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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.

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Key streaming concepts

  1. Chatting: Stream partial assistant messages. Each chunk includes the content so you can render messages as they arrive.
  2. 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.
  3. Tool calling: Watch for streamed tool_calls in each chunk, execute the requested tool, and append tool outputs back into the conversation.

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Handling streamed chunks

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.
  • Python
  • JavaScript
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 }]
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