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

# Running Workflows

> Execute workflow runs with streaming and non-streaming responses

<Steps>
  <Step title="Create a Python file">
    ```python run_workflows.py theme={null}
    import asyncio

    from agno.client import AgentOSClient


    async def run_workflow_non_streaming():
        """Execute a non-streaming workflow run."""
        print("=" * 60)
        print("Non-Streaming Workflow Run")
        print("=" * 60)

        client = AgentOSClient(base_url="http://localhost:7777")

        # Get available workflows
        config = await client.aget_config()
        if not config.workflows:
            print("No workflows available")
            return

        workflow_id = config.workflows[0].id
        print(f"Running workflow: {workflow_id}")

        try:
            result = await client.run_workflow(
                workflow_id=workflow_id,
                message="What are the benefits of using Python for data science?",
            )

            print(f"\nRun ID: {result.run_id}")
            print(f"Content: {result.content}")
        except Exception as e:
            print(f"Error: {e}")


    async def run_workflow_streaming():
        """Execute a streaming workflow run."""
        print("\n" + "=" * 60)
        print("Streaming Workflow Run")
        print("=" * 60)

        client = AgentOSClient(base_url="http://localhost:7777")

        # Get available workflows
        config = await client.aget_config()
        if not config.workflows:
            print("No workflows available")
            return

        workflow_id = config.workflows[0].id
        print(f"Streaming from workflow: {workflow_id}")
        print("\nResponse: ", end="", flush=True)

        try:
            async for event in client.run_workflow_stream(
                workflow_id=workflow_id,
                message="Explain machine learning in simple terms.",
            ):
                if event.event == "RunContent" and hasattr(event, "content"):
                    print(event.content, end="", flush=True)
                elif event.event == "WorkflowAgentCompleted" and hasattr(event, "content") and event.content:
                    print(event.content, end="", flush=True)

            print("\n")
        except Exception as e:
            print(f"\nError: {type(e).__name__}: {e}")


    async def main():
        await run_workflow_non_streaming()
        await run_workflow_streaming()


    if __name__ == "__main__":
        asyncio.run(main())
    ```
  </Step>

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Start an AgentOS Server">
    Make sure you have an AgentOS server running with workflows configured. See [Creating Your First OS](/agent-os/run-your-os) for setup instructions.
  </Step>

  <Step title="Run the Client">
    <CodeGroup>
      ```bash Mac theme={null}
      python run_workflows.py
      ```

      ```bash Windows theme={null}
      python run_workflows.py
      ```
    </CodeGroup>
  </Step>
</Steps>