langgraph checkpoint: new library for checkpoint interfaces (#1163)

---------

Co-authored-by: Nuno Campos <nuno@langchain.dev>

examples/create-react-agent-hitl.ipynb

@@ -1,247 +1,247 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
-    "\n",
-    "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
-    "\n",
-    "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_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(\"OPENAI_API_KEY\")\n",
-    "\n",
-    "# Recommended\n",
-    "_set_env(\"LANGCHAIN_API_KEY\")\n",
-    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we initialize the model we want to use.\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "\n",
-    "\n",
-    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-    "\n",
-    "from typing import Literal\n",
-    "\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
-    "    \"\"\"Use this to get weather information.\"\"\"\n",
-    "    if city == \"nyc\":\n",
-    "        return \"It might be cloudy in nyc\"\n",
-    "    elif city == \"sf\":\n",
-    "        return \"It's always sunny in sf\"\n",
-    "    else:\n",
-    "        raise AssertionError(\"Unknown city\")\n",
-    "\n",
-    "\n",
-    "tools = [get_weather]\n",
-    "\n",
-    "# We need a checkpointer to enable human-in-the-loop patterns\n",
-    "from langgraph.checkpoint import MemorySaver\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(\n",
-    "    model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_stream(stream):\n",
-    "    for s in stream:\n",
-    "        message = s[\"messages\"][-1]\n",
-    "        if isinstance(message, tuple):\n",
-    "            print(message)\n",
-    "        else:\n",
-    "            message.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+      "metadata": {},
+      "source": [
+        "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
+        "\n",
+        "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
+        "\n",
+        "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's the weather in SF?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n",
-      " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n",
-      "  Args:\n",
-      "    city: sf\n"
-     ]
-    }
-   ],
-   "source": [
-    "config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n",
-    "\n",
-    "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Next step:  ('tools',)\n"
-     ]
-    }
-   ],
-   "source": [
-    "snapshot = graph.get_state(config)\n",
-    "print(\"Next step: \", snapshot.next)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%capture --no-stderr\n",
+        "%pip install -U langgraph langchain-openai"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_weather\n",
-      "\n",
-      "It's always sunny in sf\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The weather in San Francisco is currently sunny.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "OPENAI_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(\"OPENAI_API_KEY\")\n",
+        "\n",
+        "# Recommended\n",
+        "_set_env(\"LANGCHAIN_API_KEY\")\n",
+        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+      "metadata": {},
+      "source": [
+        "## Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# First we initialize the model we want to use.\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+        "\n",
+        "\n",
+        "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+        "\n",
+        "from typing import Literal\n",
+        "\n",
+        "from langchain_core.tools import tool\n",
+        "\n",
+        "\n",
+        "@tool\n",
+        "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+        "    \"\"\"Use this to get weather information.\"\"\"\n",
+        "    if city == \"nyc\":\n",
+        "        return \"It might be cloudy in nyc\"\n",
+        "    elif city == \"sf\":\n",
+        "        return \"It's always sunny in sf\"\n",
+        "    else:\n",
+        "        raise AssertionError(\"Unknown city\")\n",
+        "\n",
+        "\n",
+        "tools = [get_weather]\n",
+        "\n",
+        "# We need a checkpointer to enable human-in-the-loop patterns\n",
+        "from langgraph.checkpoint.memory import MemorySaver\n",
+        "\n",
+        "memory = MemorySaver()\n",
+        "\n",
+        "# Define the graph\n",
+        "\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "graph = create_react_agent(\n",
+        "    model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "00407425-506d-4ffd-9c86-987921d8c844",
+      "metadata": {},
+      "source": [
+        "## Usage\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def print_stream(stream):\n",
+        "    for s in stream:\n",
+        "        message = s[\"messages\"][-1]\n",
+        "        if isinstance(message, tuple):\n",
+        "            print(message)\n",
+        "        else:\n",
+        "            message.pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "What's the weather in SF?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n",
+            " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n",
+            "  Args:\n",
+            "    city: sf\n"
+          ]
+        }
+      ],
+      "source": [
+        "config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
+        "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n",
+        "\n",
+        "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Next step:  ('tools',)\n"
+          ]
+        }
+      ],
+      "source": [
+        "snapshot = graph.get_state(config)\n",
+        "print(\"Next step: \", snapshot.next)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+            "Name: get_weather\n",
+            "\n",
+            "It's always sunny in sf\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "The weather in San Francisco is currently sunny.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.1"
     }
-   ],
-   "source": [
-    "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

examples/create-react-agent-memory.ipynb

@@ -1,255 +1,255 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to add memory to the prebuilt ReAct agent\n",
-    "\n",
-    "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
-    "\n",
-    "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_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(\"OPENAI_API_KEY\")\n",
-    "\n",
-    "# Recommended\n",
-    "_set_env(\"LANGCHAIN_API_KEY\")\n",
-    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we initialize the model we want to use.\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "\n",
-    "\n",
-    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-    "\n",
-    "from typing import Literal\n",
-    "\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
-    "    \"\"\"Use this to get weather information.\"\"\"\n",
-    "    if city == \"nyc\":\n",
-    "        return \"It might be cloudy in nyc\"\n",
-    "    elif city == \"sf\":\n",
-    "        return \"It's always sunny in sf\"\n",
-    "    else:\n",
-    "        raise AssertionError(\"Unknown city\")\n",
-    "\n",
-    "\n",
-    "tools = [get_weather]\n",
-    "\n",
-    "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
-    "# to retain the chat context between interactions\n",
-    "from langgraph.checkpoint import MemorySaver\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(model, tools=tools, checkpointer=memory)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "Let's interact with it multiple times to show that it can remember"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_stream(stream):\n",
-    "    for s in stream:\n",
-    "        message = s[\"messages\"][-1]\n",
-    "        if isinstance(message, tuple):\n",
-    "            print(message)\n",
-    "        else:\n",
-    "            message.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
+      "metadata": {},
+      "source": [
+        "# How to add memory to the prebuilt ReAct agent\n",
+        "\n",
+        "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
+        "\n",
+        "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's the weather in NYC?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
-      " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
-      "  Args:\n",
-      "    city: nyc\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_weather\n",
-      "\n",
-      "It might be cloudy in nyc\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The weather in NYC might be cloudy.\n"
-     ]
-    }
-   ],
-   "source": [
-    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-    "\n",
-    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
-   "metadata": {},
-   "source": [
-    "Notice that when we pass the same the same thread ID, the chat history is preserved"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "187479f9-32fa-4611-9487-cf816ba2e147",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's it known for?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "New York City (NYC) is known for many things, including:\n",
-      "\n",
-      "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
-      "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
-      "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
-      "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
-      "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
-      "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
-      "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
-      "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
-      "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
-      "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "a213e11a-5c62-4ddb-a707-490d91add383",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%capture --no-stderr\n",
+        "%pip install -U langgraph langchain-openai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "OPENAI_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(\"OPENAI_API_KEY\")\n",
+        "\n",
+        "# Recommended\n",
+        "_set_env(\"LANGCHAIN_API_KEY\")\n",
+        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
+      "metadata": {},
+      "source": [
+        "## Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# First we initialize the model we want to use.\n",
+        "from langchain_openai import ChatOpenAI\n",
+        "\n",
+        "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+        "\n",
+        "\n",
+        "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
+        "\n",
+        "from typing import Literal\n",
+        "\n",
+        "from langchain_core.tools import tool\n",
+        "\n",
+        "\n",
+        "@tool\n",
+        "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+        "    \"\"\"Use this to get weather information.\"\"\"\n",
+        "    if city == \"nyc\":\n",
+        "        return \"It might be cloudy in nyc\"\n",
+        "    elif city == \"sf\":\n",
+        "        return \"It's always sunny in sf\"\n",
+        "    else:\n",
+        "        raise AssertionError(\"Unknown city\")\n",
+        "\n",
+        "\n",
+        "tools = [get_weather]\n",
+        "\n",
+        "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
+        "# to retain the chat context between interactions\n",
+        "from langgraph.checkpoint.memory import MemorySaver\n",
+        "\n",
+        "memory = MemorySaver()\n",
+        "\n",
+        "# Define the graph\n",
+        "\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "graph = create_react_agent(model, tools=tools, checkpointer=memory)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "00407425-506d-4ffd-9c86-987921d8c844",
+      "metadata": {},
+      "source": [
+        "## Usage\n",
+        "\n",
+        "Let's interact with it multiple times to show that it can remember"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def print_stream(stream):\n",
+        "    for s in stream:\n",
+        "        message = s[\"messages\"][-1]\n",
+        "        if isinstance(message, tuple):\n",
+        "            print(message)\n",
+        "        else:\n",
+        "            message.pretty_print()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "What's the weather in NYC?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "Tool Calls:\n",
+            "  get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
+            " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
+            "  Args:\n",
+            "    city: nyc\n",
+            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+            "Name: get_weather\n",
+            "\n",
+            "It might be cloudy in nyc\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "The weather in NYC might be cloudy.\n"
+          ]
+        }
+      ],
+      "source": [
+        "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
+        "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
+        "\n",
+        "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
+      "metadata": {},
+      "source": [
+        "Notice that when we pass the same the same thread ID, the chat history is preserved"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "187479f9-32fa-4611-9487-cf816ba2e147",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================\u001b[1m Human Message \u001b[0m=================================\n",
+            "\n",
+            "What's it known for?\n",
+            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+            "\n",
+            "New York City (NYC) is known for many things, including:\n",
+            "\n",
+            "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
+            "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
+            "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
+            "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
+            "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
+            "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
+            "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
+            "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
+            "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
+            "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
+          ]
+        }
+      ],
+      "source": [
+        "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
+        "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.1"
     }
-   ],
-   "source": [
-    "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
-    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }