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   "source": [
    "# Introduction\n",
    "\n",
    "\n",
    "AgentChat provides intuitive defaults, such as **Agents** with preset behaviors and **Teams** with predefined communication protocols, to simplify building multi-agent applications.\n",
    "\n",
    ":::{note}\n",
    "For installation instructions, please refer to the [installation guide](../installation.md).\n",
    ":::\n",
    "\n",
    "\n",
    "## Defining a Model Client \n",
    "\n",
    "In many cases, an agent will require access to a generative model. AgentChat utilizes the model clients provided by the [ `autogen-core`](../../core-user-guide/framework/model-clients.ipynb) package to access models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CreateResult(finish_reason='stop', content='The capital of France is Paris.', usage=RequestUsage(prompt_tokens=15, completion_tokens=7), cached=False, logprobs=None)\n"
     ]
    }
   ],
   "source": [
    "from autogen_core.components.models import OpenAIChatCompletionClient, UserMessage\n",
    "\n",
    "# Create an OpenAI model client.\n",
    "model_client = OpenAIChatCompletionClient(\n",
    "    model=\"gpt-4o-2024-08-06\",\n",
    "    # api_key=\"sk-...\", # Optional if you have an OPENAI_API_KEY env variable set.\n",
    ")\n",
    "model_client_result = await model_client.create(\n",
    "    messages=[\n",
    "        UserMessage(content=\"What is the capital of France?\", source=\"user\"),\n",
    "    ]\n",
    ")\n",
    "print(model_client_result)  # \"Paris\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Logs\n",
    "\n",
    "As agents interact with each other, they generate logs that can be useful in building and debugging multi-agent systems. Your application can consume these logs by attaching a log handler to the AgentChat events. AgentChat also provides a default log handler that writes logs to the console and file.\n",
    "\n",
    "Attache the log handler before running your application to view agent message logs. \n",
    "\n",
    "```{tip}\n",
    "You can implement your own log handler and attach it to the AgentChat events.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from autogen_agentchat import EVENT_LOGGER_NAME\n",
    "from autogen_agentchat.logging import ConsoleLogHandler\n",
    "\n",
    "logger = logging.getLogger(EVENT_LOGGER_NAME)\n",
    "logger.addHandler(ConsoleLogHandler())\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Whats Next ?\n",
    "\n",
    "Now that we have installed the `autogen-agentchat` package, let's move on to exploring how to build agents using the agent presets provided by the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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