{
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"
    }
   },
   "cell_type": "markdown",
   "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
   "metadata": {},
   "source": [
    "# How to edit graph state\n",
    "\n",
    "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Manually updating the graph state a common HIL interaction pattern, allowing the human to edit actions (e.g., what tool is being called or how it is being called).\n",
    "\n",
    "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state and then resume from that spot to continue.  \n",
    "\n",
    "![image.png](attachment:49539520-097a-43d5-94b4-2b56193a579f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbd446a-808f-4394-be92-d45ab818953c",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First we need to install the packages required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install --quiet -U langgraph langchain_anthropic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
   "metadata": {},
   "source": [
    "Next, we need to set API keys for Anthropic (the LLM we will use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANTHROPIC_API_KEY:  ········\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "\n",
    "def _set_env(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
    "\n",
    "\n",
    "_set_env(\"ANTHROPIC_API_KEY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
   "metadata": {},
   "source": [
    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "_set_env(\"LANGCHAIN_API_KEY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035e567c-db5c-4085-ba4e-5b3814561c21",
   "metadata": {},
   "source": [
    "## Simple Usage\n",
    "\n",
    "Let's look at very basic usage of this.\n",
    "\n",
    "Below, we do three things:\n",
    "\n",
    "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` a specified step (node).\n",
    "\n",
    "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n",
    "\n",
    "3) We use `.update_state` to update the state of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85e452f8-f33a-4ead-bb4d-7386cdba8edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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dJy0ndbDgVJupJyfaKUpVZWKUpQClKUApSlAc78gT6slh8+uXtr1dEVzvyBPqyWHz65e2vV0RQClKUApSlAKUpQClKUApSlAKUpQHO/IE+rJYfPrl7a9XRFc78gT6slh8+uXtr1dEUApSlAKUpQClKUApSlAKUrGmXOHb8fKpbEbPEc84EfrNZSbyQMmub+VlyublyXbhYd7QfSWz3Zpe5cE3b5Lzb6D4TSkcwv8AmqQoHeGcqGPBJq/OlVl74geso99VLypdn2ntvexe+aZFzthuqUfLLU6uS2ObltglHHPAKBU2T2Bw1PVz4WZszkvkP8sifB6HbH7doBV1el3J7nLqi7bnMsuvreddLXMnIbQpRxvje3OzNfSKuAPwZ+x6Doy2XvaFqVxi33mapVst0aa4ltxphKhzzm6o5BWtISMgEBtXYqu6+lVl74geso99NXPhYszaUrAjX62THA3HuMR9ZOAlt9KifuBrPqDTjtRgUpSsAUpSgFKVE9p1wch6UcjsOFp+4PNQUrBIKUuLAcII4ghvfII7cdXXU4R05KO8lGLk1FdppL5qmXqd11i2SnIFnQotqlsHdelKBwebV/Mb4EBY8JXWkpSApeka0rZ2VKWLbGcdUSpTrrYccUT1kqVkk/aa2LLLcdlDTSEttNpCUoSMBIHAAVErptc0nZ9YtaWlXUi+uKZQYzUZ50NqdOGkuOIQUNlXYFqGaSrS2Qdl97d56OnSp0YpEh6P2vu2H6BPup0ftfdsP0CfdUD2dbcbZr/VuqbCiJMhybPcHIba3IUkNvNtttqU4pxTSUIVvLUAgqyQkKGQQa2OntuGh9VX5mzWu/NyZz5WmNlh1tqUUAlQZdUgNvYAJ8BSuAJ7Kr1k+JlqlB9pK+j9r7th+gT7qdH7X3bD9An3VCtNcoLQOr5tsi2y+qcXdFFEFx+DIjsyVjOUIdcbShS+B8AHe4dVWJTWT4mZTjLNGte0xZ5LZQ7aYLqDkFK4yCP1VkWtU7SbiXLQ+67DT++Wp90raWnt5oqOWleIA7njTx3hU+utoW0DZ/eYlyuEDTr2lJd7j2lmFHW+q4rbecDaHgs4QVZO8WwjqBG921cVTVaosm7rc9hW4wqpxkiwLLeI1/tjM6KVFp0firG6tCgcKSodigQQR4xWdVdaDlm36xuNtBAYnxhPQgdjqFJbdP3hTP3pJ7asWpVIqLutjz+/dsPPVaeqm4ilKVWUioXtWjk6diTMEpg3CPIXgZwgr5tR+wBwk+QGppXomw2LjDfiSWkvxn21NOtLGUrQoYIPkIJqynLQmpMnCWjJS3FcVQO0IXWxbXkXDRFo1MzqOdKgM3MiAXLJdIo3QtbjpyG1tNlQCgUqykDCgavSZGe0fIRb7k4TFJ3IdxX+I8nOEtuKPBLoGAQfx/xk/wA5KMqqpwdN57Ox7z0ica0U4soGFabwzd9sOilWq6xJerJMqXa723EWqAEu29ttJU+kbqFJW2RunBzjGc1qNj2kLTNc0da73pXaDDv1iS26v52nTXLTDksNlIW2pbxZWk8QgNg8FYwBmulaVWNUr3OPNm064bQ9iWyzRlp01e0yYtxhT5F6kQlNQYzDEkuqcQ+fBWohO6lKcnwjnFdh1qdK6UtWitOwrFZovyO1Qm+aYj84pzcTknG8slR4k9ZNRf6ANmn5A6b/AO1s/wC2hiEJQW9lXax14/ctuyHr5o/WUrS+khm1pt+nZUhqbcFApXJKkowUtJJQjryVKUDjFdHg5AP668GGG4zLbLKEtNNpCEIQMBIAwAB4qx5FwCZbUCMj5Xc3xlmG2fCI6t5X9VA7VHgPKSAZRi5u0USS0LykzP0fHVL2hrkJB3IFrW0s44ZfdQQM+PEY/wCI8dWVWj0jpsabtq0urS9PlOc/LeQDurcIAwnPEJSAEgeIeMmt5WxUabUVsSt587nnq9TWVHJClKVUUClKUB6ZcRifGcjyWW5Ed1JStp1IUhY7QQeBFRR7ZTYyomKu4W1JJPNxJriWxnxIJKR9gAFTGlWRqThlFkoylH8rsQj6KIHe969d+FPoogd73r134VN6VZr6m/wLNdU4mc18lOHM2u7FLVqbUN7ujl0kSpjTio8jm0brclxtGEgf1Uird+iiB3vevXfhVXcgT6slh8+uXtr1dEU19Tf4DXVOJkKTsotZ4PXC8vo7Um4uIz96Ck/pqRWLTdr01HWzbILMNCzvOFtPhOHxrUeKj5SSa2dKhKrOSs3kQlOUvzO4pSlVEBSlKAUpSgFKUoBSlKA535An1ZLD59cvbXq6IrnfkCfVksPn1y9teroigFKUoBSlKAUpSgFKUoBSlKAUpXEX4TvYWrV2gbftFtkcLuenR8mn7g8JyEtfgnxnm3FZx4nVk9VAWryBPqyWHz65e2vV0RXyE/B4bEXNqW3OJf5bS/mPSSm7m64OAVKCsxm8+PfSV/Y0R219e6AUpSgFKUoBSlKAUpWt1Fe2dN2OZcn0lxEdsqDaThTiupKBntUohI8pqUU5NRW1mUr5I9Go9VwNMNN/KVLelPZ5iHHTvvPYxndT4hkZUcJGRkjIqJva91JJUTGs9vht5OBLlqccx2EhCMD7Ao/bWshsvrcdmz1h+5ysKfdzkJ6yG0eJtGSEj7ScqUonKqbqQg7RV/a/l9eWw7VLBQSvUzZ7Omerv7NZP8z1YN7vOodSWafablbrDMt05hcaTHcLxS62tJSpJ8hBIrJS4lalJSoKUg4UAeIOM8fuIr9rGvfCu4v6JR3Fa8nzZnP5OehnNN2BFsmJfluTJE2WXOdeWrAGd0AAJQlKQBw4E9ZNWd0z1d/ZrJ/mer10pr3wruHRKO49yNbarb4qt9mkf3BIdaz/ANW4r9Vbyw7Q41yltQbjEes1wdOGm3yFtPHxNup4E/3VbqjgkJwM1BF60083qFNhVfbYm+qG8LYZjYkkYznms73Vx6q2kqK1NjrZeRvtr6xnHlBBHEEHiCOIIprYyynFfDb5Fc8HSkvw5MtOlRLQF+fnNTLVOcL063FGHlnKn2Fg824ry5StJ8ZQT21LaxOOg7HEnFwk4vsFKUqBAVCdrC1fM1pa/wCU7dYwcz1YSStP/uhH34qbVodb2J3UWmpUWMQJqSiRGJOBzrawtAJ7ASkJPkJ66uotKomyym1GabIfVI6q1lqexbbWod61BI0to992G1aFt2tp+HcXFfvzD8ggqZdUrwUcUjGCN48Kua3zkXGIh9AUjOUrbXwU2tJKVIUOxSVApI7CDUH1ZsZt+tdRIuN0v1+dt4kR5S7CmYkW9xxkpU2oo3N4eElKiErAJGSDWq04tp7T0s05JaJWWzZ25aGnbcdVydQT7nAs14nSXbSpiOlEhTcGO6FlaWgsKCQEABQThIJBOSfLZvrPa1ebrpS6yoF4n2i7qbcuTMyDbY8GLHdb3g5GcakKfO6SjAcCipJOd04FWixsgtcXWl31Axcbm0xeMquVkDyFW+Yss8yVrbKCreKAnOFAEpBINY2hdi0LZ9OiKt2pdSv2qElTcOyTLgHIUZBBASlO4FqSkHCQtSt3hjqoVKEk1u95Umktoev7dsu0BtCumrVX1m7T4kO42Z63R2my2/I5kLaU2hKw4klKuJKTg8BXT1UBsU5Oblm0boper7he5E+yYlN6dlT23LfFlJUoocSltPhFOcjK1AE8BVg/P+0v8itN/wDlD3/w1gzT0ox/FfxIFym7LFgR7BeHLHCi2Vq+Qp161JGbR8vh7jzaW1ITgFQUd1Cl7xKU9SVdl91WeoNhlv1pdlTb9fNQyre++1Mf02q5b9tLqN0pG7uBZQFJSd3eCSRndqzKFkU1Jt9p+6VWpvaVHQj8V60SS7jxoeY3M+kc/TVm1A9nNvVNuFxv6weYdQmHCOchbaSVLcHkUo48obSR11PK3KuWjHtS+vK9vgcDEyUqraFKUqg1RSlKAiOptEOTZblyszzcO4uAc8y8D8nlEDAK8cUrwAN8Z4YCgrdTuxV4XyCoolaauBUCfDiKbfbPlBCgr/FINWxSrtNNWnG/j9+826eJqUlZbCovnCf+Tl69U+NPnCf+Tl69U+NW7SmlS4OZd06puRRGkNoUPX1iZvWnrddLta3lrbblR4uUKUhRQsDJ7FJI+6t184T/AMnL16p8ainIE+rJYfPrl7a9XRFNKlwcx06puRUiZV0d4M6YvLiz1JLTbf6VrSP01trXoe63taV30NW+3darcw5zrr48TrmAEp8aE5z/AFsZBsWlNZGOcI2e/b9ORXPF1Zq2w8GmkMNIaaQlttCQlKEDASB1ADsFedKVSaQpSlAKUpQClKUApSlAc78gT6slh8+uXtr1dEVzvyBPqyWHz65e2vV0RQClKUApSlAKUpQClKUApSlAKUrm/lZcrm5cl24WHe0H0ls92aXuXBN2+S82+g+E0pHML/mqQoHeGcqGPBJoDI5An1ZLD59cvbXq6Ir5u8h/lkT4PQ7Y/btAKur0u5Pc5dUXbc5ll19bzrpa5k5DaFKON8b252Zr6RUApSlAKUpQClKUArDm3m321xLcudGirUN4JeeSgkePBNZlVZrGBFn7THxJjMyAm0R93nWwrH/7P9WaleMYynLYlfml8zXxFZYelKq1e3mT/pVZe+IHrKPfTpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHurS6bQ4XyOH13D1b7/oWJ0qsvfED1lHvqpeVLs+09t72L3zTIudsN1Sj5ZanVyWxzctsEo454BQKmyewOGtt0etfdsP0CPdTo9a+7YfoEe6nTaHC+Q67h6t9/wBDlj8Gfsfg6Mtl72halcYt95mqVbLdGmuJbcaYSoc85uqOQVrSEjIBAbV2KruvpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHup02hwvkOu4erff8AQsTpVZe+IHrKPfTpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHup02hwvkOu4erff9Cxm9S2h5xDbd1hLcWQlKUyEEknqAGa2VUpqGzW+LFhuswYzTqbjBwtDKUkf8U11ECrrrbhKFWmqsL7Ws/ZbzOvhMUsXTdRK2dvDzFKUoboqtNTfymSfzRG/wBaRVl1Wmpv5TJP5ojf60io1P0Kvu+aOb6R/aVPh4o86UpXmTwJp9V6vs+h7M5db5Pbt8FCko5xYKipajhKEJSCpaiepKQSewVGWdu+hHdOTL6dQNsW2FJZiS1yWHWXIzrqkpbDra0BbYUVDwlJAxk5wCai/KS0ncr5H0bd4cK7XWBYrx8ruECxSXGJq2VMuNFxlTakrK0FYO6kgkFQqEX/AERb7toW5XXTWmtZpucy+2VqQrUhmPy5LEeY05vpQ+tbiW0BbmSQnGFHq41fGEWk2b9KjSlGLk3dv2ZZ+WZd+m9rGlNVx7u9AuoQm0IDk9M5h2GuM2UlQcWl5KFBBSlRC8bpAODwqG2HlC2nXG1bTunNLymrjapttmzJT7sOQy4C2pkNFouBIU2rfc8IBQO6MEYOYdtz2eai1lq7aKxZrbIeE/RtvaZWUFDMt5qc+6uOHCN3fU34OM8A4M4Brc2XUMrX23DQ12i6R1JYrbbrJcmJC7vanIrbLi1Rt1rJGM+ArBHA48EnBxJQja/3sJKlTUXJZ5Pt2ZX+OezZsL4pSlaxzjUao/g+L+cYPtbVW/VQao/g+L+cYPtbVW/XocJ+1X+peET2fob9s/8AT8EKUpV53RVaam/lMk/miN/rSKsuozqHQEDUV2FydlTokoMJjlUN/mwpCVKUARg9q1f41nRU4Tpt2urc0/kauKovEUZUk7N+ZXGqdmmktcSmZOodNWq9yGUc227cIbbykJzndBUDgZOa0v7n/ZngDoDpzA44+bGcfs1aP0VQe+L3678KfRVB74vfrvwrQWBtsq8mefXonEJWVRcyHaU0BpnQolDTlgttiErdL4t8VDPO7ud3e3QM43lYz4zW/rZfRVB74vfrvwp9FUHvi9+u/CsPAJ7anJkH6GrSd3NczW1gXyxW7U1qkWy7QY9zt0gAPRZbQcbcAII3kngeIB+6pD9FUHvi9+u/Cn0VQe+L3678Kx1evWLuYXoWss1Ncyr0bAdmjZynQOnEnBGRbGRwIwR+L4q91v2G7O7TPjToWh9PxJkZ1LzD7NuaSttaSClSSE5BBAII8VWV9FUHvi9+u/Cn0VQe+L3678Kl0H+3kyzqrE+t8SLao/g+L+cYPtbVW/UJGye2F1hbtxu0hLLzb4bel5QVIWFpyMcRlIqbVu06ao0VSTvm33peR2cDhpYSk6cnfO/JeQpSlZOgKUpQClKUApSlAKUpQClKUApSlAKUpQH/2Q==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import TypedDict\n",
    "from langgraph.graph import StateGraph, START, END\n",
    "from langgraph.checkpoint.memory import MemorySaver\n",
    "from IPython.display import Image, display\n",
    "\n",
    "class State(TypedDict):\n",
    "    input: str\n",
    "\n",
    "def step_1(state):\n",
    "    print(\"---Step 1---\")\n",
    "    pass\n",
    "\n",
    "def step_2(state):\n",
    "    print(\"---Step 2---\")\n",
    "    pass\n",
    "\n",
    "def step_3(state):\n",
    "    print(\"---Step 3---\")\n",
    "    pass\n",
    "\n",
    "builder = StateGraph(State)\n",
    "builder.add_node(\"step_1\", step_1)\n",
    "builder.add_node(\"step_2\", step_2)\n",
    "builder.add_node(\"step_3\", step_3)\n",
    "builder.add_edge(START, \"step_1\")\n",
    "builder.add_edge(\"step_1\", \"step_2\")\n",
    "builder.add_edge(\"step_2\", \"step_3\")\n",
    "builder.add_edge(\"step_3\", END)\n",
    "\n",
    "# Set up memory\n",
    "memory = MemorySaver()\n",
    "\n",
    "# Add \n",
    "graph = builder.compile(checkpointer=memory, interrupt_before=[\"step_2\"])\n",
    "\n",
    "# View\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input': 'hello world'}\n",
      "---Step 1---\n"
     ]
    }
   ],
   "source": [
    "# Input\n",
    "initial_input = {\"input\": \"hello world\"}\n",
    "\n",
    "# Thread\n",
    "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
    "\n",
    "# Run the graph until the first interruption\n",
    "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab27716-e861-4ba3-9d7d-90694013e3c4",
   "metadata": {},
   "source": [
    "Now, we can just manually update our graph state - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49d61230-e5dc-4272-b8ab-09b0af30f088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current state!\n",
      "{'input': 'hello world'}\n",
      "---\n",
      "---\n",
      "Updated state!\n",
      "{'input': 'hello universe!'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Current state!\")\n",
    "print(graph.get_state(thread).values)\n",
    "\n",
    "graph.update_state(thread, {\"input\": \"hello universe!\"})\n",
    "\n",
    "print(\"---\\n---\\nUpdated state!\")\n",
    "print(graph.get_state(thread).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf77f6eb-4cc0-4615-a095-eb5ae7027b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Step 2---\n",
      "---Step 3---\n"
     ]
    }
   ],
   "source": [
    "# Continue the graph execution\n",
    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3333b771",
   "metadata": {},
   "source": [
    "## Agent\n",
    "\n",
    "In the context of agents, updating state is useful for things like editing tool calls.\n",
    " \n",
    "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n",
    "\n",
    "We will use Anthropic's models and a fake tool (just for demo purposes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6098e5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the tool\n",
    "from langchain_anthropic import ChatAnthropic\n",
    "from langchain_core.tools import tool\n",
    "from langgraph.graph import MessagesState, START, END, StateGraph\n",
    "from langgraph.prebuilt import ToolNode\n",
    "from langgraph.checkpoint.memory import MemorySaver\n",
    "\n",
    "\n",
    "@tool\n",
    "def search(query: str):\n",
    "    \"\"\"Call to surf the web.\"\"\"\n",
    "    # This is a placeholder for the actual implementation\n",
    "    # Don't let the LLM know this though 😊\n",
    "    return [\n",
    "        \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
    "    ]\n",
    "\n",
    "\n",
    "tools = [search]\n",
    "tool_node = ToolNode(tools)\n",
    "\n",
    "# Set up the model\n",
    "\n",
    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
    "model = model.bind_tools(tools)\n",
    "\n",
    "\n",
    "# Define nodes and conditional edges\n",
    "\n",
    "\n",
    "# Define the function that determines whether to continue or not\n",
    "def should_continue(state):\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    # If there is no function call, then we finish\n",
    "    if not last_message.tool_calls:\n",
    "        return \"end\"\n",
    "    # Otherwise if there is, we continue\n",
    "    else:\n",
    "        return \"continue\"\n",
    "\n",
    "\n",
    "# Define the function that calls the model\n",
    "def call_model(state):\n",
    "    messages = state[\"messages\"]\n",
    "    response = model.invoke(messages)\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": [response]}\n",
    "\n",
    "\n",
    "# Define a new graph\n",
    "workflow = StateGraph(MessagesState)\n",
    "\n",
    "# Define the two nodes we will cycle between\n",
    "workflow.add_node(\"agent\", call_model)\n",
    "workflow.add_node(\"action\", tool_node)\n",
    "\n",
    "# Set the entrypoint as `agent`\n",
    "# This means that this node is the first one called\n",
    "workflow.add_edge(START, \"agent\")\n",
    "\n",
    "# We now add a conditional edge\n",
    "workflow.add_conditional_edges(\n",
    "    # First, we define the start node. We use `agent`.\n",
    "    # This means these are the edges taken after the `agent` node is called.\n",
    "    \"agent\",\n",
    "    # Next, we pass in the function that will determine which node is called next.\n",
    "    should_continue,\n",
    "    # Finally we pass in a mapping.\n",
    "    # The keys are strings, and the values are other nodes.\n",
    "    # END is a special node marking that the graph should finish.\n",
    "    # What will happen is we will call `should_continue`, and then the output of that\n",
    "    # will be matched against the keys in this mapping.\n",
    "    # Based on which one it matches, that node will then be called.\n",
    "    {\n",
    "        # If `tools`, then we call the tool node.\n",
    "        \"continue\": \"action\",\n",
    "        # Otherwise we finish.\n",
    "        \"end\": END,\n",
    "    },\n",
    ")\n",
    "\n",
    "# We now add a normal edge from `tools` to `agent`.\n",
    "# This means that after `tools` is called, `agent` node is called next.\n",
    "workflow.add_edge(\"action\", \"agent\")\n",
    "\n",
    "# Set up memory\n",
    "memory = MemorySaver()\n",
    "\n",
    "# Finally, we compile it!\n",
    "# This compiles it into a LangChain Runnable,\n",
    "# meaning you can use it as you would any other runnable\n",
    "\n",
    "# We add in `interrupt_before=[\"action\"]`\n",
    "# This will add a breakpoint before the `action` node is called\n",
    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
   "metadata": {},
   "source": [
    "## Interacting with the Agent\n",
    "\n",
    "We can now interact with the agent and see that it stops before calling a tool.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "search for the weather in sf now\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "[{'text': \"Certainly! I can help you search for the current weather in San Francisco. To do this, I'll use the search function to look up the most up-to-date weather information. Let me do that for you right away.\", 'type': 'text'}, {'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}]\n",
      "Tool Calls:\n",
      "  search (toolu_01FSkinAVXR1C4D5kecrzAnj)\n",
      " Call ID: toolu_01FSkinAVXR1C4D5kecrzAnj\n",
      "  Args:\n",
      "    query: current weather in San Francisco\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.messages import HumanMessage\n",
    "\n",
    "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n",
    "inputs = [HumanMessage(content=\"search for the weather in sf now\")]\n",
    "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n",
    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e3f5b9-9700-42b1-863f-c404861f8620",
   "metadata": {},
   "source": [
    "**Edit**\n",
    "\n",
    "We can now update the state accordingly. Let's modify the tool call to have the query `\"current weather in SF\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1aa7b1b9-9322-4815-bc0d-eb083870ac15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'configurable': {'thread_id': '3',\n",
       "  'thread_ts': '1ef3e229-4126-628c-8002-2a809f9bb238'}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, lets get the current state\n",
    "current_state = app.get_state(thread)\n",
    "\n",
    "# Let's now get the last message in the state\n",
    "# This is the one with the tool calls that we want to update\n",
    "last_message = current_state.values[\"messages\"][-1]\n",
    "\n",
    "# Let's now update the args for that tool call\n",
    "last_message.tool_calls[0][\"args\"] = {\"query\": \"current weather in SF\"}\n",
    "\n",
    "# Let's now call `update_state` to pass in this message in the `messages` key\n",
    "# This will get treated as any other update to the state\n",
    "# It will get passed to the reducer function for the `messages` key\n",
    "# That reducer function will use the ID of the message to update it\n",
    "# It's important that it has the right ID! Otherwise it would get appended\n",
    "# as a new message\n",
    "app.update_state(thread, {\"messages\": last_message})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcc5457-1ba1-4cba-ac41-da5c67cc67e5",
   "metadata": {},
   "source": [
    "Let's now check the current state of the app to make sure it got updated accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3fcf2bd-f881-49fe-b20e-ad16e6819bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'search',\n",
       "  'args': {'query': 'current weather in SF'},\n",
       "  'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_state = app.get_state(thread).values[\"messages\"][-1].tool_calls\n",
    "current_state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81",
   "metadata": {},
   "source": [
    "**Resume**\n",
    "\n",
    "We can now call the agent again with no inputs to continue, ie. run the tool as requested. We can see from the logs that it passes in the update args to the tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: search\n",
      "\n",
      "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "Based on the search results, I can provide you with the current weather information for San Francisco:\n",
      "\n",
      "The weather in San Francisco is currently sunny. \n",
      "\n",
      "It's important to note that the search result also included a playful astrological reference, which isn't directly related to the weather. If you need more specific weather details like temperature, humidity, or forecast, please let me know, and I can perform another search to find that information for you.\n",
      "\n",
      "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n"
     ]
    }
   ],
   "source": [
    "for event in app.stream(None, thread, stream_mode=\"values\"):\n",
    "    event[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78780afe-409d-46cd-a734-e82538cdd8de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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