genkit_shelf

Shelf integration for Genkit Dart.

Usage

Serving Flows

The easiest way to serve your flows is using startFlowServer:

import 'package:genkit/genkit.dart';
import 'package:genkit_shelf/genkit_shelf.dart';

void main() async {
  final ai = Genkit();

  final flow = ai.defineFlow(
    name: 'myFlow',
    inputSchema: .string(),
    outputSchema: .string(),
    fn: (String input, _) async => 'Hello $input',
  );

  await startFlowServer(
    flows: [flow],
    port: 8080,
  );
}

Serving Models

You can also serve AI models (and other actions) using startFlowServer or shelfHandler:

import 'package:genkit/genkit.dart';
import 'package:genkit_google_genai/genkit_google_genai.dart';
import 'package:genkit_shelf/genkit_shelf.dart';

void main() async {
  // Just an example, can use Anthropic, OpenAI, etc. models
  final geminiApi = googleAI();
  final geminiFlash = geminiApi.model('gemini-2.5-flash');

  await startFlowServer(
    flows: [geminiFlash],
    port: 8080,
  );
}

Existing Shelf Application

You can also integrate Genkit flows and actions (like models) into an existing Shelf application using shelfHandler. This allows you to use your own routing, middleware, and server configuration.

import 'package:genkit/genkit.dart';
import 'package:genkit_google_genai/genkit_google_genai.dart';
import 'package:genkit_shelf/genkit_shelf.dart';
import 'package:shelf/shelf.dart';
import 'package:shelf/shelf_io.dart' as io;
import 'package:shelf_router/shelf_router.dart';

void main() async {
  final ai = Genkit();

  final flow = ai.defineFlow(
    name: 'myFlow',
    inputSchema: .string(),
    outputSchema: .string(),
    fn: (String input, _) async => 'Hello $input',
  );

  final geminiApi = googleAI();
  final geminiFlash = geminiApi.model('gemini-2.5-flash');

  // Create a Shelf Router
  final router = Router();

  // Mount handlers
  router.post('/myFlow', shelfHandler(flow));
  router.post('/geminiFlash', shelfHandler(geminiFlash));

  // Add other application routes
  router.get('/health', (Request request) => Response.ok('OK'));

  // Start the server
  await io.serve(router.call, 'localhost', 8080);
}

Consuming Remote Models

When you serve a model using genkit_shelf, you can consume it from another Genkit application using defineRemoteModel:

final ai = Genkit();

final remoteModel = ai.defineRemoteModel(
  name: 'myRemoteModel',
  url: 'http://localhost:8080/googleai/gemini-2.5-flash',
);

final response = await ai.generate(
  model: remoteModel,
  prompt: 'Hello!',
);

print(response.text);