📄 ai-sdk/cookbook/rsc/call-tools-in-parallel

Some language models support calling tools in parallel. This is particularly useful when multiple tools are independent of each other and can be executed in parallel during the same generation step.

http://localhost:3000

User: How is it going?

Assistant: All good, how may I help you?

What is the weather in Paris and New York?

Send Message

Client

Let's modify our previous example to call getWeather tool for multiple cities in parallel.

app/page.tsx

'use client';
import { useState } from 'react';import { Message, continueConversation } from './actions';
// Allow streaming responses up to 30 secondsexport const maxDuration = 30;
export default function Home() {  const [conversation, setConversation] = useState<Message[]>([]);  const [input, setInput] = useState<string>('');
  return (    <div>      <div>        {conversation.map((message, index) => (          <div key={index}>            {message.role}: {message.content}          </div>        ))}      </div>
      <div>        <input          type="text"          value={input}          onChange={event => {            setInput(event.target.value);          }}        />        <button          onClick={async () => {            const { messages } = await continueConversation([              ...conversation,              { role: 'user', content: input },            ]);
            setConversation(messages);          }}        >          Send Message        </button>      </div>    </div>  );}

Server

Let's update the tools object to now use the getWeather function instead.

app/actions.ts

'use server';
import { generateText } from 'ai';import { openai } from '@ai-sdk/openai';import { z } from 'zod';
export interface Message {  role: 'user' | 'assistant';  content: string;}
function getWeather({ city, unit }) {  // This function would normally make an  // API request to get the weather.
  return { value: 25, description: 'Sunny' };}
export async function continueConversation(history: Message[]) {  'use server';
  const { text, toolResults } = await generateText({    model: openai('gpt-3.5-turbo'),    system: 'You are a friendly weather assistant!',    messages: history,    tools: {      getWeather: {        description: 'Get the weather for a location',        inputSchema: z.object({          city: z.string().describe('The city to get the weather for'),          unit: z            .enum(['C', 'F'])            .describe('The unit to display the temperature in'),        }),        execute: async ({ city, unit }) => {          const weather = getWeather({ city, unit });          return `It is currently ${weather.value}°${unit} and ${weather.description} in ${city}!`;        },      },    },  });
  return {    messages: [      ...history,      {        role: 'assistant' as const,        content:          text || toolResults.map(toolResult => toolResult.result).join('\n'),      },    ],  };}

On this page

Call Tools in Parallel

Client

Server

Deploy and Scale AI Apps with Vercel.

Vercel delivers the infrastructure and developer experience you need to ship reliable AI-powered applications at scale.

Trusted by industry leaders:

OpenAI
Photoroom

Talk to an expert