Building an AI Chef Assistant

In this guide, you’ll create a “Chef Assistant” agent that helps users cook meals with available ingredients.

You’ll learn how to create the agent and register it with Mastra. Next, you’ll interact with the agent through your terminal and get to know different response formats. Lastly, you’ll access the agent through Mastra’s local API endpoints.

Prerequisites

Node.js v20.0 or later installed
An API key from a supported Model Provider
An existing Mastra project (Follow the installation guide to set up a new project)

Creating the Agent

To create an agent in Mastra use the Agent class to define it and then register it with Mastra.

Define the Agent

Create a new file src/mastra/agents/chefAgent.ts and define your agent:

src/mastra/agents/chefAgent.ts


import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
 
export const chefAgent = new Agent({
  name: "chef-agent",
  instructions:
    "You are Michel, a practical and experienced home chef" +
    "You help people cook with whatever ingredients they have available.",
  model: openai("gpt-4o-mini"),
});

Register the Agent with Mastra

In your src/mastra/index.ts file, register the agent:

src/mastra/index.ts


import { Mastra } from "@mastra/core";
import { chefAgent } from "./agents/chefAgent";
 
export const mastra = new Mastra({
  agents: { chefAgent },
});

Interacting with the Agent

Depending on your requirements you can interact and get responses from the agent in different formats. In the following steps you’ll learn how to generate, stream, and get structured output.

Generating Text Responses

Create a new file src/index.ts and add a main() function to it. Inside, craft a query to ask the agent and log its response.

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
 
async function main() {
  const query =
    "In my kitchen I have: pasta, canned tomatoes, garlic, olive oil, and some dried herbs (basil and oregano). What can I make?";
  console.log(`Query: ${query}`);
 
  const response = await chefAgent.generate([{ role: "user", content: query }]);
  console.log("\n👨‍🍳 Chef Michel:", response.text);
}
 
main();

Afterwards, run the script:


npx bun src/index.ts

You should get an output similar to this:


Query: In my kitchen I have: pasta, canned tomatoes, garlic, olive oil, and some dried herbs (basil and oregano). What can I make?

👨‍🍳 Chef Michel: You can make a delicious pasta al pomodoro! Here's how...

Streaming Responses

In the previous example you might have waited a bit for the response without any sign of progress. To show the agent’s output as it creates it you should instead stream its response to the terminal.

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
 
async function main() {
  const query =
    "Now I'm over at my friend's house, and they have: chicken thighs, coconut milk, sweet potatoes, and some curry powder.";
  console.log(`Query: ${query}`);
 
  const stream = await chefAgent.stream([{ role: "user", content: query }]);
 
  console.log("\n Chef Michel: ");
 
  for await (const chunk of stream.textStream) {
    process.stdout.write(chunk);
  }
 
  console.log("\n\n✅ Recipe complete!");
}
 
main();

Afterwards, run the script again:


npx bun src/index.ts

You should get an output similar to the one below. This time though you can read it line by line instead of one large block.


Query: Now I'm over at my friend's house, and they have: chicken thighs, coconut milk, sweet potatoes, and some curry powder.

👨‍🍳 Chef Michel:
Great! You can make a comforting chicken curry...

✅ Recipe complete!

Generating a Recipe with Structured Data

Instead of showing the agent’s response to a human you might want to pass it along to another part of your code. For these instances your agent should return structured output.

Change your src/index.ts to the following:

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
import { z } from "zod";
 
async function main() {
  const query =
    "I want to make lasagna, can you generate a lasagna recipe for me?";
  console.log(`Query: ${query}`);
 
  // Define the Zod schema
  const schema = z.object({
    ingredients: z.array(
      z.object({
        name: z.string(),
        amount: z.string(),
      }),
    ),
    steps: z.array(z.string()),
  });
 
  const response = await chefAgent.generate(
    [{ role: "user", content: query }],
    { output: schema },
  );
  console.log("\n👨‍🍳 Chef Michel:", response.object);
}
 
main();

After running the script again you should get an output similar to this:


Query: I want to make lasagna, can you generate a lasagna recipe for me?

👨‍🍳 Chef Michel: {
  ingredients: [
    { name: "Lasagna noodles", amount: "12 sheets" },
    { name: "Ground beef", amount: "1 pound" },
    // ...
  ],
  steps: [
    "Preheat oven to 375°F (190°C).",
    "Cook the lasagna noodles according to package instructions.",
    // ...
  ]
}

Running the Agent Server

Learn how to interact with your agent through Mastra’s API.

Using `mastra dev`

You can run your agent as a service using the mastra dev command:


mastra dev

This will start a server exposing endpoints to interact with your registered agents. Within the playground you can test your agent through a UI.

Accessing the Chef Assistant API

By default, mastra dev runs on http://localhost:4111. Your Chef Assistant agent will be available at:


POST http://localhost:4111/api/agents/chefAgent/generate

Interacting with the Agent via `curl`

You can interact with the agent using curl from the command line:


curl -X POST http://localhost:4111/api/agents/chefAgent/generate \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "I have eggs, flour, and milk. What can I make?"
      }
    ]
  }'

Sample Response:


{
  "text": "You can make delicious pancakes! Here's a simple recipe..."
}

Building an AI Chef Assistant

In this guide, you’ll create a “Chef Assistant” agent that helps users cook meals with available ingredients.

Prerequisites

Node.js v20.0 or later installed
An API key from a supported Model Provider
An existing Mastra project (Follow the installation guide to set up a new project)

Creating the Agent

To create an agent in Mastra use the Agent class to define it and then register it with Mastra.

Define the Agent

Create a new file src/mastra/agents/chefAgent.ts and define your agent:

src/mastra/agents/chefAgent.ts


import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
 
export const chefAgent = new Agent({
  name: "chef-agent",
  instructions:
    "You are Michel, a practical and experienced home chef" +
    "You help people cook with whatever ingredients they have available.",
  model: openai("gpt-4o-mini"),
});

Register the Agent with Mastra

In your src/mastra/index.ts file, register the agent:

src/mastra/index.ts


import { Mastra } from "@mastra/core";
import { chefAgent } from "./agents/chefAgent";
 
export const mastra = new Mastra({
  agents: { chefAgent },
});

Interacting with the Agent

Depending on your requirements you can interact and get responses from the agent in different formats. In the following steps you’ll learn how to generate, stream, and get structured output.

Generating Text Responses

Create a new file src/index.ts and add a main() function to it. Inside, craft a query to ask the agent and log its response.

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
 
async function main() {
  const query =
    "In my kitchen I have: pasta, canned tomatoes, garlic, olive oil, and some dried herbs (basil and oregano). What can I make?";
  console.log(`Query: ${query}`);
 
  const response = await chefAgent.generate([{ role: "user", content: query }]);
  console.log("\n👨‍🍳 Chef Michel:", response.text);
}
 
main();

Afterwards, run the script:


npx bun src/index.ts

You should get an output similar to this:


Query: In my kitchen I have: pasta, canned tomatoes, garlic, olive oil, and some dried herbs (basil and oregano). What can I make?

👨‍🍳 Chef Michel: You can make a delicious pasta al pomodoro! Here's how...

Streaming Responses

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
 
async function main() {
  const query =
    "Now I'm over at my friend's house, and they have: chicken thighs, coconut milk, sweet potatoes, and some curry powder.";
  console.log(`Query: ${query}`);
 
  const stream = await chefAgent.stream([{ role: "user", content: query }]);
 
  console.log("\n Chef Michel: ");
 
  for await (const chunk of stream.textStream) {
    process.stdout.write(chunk);
  }
 
  console.log("\n\n✅ Recipe complete!");
}
 
main();

Afterwards, run the script again:


npx bun src/index.ts

You should get an output similar to the one below. This time though you can read it line by line instead of one large block.


Query: Now I'm over at my friend's house, and they have: chicken thighs, coconut milk, sweet potatoes, and some curry powder.

👨‍🍳 Chef Michel:
Great! You can make a comforting chicken curry...

✅ Recipe complete!

Generating a Recipe with Structured Data

Instead of showing the agent’s response to a human you might want to pass it along to another part of your code. For these instances your agent should return structured output.

Change your src/index.ts to the following:

src/index.ts


import { chefAgent } from "./mastra/agents/chefAgent";
import { z } from "zod";
 
async function main() {
  const query =
    "I want to make lasagna, can you generate a lasagna recipe for me?";
  console.log(`Query: ${query}`);
 
  // Define the Zod schema
  const schema = z.object({
    ingredients: z.array(
      z.object({
        name: z.string(),
        amount: z.string(),
      }),
    ),
    steps: z.array(z.string()),
  });
 
  const response = await chefAgent.generate(
    [{ role: "user", content: query }],
    { output: schema },
  );
  console.log("\n👨‍🍳 Chef Michel:", response.object);
}
 
main();

After running the script again you should get an output similar to this:


Query: I want to make lasagna, can you generate a lasagna recipe for me?

👨‍🍳 Chef Michel: {
  ingredients: [
    { name: "Lasagna noodles", amount: "12 sheets" },
    { name: "Ground beef", amount: "1 pound" },
    // ...
  ],
  steps: [
    "Preheat oven to 375°F (190°C).",
    "Cook the lasagna noodles according to package instructions.",
    // ...
  ]
}

Running the Agent Server

Learn how to interact with your agent through Mastra’s API.

Using `mastra dev`

You can run your agent as a service using the mastra dev command:


mastra dev

This will start a server exposing endpoints to interact with your registered agents. Within the playground you can test your agent through a UI.

Accessing the Chef Assistant API

By default, mastra dev runs on http://localhost:4111. Your Chef Assistant agent will be available at:


POST http://localhost:4111/api/agents/chefAgent/generate

Interacting with the Agent via `curl`

You can interact with the agent using curl from the command line:


curl -X POST http://localhost:4111/api/agents/chefAgent/generate \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "I have eggs, flour, and milk. What can I make?"
      }
    ]
  }'

Sample Response:


{
  "text": "You can make delicious pancakes! Here's a simple recipe..."
}

Building an AI Chef Assistant

Prerequisites

Creating the Agent

Define the Agent

Register the Agent with Mastra

Interacting with the Agent

Generating Text Responses

Streaming Responses

Generating a Recipe with Structured Data

Running the Agent Server

Using mastra dev

Accessing the Chef Assistant API

Interacting with the Agent via curl

Building an AI Chef Assistant

Prerequisites

Creating the Agent

Define the Agent

Register the Agent with Mastra

Interacting with the Agent

Generating Text Responses

Streaming Responses

Generating a Recipe with Structured Data

Running the Agent Server

Using mastra dev

Accessing the Chef Assistant API

Interacting with the Agent via curl

Using `mastra dev`

Interacting with the Agent via `curl`

Using `mastra dev`

Interacting with the Agent via `curl`