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dart-genkit

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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Prix non confirmé★ 29 Stars GitHubRegistre mis à jour · 11 sept. 2026agent-skill

Vue d’ensemble

Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.

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AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the Genkit Dart SDK.

Project Setup
Defining Prompts & Models
Creating Custom Tools
Orchestrating Flows
Best Practices

Project Setup

Add the Genkit package to your Dart project's pubspec.yaml:

dependencies:
  genkit: ^0.1.0-preview  # Replace with the latest version
  google_generative_ai: ^0.4.0

Set up your API credentials (e.g., Gemini API key) in your environment:
```
export GEMINI_API_KEY="your_api_key_here"
```

Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

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

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}

Creating Custom Tools

Genkit agents utilize Tools to execute tasks (e.g., database queries, web scraping, mathematical calculations).

import 'package:genkit/genkit.dart';

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);

Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

import 'package:genkit/genkit.dart';

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}

Best Practices

Exhaustive Schema Definitions: Always define explicit inputSchema and outputSchema for tools and flows to ensure the LLM generates correctly structured arguments.
Environment Isolation: Do not hardcode API keys. Use environment variables or secure credential storage.
Trace Observability: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
Error Handling: Wrap tool execution in standard try-catch blocks and return error details gracefully so the agent can self-correct.

Voir la source ↗

Métadonnées du fichier

name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"

Voir le texte original

---
name: dart-genkit
description: Use when building AI workflows, tool calling agents, structured outputs, or LLM pipelines using the Genkit Dart SDK.
metadata:
    platforms: "dart"
    languages: "dart"
    category: "ai"
---

# AI Engineering with Genkit Dart

Build structured, type-safe, and observable AI-powered workflows and agents using the **Genkit Dart SDK**.

## Contents
- [Project Setup](#project-setup)
- [Defining Prompts & Models](#defining-prompts--models)
- [Creating Custom Tools](#creating-custom-tools)
- [Orchestrating Flows](#orchestrating-flows)
- [Best Practices](#best-practices)

---

## Project Setup

1. Add the Genkit package to your Dart project's `pubspec.yaml`:
   ```yaml
   dependencies:
     genkit: ^0.1.0-preview  # Replace with the latest version
     google_generative_ai: ^0.4.0
   ```
2. Set up your API credentials (e.g., Gemini API key) in your environment:
   ```bash
   export GEMINI_API_KEY="your_api_key_here"
   ```

---

## Defining Prompts & Models

Genkit separates model invocation and prompt structure from core application logic using structured prompts.

```dart
import 'package:genkit/genkit.dart';
import 'package:google_generative_ai/google_generative_ai.dart';

void main() async {
  // Initialize Genkit
  final ai = Genkit(
    model: 'gemini-1.5-flash',
    apiKey: String.fromEnvironment('GEMINI_API_KEY'),
  );

  // Invoke the model with a simple prompt
  final response = await ai.generate(
    prompt: 'Explain the concept of monads in Dart.',
  );
  
  print(response.text);
}
```

---

## Creating Custom Tools

Genkit agents utilize **Tools** to execute tasks (e.g., database queries, web scraping, mathematical calculations).

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

// 1. Define schemas for input and output
final additionInputSchema = Schema.object({
  'a': Schema.number(description: 'First number'),
  'b': Schema.number(description: 'Second number'),
});

// 2. Define the tool
final addTool = ai.defineTool(
  name: 'addNumbers',
  description: 'Adds two numbers together.',
  inputSchema: additionInputSchema,
  action: (input) async {
    final a = input['a'] as num;
    final b = input['b'] as num;
    return {'result': a + b};
  },
);
```

---

## Orchestrating Flows

Flows are executable pipelines that support structured input and output schemas, built-in telemetry, and error handling.

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

// Define the input and output schemas
final jokeRequestSchema = Schema.object({
  'topic': Schema.string(description: 'The topic for the joke'),
});

final jokeResponseSchema = Schema.object({
  'setup': Schema.string(),
  'punchline': Schema.string(),
});

// Define the Flow
final jokeFlow = ai.defineFlow(
  name: 'jokeFlow',
  inputSchema: jokeRequestSchema,
  outputSchema: jokeResponseSchema,
  action: (input) async {
    final topic = input['topic'] as String;
    
    final response = await ai.generate(
      prompt: 'Tell me a structured joke about $topic.',
      responseSchema: jokeResponseSchema,
    );
    
    return response.structuredOutput!;
  },
);

void main() async {
  // Run the flow
  final result = await jokeFlow.run({'topic': 'coding'});
  print('Setup: ${result['setup']}');
  print('Punchline: ${result['punchline']}');
}
```

---

## Best Practices

- **Exhaustive Schema Definitions**: Always define explicit `inputSchema` and `outputSchema` for tools and flows to ensure the LLM generates correctly structured arguments.
- **Environment Isolation**: Do not hardcode API keys. Use environment variables or secure credential storage.
- **Trace Observability**: Enable Genkit's trace observability in development to inspect agent decision trees and tool invocations.
- **Error Handling**: Wrap tool execution in standard `try-catch` blocks and return error details gracefully so the agent can self-correct.

Examiner la source

Prix et coûts d’utilisation

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Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
L’approbation de revue IA est absente
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: dhruvanbhalara/skills
Licence: MIT
Version: Unknown
Dernier push GitHub: 30 août 2026
Registre mis à jour: 11 sept. 2026
Chemin des instructions: skills/dart/dart-genkit/SKILL.md @ a74a6fbe04a0

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

53/100

Revue nécessaire

Confiance

57/100

Do not auto-install

Audit

68/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
L’approbation de revue IA est absente
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 4 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dart-genkit%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dart-genkit%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/dhruvanbhalara-dart-genkit/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/dhruvanbhalara-dart-genkit"
  }
}

Pour le créateur

Source de la fiche

Indexé par Registry

Revendiable

Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: dhruvanbhalara
Source: dhruvanbhalara/skills
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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Cette fiche Indexé par Registry est attribuée à dhruvanbhalara, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

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