A Meal Planner & Grocery Shopping Agent in Typescript with HazelJS

In this post, we’ll build a practical AI-powered meal planning agent using HazelJS. This agent helps users create personalized meal plans, discover recipes, and generate shopping lists—all while demonstrating HazelJS’s powerful agent orchestration, RAG…


This content originally appeared on DEV Community and was authored by Nisa Fatima

In this post, we'll build a practical AI-powered meal planning agent using HazelJS. This agent helps users create personalized meal plans, discover recipes, and generate shopping lists—all while demonstrating HazelJS's powerful agent orchestration, RAG capabilities, and production-ready patterns.

What We're Building

Our meal planning agent handles the complete meal planning workflow:

  • Dietary Intake: Extracts dietary restrictions, budget, cooking time, and preferences
  • Recipe Search: Finds recipes using RAG over a recipe database
  • Meal Planning: Creates day-by-day meal plans with balanced nutrition
  • Shopping Lists: Generates organized grocery lists with cost estimates
  • Nutrition Coaching: Orchestrates the entire workflow via supervisor routing

Why HazelJS?

HazelJS provides a production-ready framework for building AI-native applications. For this project, we leverage:

  • Multi-agent orchestration with @Agent, @Tool, and @Delegate decorators
  • RAG (Retrieval-Augmented Generation) for semantic recipe search
  • Supervisor routing to delegate tasks to specialized agents
  • Production resilience with retries, circuit breakers, and rate limiting
  • Built-in observability via the HazelJS Inspector
  • Guardrails for safer AI recommendations

Project Architecture

meal-planning-agent/
├── src/
│   ├── agents/              # Agent implementations
│   │   ├── dietary-intake.agent.ts
│   │   ├── recipe-search.agent.ts
│   │   ├── meal-plan.agent.ts
│   │   ├── shopping-list.agent.ts
│   │   └── nutrition-coach.agent.ts
│   ├── providers/           # LLM and embedding providers
│   │   ├── meal-planning-llm.provider.ts
│   │   └── local-embedding.provider.ts
│   ├── data/                # Recipe fixtures
│   │   └── recipe-fixtures.ts
│   ├── meal/                # Controllers and services
│   │   ├── meal.controller.ts
│   │   └── recipe-kb.service.ts
│   ├── app.module.ts
│   ├── health.controller.ts
│   └── index.ts
├── package.json
├── tsconfig.json
└── README.md

Agent Implementation

Dietary Intake Agent

The DietaryIntakeAgent extracts structured information from natural language:

@Agent({
  name: 'DietaryIntakeAgent',
  description: 'Extracts dietary restrictions, budget, cooking time, cuisine preferences, and nutritional goals.',
  systemPrompt: 'You are a dietary intake specialist. Extract the user profile before any meal plan is created.',
  maxSteps: 4,
  temperature: 0,
})
@Service()
export class DietaryIntakeAgent {
  @Tool({
    name: 'extractDietaryProfile',
    description: 'Extract structured dietary planning information from a user request.',
    parameters: [
      { name: 'message', type: 'string', description: 'The raw user request', required: true },
      { name: 'userId', type: 'string', description: 'User id when available', required: false },
    ],
  })
  async extractDietaryProfile(input: { message: string; userId?: string }) {
    const dietaryRestrictions = this.extractDietaryRestrictions(input.message);
    const budget = this.extractBudget(input.message) ?? 150;
    const cookingTime = this.extractCookingTime(input.message) ?? 30;

    return {
      userId: input.userId ?? 'user-demo',
      dietaryRestrictions,
      budget,
      cookingTime,
      // ... more fields
    };
  }
}

Recipe Search Agent with RAG

The RecipeSearchAgent uses RAG to find recipes semantically:

@Agent({
  name: 'RecipeSearchAgent',
  description: 'Retrieves recipes from the knowledge base based on dietary restrictions, cuisine, and nutritional goals.',
  systemPrompt: 'You are a recipe search specialist. Use retrieved context only and cite source ids.',
  enableRAG: true,
  ragTopK: 3,
  maxSteps: 4,
  temperature: 0,
})
@Service()
export class RecipeSearchAgent {
  constructor(private readonly knowledgeBase: RecipeKnowledgeBaseService) {}

  @Tool({
    name: 'searchRecipes',
    description: 'Search recipes based on dietary preferences, cuisine, and nutritional goals.',
    parameters: [
      { name: 'query', type: 'string', description: 'The recipe search query', required: true },
      { name: 'topK', type: 'number', description: 'Number of recipes to retrieve', required: false },
    ],
  })
  async searchRecipes(input: { query: string; topK?: number }) {
    return this.knowledgeBase.answer(input.query, input.topK ?? 3);
  }
}

Nutrition Coach with Supervisor

The NutritionCoachAgent orchestrates the workflow using delegation:

@Agent({
  name: 'NutritionCoachAgent',
  description: 'Coordinates dietary intake, recipe retrieval, meal planning, and shopping list creation.',
  systemPrompt: 'You are the nutrition coach orchestrator. Extract constraints, retrieve recipes, build meal plans, and create shopping lists.',
  maxSteps: 8,
  temperature: 0,
})
@Service()
export class NutritionCoachAgent {
  @Delegate({
    agent: 'DietaryIntakeAgent',
    description: 'Extract dietary restrictions, budget, cooking time, and preferences from a user request.',
    inputField: 'input',
  })
  async analyzeMealRequest(input: string): Promise<string> {
    return '';
  }

  @Delegate({
    agent: 'RecipeSearchAgent',
    description: 'Retrieve recipes based on dietary preferences and nutritional goals.',
    inputField: 'input',
  })
  async getRecipes(input: string): Promise<string> {
    return '';
  }
  // ... more delegates
}

RAG Implementation

We use MemoryVectorStore and RAGPipeline for semantic recipe search:

@Service()
export class RecipeKnowledgeBaseService {
  private readonly embeddings = new LocalMealEmbeddingProvider();
  private readonly vectorStore = new MemoryVectorStore(this.embeddings);
  private readonly rag = new RAGPipeline({
    vectorStore: this.vectorStore,
    embeddingProvider: this.embeddings,
    topK: 3,
  });

  async answer(query: string, topK = 3) {
    const sources = await this.rag.retrieve(query, { topK }, RetrievalStrategy.HYBRID);
    return {
      answer: sources.map((source) => source.content).join('\n\n'),
      sources: sources.map((source) => ({
        id: source.id,
        score: Number(source.score.toFixed(3)),
        cuisine: source.metadata?.cuisine,
        dietary: source.metadata?.dietary,
        // ... more metadata
      })),
    };
  }
}

Production Configuration

The AgentModule is configured with production-ready features:

@HazelModule({
  imports: [
    ConfigModule.forRoot({ envFilePath: ['.env', '.env.local'], isGlobal: true }),
    CacheModule.forRoot({ strategy: 'memory', isGlobal: true }),
    InspectorModule.forRoot({ inspectorBasePath: '/__hazel', developmentOnly: true }),
    GuardrailsModule.forRoot({
      redactPIIByDefault: true,
      blockInjectionByDefault: true,
      blockToxicityByDefault: true,
    }),
    AIModule,
    RAGModule,
    AgentModule.forRoot({
      runtime: {
        llmProvider: new MealPlanningLocalLLMProvider(),
        defaultMaxSteps: 8,
        defaultTimeout: 15000,
        enableObservability: true,
        enableMetrics: true,
        enableRetry: true,
        enableCircuitBreaker: true,
        rateLimitPerMinute: 120,
      },
    }),
  ],
  controllers: [HealthController, MealController],
  providers: [
    RecipeKnowledgeBaseService,
    DietaryIntakeAgent,
    RecipeSearchAgent,
    MealPlanAgent,
    ShoppingListAgent,
    NutritionCoachAgent,
  ],
})
export class AppModule {}

Running the Project

# Install dependencies
npm install --legacy-peer-deps

# Build the project
npm run build

# Run eval tests
npm run eval

# Start development server
npm run dev

Testing the API

# Health check
curl http://localhost:3000/health

# Dietary intake
curl -s -X POST http://localhost:3000/meal/intake \
  -H 'content-type: application/json' \
  -d '{"message":"I am vegetarian, have a $150 weekly budget, can cook 30 mins per meal.","userId":"user-1"}'

# Recipe search with RAG
curl -s -X POST http://localhost:3000/meal/recipes \
  -H 'content-type: application/json' \
  -d '{"message":"Find high-protein vegetarian recipes"}'

# Supervisor orchestration
curl -s -X POST http://localhost:3000/meal/supervisor \
  -H 'content-type: application/json' \
  -d '{"message":"I am vegetarian, have a $150 weekly budget, can cook 30 mins per meal. Plan my week with high-protein options.","userId":"user-1"}'

HazelJS Inspector

Access the built-in inspector at http://localhost:3000/__hazel for:

  • Real-time agent execution traces
  • Tool call logs
  • Performance metrics
  • Circuit breaker status
  • Rate limiter status

Key Takeaways

  1. Multi-agent architecture: Each agent has a focused responsibility, making the system maintainable and testable
  2. RAG for knowledge retrieval: Semantic search over recipe database provides accurate, context-aware results
  3. Supervisor routing: The orchestrator agent delegates tasks to specialists, enabling complex workflows
  4. Production resilience: Built-in retries, circuit breakers, and rate limiting ensure reliability
  5. Observability: The inspector provides deep insights into agent behavior and performance
  6. Guardrails: PII redaction and content safety protect users from inappropriate recommendations

What's Next?

For a production deployment, you would:

  • Replace the local LLM provider with OpenAI, Anthropic, or Google Generative AI
  • Add persistent storage with @hazeljs/memory for user preferences and meal history
  • Implement @hazeljs/flow for advanced workflow orchestration
  • Add @hazeljs/pubsub for async meal planning notifications
  • Connect to real recipe APIs for dynamic content

Conclusion

This meal planning agent demonstrates how HazelJS enables building sophisticated AI applications with production-ready patterns. The combination of multi-agent orchestration, RAG, and resilience features makes it easy to create reliable, observable, and scalable AI-native applications.

Complete project is available here: Meal Planning Agent
See you next time!


This content originally appeared on DEV Community and was authored by Nisa Fatima


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Nisa Fatima | Sciencx (2026-06-13T16:33:41+00:00) A Meal Planner & Grocery Shopping Agent in Typescript with HazelJS. Retrieved from https://www.scien.cx/2026/06/13/a-meal-planner-grocery-shopping-agent-in-typescript-with-hazeljs/

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