Stream Text Multi-Step

You may want to have different steps in your stream where each step has different settings, e.g. models, tools, or system prompts.

With createDataStreamResponse and sendFinish / sendStart options when merging into the data stream, you can control when the finish and start events are sent to the client, allowing you to have different steps in a single assistant UI message.

Server

app/api/chat/route.ts

import { openai } from '@ai-sdk/openai';
import { createDataStreamResponse, streamText, tool } from 'ai';
import { z } from 'zod';

export async function POST(req: Request) {
  const { messages } = await req.json();

  return createDataStreamResponse({
    execute: async dataStream => {
      // step 1 example: forced tool call
      const result1 = streamText({
        model: openai('gpt-4o-mini', { structuredOutputs: true }),
        system: 'Extract the user goal from the conversation.',
        messages,
        toolChoice: 'required', // force the model to call a tool
        tools: {
          extractGoal: tool({
            parameters: z.object({ goal: z.string() }),
            execute: async ({ goal }) => goal, // no-op extract tool
          }),
        },
      });

      // forward the initial result to the client without the finish event:
      result1.mergeIntoDataStream(dataStream, {
        experimental_sendFinish: false, // omit the finish event
      });

      // note: you can use any programming construct here, e.g. if-else, loops, etc.
      // workflow programming is normal programming with this approach.

      // example: continue stream with forced tool call from previous step
      const result2 = streamText({
        // different system prompt, different model, no tools:
        model: openai('gpt-4o'),
        system:
          'You are a helpful assistant with a different system prompt. Repeat the extract user goal in your answer.',
        // continue the workflow stream with the messages from the previous step:
        messages: [...messages, ...(await result1.response).messages],
      });

      // forward the 2nd result to the client (incl. the finish event):
      result2.mergeIntoDataStream(dataStream, {
        experimental_sendStart: false, // omit the start event
      });
    },
  });
}

Client

app/page.tsx

'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div>
      {messages?.map(message => (
        <div key={message.id}>
          <strong>{`${message.role}: `}</strong>
          {message.parts.map((part, index) => {
            switch (part.type) {
              case 'text':
                return <span key={index}>{part.text}</span>;
              case 'tool-invocation': {
                return (
                  <pre key={index}>
                    {JSON.stringify(part.toolInvocation, null, 2)}
                  </pre>
                );
              }
            }
          })}
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
      </form>
    </div>
  );
}