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

# 流式用户确认

> 此示例演示了如何实现流式用户确认流程，从而实现实时交互和响应流。

## 代码

```python cookbook/agent_concepts/user_control_flows/confirmation_required_stream.py theme={null}
import json

import httpx
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools import tool
from agno.utils import pprint
from rich.console import Console
from rich.prompt import Prompt

console = Console()

@tool(requires_confirmation=True)
def get_top_hackernews_stories(num_stories: int) -> str:
    """Fetch top stories from Hacker News.

    Args:
        num_stories (int): Number of stories to retrieve

    Returns:
        str: JSON string containing story details
    """
    # Fetch top story IDs
    response = httpx.get("https://hacker-news.firebaseio.com/v0/topstories.json")
    story_ids = response.json()

    # Yield story details
    all_stories = []
    for story_id in story_ids[:num_stories]:
        story_response = httpx.get(
            f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json"
        )
        story = story_response.json()
        if "text" in story:
            story.pop("text", None)
        all_stories.append(story)
    return json.dumps(all_stories)

agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[get_top_hackernews_stories],
    markdown=True,
)

for run_response in agent.run("Fetch the top 2 hackernews stories", stream=True):
    if run_response.is_paused:
        for tool in run_response.tools_requiring_confirmation:
            # Ask for confirmation
            console.print(
                f"工具 {tool.tool_name}({tool.tool_args}) 需要确认。"
            )
            message = (
                Prompt.ask("是否继续？", choices=["y", "n"], default="y")
                .strip()
                .lower()
            )

            if message == "n":
                tool.confirmed = False
            else:
                tool.confirmed = True
        run_response = agent.continue_run(run_response=run_response, stream=True)
        pprint.pprint_run_response(run_response)
```

## 用法

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="设置你的 API 密钥">
    ```bash theme={null}
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="安装库">
    ```bash theme={null}
    pip install -U agno httpx rich openai
    ```
  </Step>

  <Step title="运行示例">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/agent_concepts/user_control_flows/confirmation_required_stream.py
      ```

      ```bash Windows theme={null}
      python cookbook/agent_concepts/user_control_flows/confirmation_required_stream.py
      ```
    </CodeGroup>
  </Step>
</Steps>

## 主要特性

* 使用 `agent.run(stream=True)` 进行流式响应
* 使用 `agent.continue_run(stream=True)` 实现流式续接
* 通过用户确认保持实时交互
* 演示如何处理带用户输入的流式响应

## 用途

* 实时用户交互
* 需要用户输入的流式应用程序
* 交互式聊天界面
* 渐进式响应生成