# A Meal Planner & Grocery Shopping Agent in Typescript with HazelJS

> Source: <https://dev.to/nisa_fatima_bcd75fa085b76/a-meal-planner-grocery-shopping-agent-in-typescript-with-hazeljs-43b1>
> Published: 2026-06-13 16:33:41+00:00

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

##
Agent Implementation

###
Dietary Intake Agent

The [DietaryIntakeAgent] extracts structured information from natural language:

###
Recipe Search Agent with RAG

The [RecipeSearchAgent] uses RAG to find recipes semantically:

###
Nutrition Coach with Supervisor

The [NutritionCoachAgent] orchestrates the workflow using delegation:

##
RAG Implementation

We use `MemoryVectorStore`

and `RAGPipeline`

for semantic recipe search:

##
Production Configuration

The `AgentModule`

is configured with production-ready features:

##
Running the Project

##
Testing the API

##
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

-
**Multi-agent architecture**: Each agent has a focused responsibility, making the system maintainable and testable
-
**RAG for knowledge retrieval**: Semantic search over recipe database provides accurate, context-aware results
-
**Supervisor routing**: The orchestrator agent delegates tasks to specialists, enabling complex workflows
-
**Production resilience**: Built-in retries, circuit breakers, and rate limiting ensure reliability
-
**Observability**: The inspector provides deep insights into agent behavior and performance
-
**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](https://hazeljs.ai/) 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](https://github.com/nisafatimaa/meal-planning-agent)

See you next time!