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    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Quickstart"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Via AgentChat, you can build applications quickly using preset agents.\n",
                "To illustrate this, we will begin with creating a team of a single tool-use\n",
                "agent that you can chat with.\n",
                "\n",
                "The following code uses the OpenAI model. If you haven't already, you need to\n",
                "install the following package and extension:"
            ]
        },
        {
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            "execution_count": null,
            "metadata": {
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            "source": [
                "pip install \"autogen-agentchat==0.4.0.dev11\" \"autogen-ext[openai]==0.4.0.dev11\""
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "To use Azure OpenAI models and AAD authentication,\n",
                "you can follow the instructions [here](./tutorial/models.ipynb#azure-openai)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "---------- user ----------\n",
                        "What is the weather in NYC?\n",
                        "---------- weather_agent ----------\n",
                        "[FunctionCall(id='call_vN04UiNJgqSz6g3MHt7Renig', arguments='{\"city\":\"New York City\"}', name='get_weather')]\n",
                        "[Prompt tokens: 75, Completion tokens: 16]\n",
                        "---------- weather_agent ----------\n",
                        "[FunctionExecutionResult(content='The weather in New York City is 73 degrees and Sunny.', call_id='call_vN04UiNJgqSz6g3MHt7Renig')]\n",
                        "---------- weather_agent ----------\n",
                        "The weather in New York City is 73 degrees and Sunny.\n",
                        "---------- Summary ----------\n",
                        "Number of messages: 4\n",
                        "Finish reason: Maximum number of turns 1 reached.\n",
                        "Total prompt tokens: 75\n",
                        "Total completion tokens: 16\n",
                        "Duration: 1.15 seconds\n",
                        "---------- user ----------\n",
                        "What is the weather in Seattle?\n",
                        "---------- weather_agent ----------\n",
                        "[FunctionCall(id='call_BesYutZXJIMfu2TlDZgodIEj', arguments='{\"city\":\"Seattle\"}', name='get_weather')]\n",
                        "[Prompt tokens: 127, Completion tokens: 14]\n",
                        "---------- weather_agent ----------\n",
                        "[FunctionExecutionResult(content='The weather in Seattle is 73 degrees and Sunny.', call_id='call_BesYutZXJIMfu2TlDZgodIEj')]\n",
                        "---------- weather_agent ----------\n",
                        "The weather in Seattle is 73 degrees and Sunny.\n",
                        "---------- Summary ----------\n",
                        "Number of messages: 4\n",
                        "Finish reason: Maximum number of turns 1 reached.\n",
                        "Total prompt tokens: 127\n",
                        "Total completion tokens: 14\n",
                        "Duration: 2.38 seconds\n"
                    ]
                }
            ],
            "source": [
                "from autogen_agentchat.agents import AssistantAgent\n",
                "from autogen_agentchat.teams import RoundRobinGroupChat\n",
                "from autogen_agentchat.ui import Console\n",
                "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
                "\n",
                "\n",
                "# Define a tool\n",
                "async def get_weather(city: str) -> str:\n",
                "    return f\"The weather in {city} is 73 degrees and Sunny.\"\n",
                "\n",
                "\n",
                "async def main() -> None:\n",
                "    # Define an agent\n",
                "    weather_agent = AssistantAgent(\n",
                "        name=\"weather_agent\",\n",
                "        model_client=OpenAIChatCompletionClient(\n",
                "            model=\"gpt-4o-2024-08-06\",\n",
                "            # api_key=\"YOUR_API_KEY\",\n",
                "        ),\n",
                "        tools=[get_weather],\n",
                "    )\n",
                "\n",
                "    # Define a team with a single agent and maximum auto-gen turns of 1.\n",
                "    agent_team = RoundRobinGroupChat([weather_agent], max_turns=1)\n",
                "\n",
                "    while True:\n",
                "        # Get user input from the console.\n",
                "        user_input = input(\"Enter a message (type 'exit' to leave): \")\n",
                "        if user_input.strip().lower() == \"exit\":\n",
                "            break\n",
                "        # Run the team and stream messages to the console.\n",
                "        stream = agent_team.run_stream(task=user_input)\n",
                "        await Console(stream)\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": [
                "The code snippet above introduces two high level concepts in AgentChat: *Agent* and *Team*.  An Agent helps us define what actions are taken when a message is received. Specifically, we use the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset - an agent that can be given access to a model (e.g., LLM) and tools (functions) that it can then use to address tasks.  A Team helps us define the rules for how agents interact with each other.  In the  {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team, agents respond in a sequential round-robin fashion.\n",
                "In this case, we have a single agent, so the same agent is used for each round."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## What's Next?\n",
                "\n",
                "Now that you have a basic understanding of how to define an agent and a team, consider following the [tutorial](./tutorial/models) for a walkthrough on other features of AgentChat.\n",
                "\n"
            ]
        }
    ],
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