Building an AI Stock Agent

In this guide, you're going to create a simple agent that fetches the last day's closing stock price for a given symbol. You'll learn how to create a tool, add it to an agent, and use the agent to fetch stock prices.

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.

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

   src/mastra/agents/stockAgent.ts

   import { openai } from "@ai-sdk/openai";
   import { Agent } from "@mastra/core/agent";
   
   export const stockAgent = new Agent({
     name: "Stock Agent",
     instructions:
       "You are a helpful assistant that provides current stock prices. When asked about a stock, use the stock price tool to fetch the stock price.",
     model: openai("gpt-4o-mini"),
   });

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

   src/mastra/index.ts

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

Creating the Stock Price Tool

So far the Stock Agent doesn't know anything about the current stock prices. To change this, create a tool and add it to the agent.

1. Create a new file src/mastra/tools/stockPrices.ts. Inside, add a stockPrices tool that will fetch the last day's closing stock price for a given symbol:

   src/mastra/tools/stockPrices.ts

   import { createTool } from "@mastra/core/tools";
   import { z } from "zod";
   
   const getStockPrice = async (symbol: string) => {
     const data = await fetch(
       `https://mastra-stock-data.vercel.app/api/stock-data?symbol=${symbol}`,
     ).then((r) => r.json());
     return data.prices["4. close"];
   };
   
   export const stockPrices = createTool({
     id: "Get Stock Price",
     inputSchema: z.object({
       symbol: z.string(),
     }),
     description: `Fetches the last day's closing stock price for a given symbol`,
     execute: async ({ context: { symbol } }) => {
       console.log("Using tool to fetch stock price for", symbol);
       return {
         symbol,
         currentPrice: await getStockPrice(symbol),
       };
     },
   });

2. Inside src/mastra/agents/stockAgent.ts import your newly created stockPrices tool and add it to the agent.

   src/mastra/agents/stockAgent.ts

   import { openai } from "@ai-sdk/openai";
   import { Agent } from "@mastra/core/agent";
   import { stockPrices } from "../tools/stockPrices";
   
   export const stockAgent = new Agent({
     name: "Stock Agent",
     instructions:
       "You are a helpful assistant that provides current stock prices. When asked about a stock, use the stock price tool to fetch the stock price.",
     model: openai("gpt-4o-mini"),
     tools: {
       stockPrices,
     },
   });

Running the Agent Server

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

1. 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 Studio you can test your stockAgent and stockPrices tool through a UI.

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

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

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

   curl -X POST http://localhost:4111/api/agents/stockAgent/generate \
     -H "Content-Type: application/json" \
     -d '{
       "messages": [
         { "role": "user", "content": "What is the current stock price of Apple (AAPL)?" }
       ]
     }'

   Expected Response:

   You should receive a JSON response similar to:

   {
     "text": "The current price of Apple (AAPL) is $174.55.",
     "agent": "Stock Agent"
   }

   This indicates that your agent successfully processed the request, used the stockPrices tool to fetch the stock price, and returned the result.