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   "source": [
    "# How to define input/output schema for your graph\n",
    "\n",
    "By default, `StateGraph` takes in a single schema and all nodes are expected to communicate with that schema. However, it is also possible to define explicit input and output schemas for a graph. This is helpful if you want to draw a distinction between input and output keys.\n",
    "\n",
    "In this notebook we'll walk through an example of this. At a high level, in order to do this you simply have to pass in `input=..., output=...` when defining the graph. Let's see an example below!"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6ec0eb77-874e-443e-8c73-93125b515106",
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    {
     "data": {
      "text/plain": [
       "{'answer': 'bye'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langgraph.graph import StateGraph, START, END\n",
    "from typing import TypedDict\n",
    "\n",
    "class InputState(TypedDict):\n",
    "    question: str\n",
    "\n",
    "class OutputState(TypedDict):\n",
    "    answer: str\n",
    "\n",
    "def answer_node(state: InputState):\n",
    "    return {\"answer\": \"bye\"}\n",
    "\n",
    "graph = StateGraph(input=InputState, output=OutputState)\n",
    "graph.add_node(answer_node)\n",
    "graph.add_edge(START, \"answer_node\")\n",
    "graph.add_edge(\"answer_node\", END)\n",
    "graph = graph.compile()\n",
    "\n",
    "graph.invoke({\"question\": \"hi\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a68836f-98e1-4684-a8a6-c1473c73460c",
   "metadata": {},
   "source": [
    "Notice that the output of invoke only includes the output schema."
   ]
  },
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   "execution_count": null,
   "id": "b952a554-f2a4-4be3-81ab-2e08f0f441c2",
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