autogen/python/docs/src/user-guide/agentchat-user-guide/quickstart.ipynb

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   "source": [
    "# Quickstart"
   ]
  },
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   "source": [
    "Via AgentChat, you can build applications quickly using preset agents.\n",
    "To illustrate this, we will begin with creating a single agent that can\n",
    "use tools.\n",
    "\n",
    "First, we need to install the AgentChat and Extension packages."
   ]
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   "source": [
    "pip install -U \"autogen-agentchat\" \"autogen-ext[openai,azure]\""
   ]
  },
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   "metadata": {},
   "source": [
    "This example uses an OpenAI model, however, you can use other models as well.\n",
    "Simply update the `model_client` with the desired model or model client class.\n",
    "\n",
    "To use Azure OpenAI models and AAD authentication,\n",
    "you can follow the instructions [here](./tutorial/models.ipynb#azure-openai).\n",
    "To use other models, see [Models](./tutorial/models.ipynb)."
   ]
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      "---------- user ----------\n",
      "What is the weather in New York?\n",
      "---------- weather_agent ----------\n",
      "[FunctionCall(id='call_bE5CYAwB7OlOdNAyPjwOkej1', arguments='{\"city\":\"New York\"}', name='get_weather')]\n",
      "---------- weather_agent ----------\n",
      "[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_bE5CYAwB7OlOdNAyPjwOkej1', is_error=False)]\n",
      "---------- weather_agent ----------\n",
      "The current weather in New York is 73 degrees and sunny.\n"
     ]
    }
   ],
   "source": [
    "from autogen_agentchat.agents import AssistantAgent\n",
    "from autogen_agentchat.ui import Console\n",
    "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
    "\n",
    "# Define a model client. You can use other model client that implements\n",
    "# the `ChatCompletionClient` interface.\n",
    "model_client = OpenAIChatCompletionClient(\n",
    "    model=\"gpt-4o\",\n",
    "    # api_key=\"YOUR_API_KEY\",\n",
    ")\n",
    "\n",
    "\n",
    "# Define a simple function tool that the agent can use.\n",
    "# For this example, we use a fake weather tool for demonstration purposes.\n",
    "async def get_weather(city: str) -> str:\n",
    "    \"\"\"Get the weather for a given city.\"\"\"\n",
    "    return f\"The weather in {city} is 73 degrees and Sunny.\"\n",
    "\n",
    "\n",
    "# Define an AssistantAgent with the model, tool, system message, and reflection enabled.\n",
    "# The system message instructs the agent via natural language.\n",
    "agent = AssistantAgent(\n",
    "    name=\"weather_agent\",\n",
    "    model_client=model_client,\n",
    "    tools=[get_weather],\n",
    "    system_message=\"You are a helpful assistant.\",\n",
    "    reflect_on_tool_use=True,\n",
    "    model_client_stream=True,  # Enable streaming tokens from the model client.\n",
    ")\n",
    "\n",
    "\n",
    "# Run the agent and stream the messages to the console.\n",
    "async def main() -> None:\n",
    "    await Console(agent.run_stream(task=\"What is the weather in New York?\"))\n",
    "    # Close the connection to the model client.\n",
    "    await model_client.close()\n",
    "\n",
    "\n",
    "# NOTE: if running this inside a Python script you'll need to use asyncio.run(main()).\n",
    "await main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's Next?\n",
    "\n",
    "Now that you have a basic understanding of how to use a single agent, consider following the [tutorial](./tutorial/index.md) for a walkthrough on other features of AgentChat."
   ]
  }
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