doing things in a very different way based on the model I chose and the type of app I was building, i.e. GUI, CLI or server-side. It's all Dart -- why can't I use all the models with a single API across all the apps?
As an example of the kinds of apps that I wanted to build, consider CalPal, a Flutter app that uses Dartantic to build an agentic workflow for managing a user's calendar. Check out this screenshot:
In about ~300 LOC, CalPal is able to figure out the events in my schedule based on an English phrase. To do this, it first has to figure out the local date and time to understand what "today" means anyway and then uses the result of that against the Zapier MCP server connected to my Google Calendar.
That multi-step tool usage is all built into Dartantic and it's what makes it an "agentic" framework.
Oh, and then just for fun, I asked CalPal to add a calendar event to my calendar based on a picture of the events at my local pool. I can't imagine the person-years of effort that would've been required to build this without generative AI, but I couldn't rest until I had that kind of power for all my Dart and Flutter apps.
Combine that with pydantic-ai for inspiration and Dartantic was born.
Enjoy!
What is Dartantic AI?
One API, 11 provider configurations out of the box:
- 5 native implementations: OpenAI, Google (Gemini), Anthropic (Claude), Mistral, Ollama
- 6 OpenAI-compatible: OpenRouter, Together AI, Cohere, Lambda, Google-OpenAI, Ollama-OpenAI
- Chat + Embeddings: OpenAI, Google, Mistral, Cohere all support both
Switch providers with one line of code. All models. Single API. Enjoy!
Basic Chat
import 'package:dartantic_ai/dartantic_ai.dart';
final agent = Agent('claude'); // or 'openai', 'gemini', etc.
final result = await agent.send('Hello!');
print(result.output);
Streaming
await for (final chunk in agent.sendStream('Tell me a story')) {
stdout.write(chunk.output);
}
Tools
final weatherTool = Tool(
name: 'get_weather',
description: 'Get weather for a location',
inputSchema: JsonSchema.object({
'location': JsonSchema.string(),
}),
onCall: (args) async => {'temp': 72, 'condition': 'sunny'},
);
final agent = Agent('openai', tools: [weatherTool]);
final result = await agent.send("Weather in Seattle?");
Embeddings
final agent = Agent('openai');
final embed = await agent.embedQuery('Hello world');
print(embed.embeddings.length); // 1536
Multi-Provider Conversations
final history = <ChatMessage>[];
// Start with Gemini
final gemini = Agent('google');
final result1 = await gemini.send('Hi, I\'m Alice', history: history);
history.addAll(result1.messages);
// Switch to Claude
final claude = Agent('anthropic');
final result2 = await claude.send('What\'s my name?', history: history);
print(result2.output); // "Your name is Alice"
Key Features
- Unified API: Same code for all providers
- Agent-Based: Automatic tool execution and state management
- Multi-Provider: Share conversations between providers
- Streaming: Real-time responses
- Tools: Extend with custom functions
- Typed Output: Get structured data back
- Embeddings: Vector operations with same Agent
- MCP Support: Connect to external tools
Next Steps
- Quick Start Guide
- Providers - Available providers and capabilities