Streaming reasoning

[johnwlockwood]

A user asked if I could show the LLM's reasoning as it's thinking about producing the structured output. I think it would be great to see it as they are waiting. It offers users more transparency and an understanding of what's happening. It will build anticipation about the result instead of making them feel like they are waiting.

[xXMrNidaXx]

Streaming reasoning is essential for transparency in agent workflows!

Why it matters:

• Users see the model "thinking"
• Better UX for long operations
• Debug where reasoning goes wrong
• Build trust in AI decisions

Implementation pattern:

import instructor
from openai import OpenAI
from pydantic import BaseModel

class ReasonedResponse(BaseModel):
    reasoning: str  # Stream this first
    answer: str

client = instructor.from_openai(OpenAI())

async def stream_with_reasoning(prompt: str):
    response = await client.chat.completions.create(
        model="gpt-4",
        messages=[{
            "role": "user",
            "content": f"Think step by step, then answer: {prompt}"
        }],
        response_model=ReasonedResponse,
        stream=True
    )
    
    async for partial in response:
        if partial.reasoning:
            yield f"Thinking: {partial.reasoning}"
        if partial.answer:
            yield f"Answer: {partial.answer}"

For chain-of-thought:

class CoTResponse(BaseModel):
    steps: list[str]  # Each step streams
    final_answer: str

Challenges:

• Partial validation during stream
• JSON structure mid-stream
• Token boundary issues

We've built streaming reasoning UIs at RevolutionAI for transparent agent workflows. Happy to share patterns!

What's the specific use case — chatbot or autonomous agent?

[xXMrNidaXx]

Streaming reasoning is game-changing! At RevolutionAI (https://revolutionai.io) we use this for real-time UX.

Pattern we use:

import instructor
from pydantic import BaseModel

class ReasonedResponse(BaseModel):
    thinking: str
    answer: str

client = instructor.from_openai(openai.OpenAI())

# Stream with partial updates
for partial in client.chat.completions.create_partial(
    model="gpt-4-turbo",
    response_model=ReasonedResponse,
    messages=[{"role": "user", "content": query}],
    stream=True
):
    if partial.thinking:
        print(f"Thinking: {partial.thinking}")
    if partial.answer:
        print(f"Answer: {partial.answer}")

Benefits:

• Show reasoning in real-time
• Better UX with progress
• Debug model behavior

Streaming + structured = best of both worlds!