output-dev-llm-streaming

LLM Text Streaming

When to Use This Skill

• Adding token or chunk progress to an LLM-powered step
• Choosing between completed generation and direct stream access
• Using onChunk, or onEnd / onError on streamText() / Agent.stream()
• Making stream failures trigger Temporal activity retries
• Streaming Agent responses or persisting streamed conversations

Choose the API

Need	Use
Complete single-shot result	generateText()
Complete result plus onChunk progress	generateTextWithStreaming()
Direct access to textStream or stream	streamText()
Complete Agent result plus onChunk progress	Agent.generateWithStreaming()
Direct access to the Agent stream	Agent.stream()

In workflow steps, prefer generateTextWithStreaming() or Agent.generateWithStreaming() when onChunk progress is sufficient. They consume the stream internally, return complete results like generateText() or Agent.generate(), and reject on provider, transport, or abort errors. Rejection allows Temporal to record the failed activity attempt and apply the step retry policy.

streamText() and Agent.stream() remain supported for code that needs direct control over stream consumption.

generateTextWithStreaming()

import { generateTextWithStreaming } from '@outputai/llm';

const result = await generateTextWithStreaming( {
  prompt: 'draft@v1',
  variables: { topic },
  onChunk( { chunk } ) {
    if ( chunk.type === 'text-delta' ) {
      process.stdout.write( chunk.text );
    }
  }
} );

return result.result;

The result has the same complete response fields as generateText(), including result, text, output, usage, finishReason, and cost. Structured output passed with aiSdk.Output.* is available through result.output.

Agent.generateWithStreaming()

const result = await agent.generateWithStreaming( {
  onChunk( { chunk } ) {
    if ( chunk.type === 'text-delta' ) {
      process.stdout.write( chunk.text );
    }
  }
} );