> ## 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.

# 异步流式代理

## 代码

```python cookbook/models/ibm/watsonx/async_basic_stream.py theme={null}
import asyncio

from agno.agent import Agent, RunResponse
from agno.models.ibm import WatsonX

agent = Agent(
    model=WatsonX(id="ibm/granite-20b-code-instruct"), debug_mode=True, markdown=True
)

# 获取响应到一个变量
# run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)
# for chunk in run_response:
#     print(chunk.content)

# 在终端打印响应
asyncio.run(agent.aprint_response("Share a 2 sentence horror story", stream=True))
```

## 用法

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

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

  <Step title="安装库">
    ```bash theme={null}
    pip install -U ibm-watsonx-ai agno
    ```
  </Step>

  <Step title="运行代理">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/models/ibm/watsonx/async_basic_stream.py
      ```

      ```bash Windows theme={null}
      python cookbook\models\ibm\watsonx\async_basic_stream.py
      ```
    </CodeGroup>
  </Step>
</Steps>

此示例结合了异步执行和流式传输。它创建了一个 `debug_mode=True` 的代理以获得额外的日志记录，并使用带有流式传输的异步 API 来获取和显示正在生成的响应。