> ## Documentation Index
> Fetch the complete documentation index at: https://pythonaisdk.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Streaming responses

> Display tokens as they arrive using stream_text.

Need ultra-low latency output? Use **`stream_text`** - an <code>async</code> iterator that yields
text deltas in real time.

<Steps>
  <Step title="Prepare the script">
    <CodeGroup>
      ```python stream.py theme={null}
      import asyncio
      from ai_sdk import openai, stream_text

      model = openai("gpt-4.1-mini")

      async def main():
      result = stream_text(
      model=model,
      prompt="Write a short poem about the sea where each line rhymes.",
      on_chunk=lambda d: print(d, end="", flush=True),
      )

          # Alternatively await the full text:
          full = await result.text()
          print("\n---\nFull text:")
          print(full)

      asyncio.run(main())

      ```
    </CodeGroup>
  </Step>

  <Step title="Run it">
    ```bash theme={null}
    python stream.py
    ```

    You’ll see tokens appear *immediately* instead of buffering the full response.
  </Step>
</Steps>

<Tip>
  `stream_text` accepts the same arguments as `generate_text` plus optional callbacks:

  * `on_chunk(delta)` - each text delta
  * `on_error(exc)` - exceptions while streaming
  * `on_finish(full_text)` - once complete

  `stream_object` is similar, but yields objects instead of text.
</Tip>

<Tip>`stream_text` is similar, but yields objects instead of text.</Tip>