---
title: Streaming
---

Streaming allows you to render text as it is produced by the model. 

Streaming is enabled by default through the REST API, but disabled by default in the SDKs.

To enable streaming in the SDKs, set the `stream` parameter to `True`.

## Key streaming concepts
1. Chatting: Stream partial assistant messages. Each chunk includes the `content` so you can render messages as they arrive.
1. Thinking: Thinking-capable models emit a `thinking` field alongside regular content in each chunk. Detect this field in streaming chunks to show or hide reasoning traces before the final answer arrives.
1. Tool calling: Watch for streamed `tool_calls` in each chunk, execute the requested tool, and append tool outputs back into the conversation.

## Handling streamed chunks


<Note> It is necessary to accumulate the partial fields in order to maintain the history of the conversation. This is particularly important for tool calling where the thinking, tool call from the model, and the executed tool result must be passed back to the model in the next request. </Note>

<Tabs>
  <Tab title="Python">

    ```python
    from ollama import chat

    stream = chat(
      model='qwen3',
      messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
      stream=True,
    )

    in_thinking = False
    content = ''
    thinking = ''
    for chunk in stream:
      if chunk.message.thinking:
        if not in_thinking:
          in_thinking = True
          print('Thinking:\n', end='', flush=True)
        print(chunk.message.thinking, end='', flush=True)
        # accumulate the partial thinking 
        thinking += chunk.message.thinking
      elif chunk.message.content:
        if in_thinking:
          in_thinking = False
          print('\n\nAnswer:\n', end='', flush=True)
        print(chunk.message.content, end='', flush=True)
        # accumulate the partial content
        content += chunk.message.content

      # append the accumulated fields to the messages for the next request
      new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
    ```
  </Tab>
  <Tab title="JavaScript">

    ```javascript
    import ollama from 'ollama'

    async function main() {
      const stream = await ollama.chat({
        model: 'qwen3',
        messages: [{ role: 'user', content: 'What is 17 × 23?' }],
        stream: true,
      })

      let inThinking = false
      let content = ''
      let thinking = ''

      for await (const chunk of stream) {
        if (chunk.message.thinking) {
          if (!inThinking) {
            inThinking = true
            process.stdout.write('Thinking:\n')
          }
          process.stdout.write(chunk.message.thinking)
          // accumulate the partial thinking
          thinking += chunk.message.thinking
        } else if (chunk.message.content) {
          if (inThinking) {
            inThinking = false
            process.stdout.write('\n\nAnswer:\n')
          }
          process.stdout.write(chunk.message.content)
          // accumulate the partial content
          content += chunk.message.content
        }
      }

      // append the accumulated fields to the messages for the next request
      new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
    }

    main().catch(console.error)
    ```
  </Tab>
</Tabs>