diff --git a/docs/docs/versions/migrating_memory/conversation_buffer_memory.ipynb b/docs/docs/versions/migrating_memory/conversation_buffer_memory.ipynb
index 56b8fb2820161..aa5d7e37c8bba 100644
--- a/docs/docs/versions/migrating_memory/conversation_buffer_memory.ipynb
+++ b/docs/docs/versions/migrating_memory/conversation_buffer_memory.ipynb
@@ -268,7 +268,7 @@
     "Please refer to the following [migration guide](/docs/versions/migrating_chains/conversation_chain/) for more information.\n",
     "\n",
     "\n",
-    "## Usasge with a pre-built agent\n",
+    "## Usage with a pre-built agent\n",
     "\n",
     "This example shows usage of an Agent Executor with a pre-built agent constructed using the [create_tool_calling_agent](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html) function.\n",
     "\n",
@@ -546,7 +546,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/docs/versions/migrating_memory/index.mdx b/docs/docs/versions/migrating_memory/index.mdx
index 800de108629cd..fbd7cd6d3e54b 100644
--- a/docs/docs/versions/migrating_memory/index.mdx
+++ b/docs/docs/versions/migrating_memory/index.mdx
@@ -85,12 +85,12 @@ Memory classes that fall into this category include:
 | `ConversationTokenBufferMemory`   | [Link to Migration Guide](conversation_buffer_window_memory)                                                                                                | Keeps only the most recent messages in the conversation under the constraint that the total number of tokens in the conversation does not exceed a certain limit.                                                   |
 | `ConversationSummaryMemory`       | [Link to Migration Guide](conversation_summary_memory)                                                                                                      | Continually summarizes the conversation history. The summary is updated after each conversation turn. The abstraction returns the summary of the conversation history.                                              |
 | `ConversationSummaryBufferMemory` | [Link to Migration Guide](conversation_summary_memory)                                                                                                      | Provides a running summary of the conversation together with the most recent messages in the conversation under the constraint that the total number of tokens in the conversation does not exceed a certain limit. |
-| `VectorStoreRetrieverMemory`      | See related [long-term memory agent tutorial](https://langchain-ai.github.io/langgraph/tutorials/memory/long_term_memory_agent/) | Stores the conversation history in a vector store and retrieves the most relevant parts of past conversation based on the input.                                                                                    |
+| `VectorStoreRetrieverMemory`      | See related [long-term memory agent tutorial](long_term_memory_agent) | Stores the conversation history in a vector store and retrieves the most relevant parts of past conversation based on the input.                                                                                    |
 
 
 ### 2. Extraction of structured information from the conversation history
 
-Please see [long-term memory agent tutorial](https://langchain-ai.github.io/langgraph/tutorials/memory/long_term_memory_agent/) implements an agent that can extract structured information from the conversation history.
+Please see [long-term memory agent tutorial](long_term_memory_agent) implements an agent that can extract structured information from the conversation history.
 
 Memory classes that fall into this category include:
 
@@ -114,7 +114,7 @@ abstractions are not as widely used as the conversation history management abstr
 
 For this reason, there are no migration guides for these abstractions. If you're struggling to migrate an application
 that relies on these abstractions, please:
-1) Please review this [Long-term memory agent tutorial](https://langchain-ai.github.io/langgraph/tutorials/memory/long_term_memory_agent/) which should provide a good starting point for how to extract structured information from the conversation history.
+1) Please review this [Long-term memory agent tutorial](long_term_memory_agent) which should provide a good starting point for how to extract structured information from the conversation history.
 2) If you're still struggling, please open an issue on the LangChain GitHub repository, explain your use case, and we'll try to provide more guidance on how to migrate these abstractions.
 
 The general strategy for extracting structured information from the conversation history is to use a chat model with tool calling capabilities to extract structured information from the conversation history.
diff --git a/docs/docs/versions/migrating_memory/long_term_memory_agent.ipynb b/docs/docs/versions/migrating_memory/long_term_memory_agent.ipynb
new file mode 100644
index 0000000000000..8ab6f7be5fc61
--- /dev/null
+++ b/docs/docs/versions/migrating_memory/long_term_memory_agent.ipynb
@@ -0,0 +1,1082 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "a2b70d8c-dd71-41d0-9c6d-d3ed922c29cc.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8cbdb316-6af5-480f-a9c7-09f116f86273",
+   "metadata": {},
+   "source": [
+    "# A Long-Term Memory Agent\n",
+    "\n",
+    "This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. The agent can store, retrieve, and use memories to enhance its interactions with users.\n",
+    "\n",
+    "Inspired by papers like [MemGPT](https://memgpt.ai/) and distilled from our own works on long-term memory, the graph extracts memories from chat interactions and persists them to a database. \"Memory\" in this tutorial will be represented in two ways: \n",
+    "* a piece of text information that is generated by the agent\n",
+    "* structured information about entities extracted by the agent in the shape of `(subject, predicate, object)` knowledge triples.\n",
+    "\n",
+    "This information can later be read or queried semantically to provide personalized context when your bot is responding to a particular user.\n",
+    "\n",
+    "The KEY idea is that by saving memories, the agent persists information about users that is SHARED across multiple conversations (threads), which is different from memory of a single conversation that is already enabled by LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/).\n",
+    "\n",
+    "![memory_graph.png](attachment:a2b70d8c-dd71-41d0-9c6d-d3ed922c29cc.png)\n",
+    "\n",
+    "You can also check out a full implementation of this agent in [this repo](https://github.com/langchain-ai/lang-memgpt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a45f864-b4bd-4355-bddd-83921db2528b",
+   "metadata": {},
+   "source": [
+    "## Install dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d6cfbeeb-3dbb-4020-8f50-2a69e78bb5c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U --quiet langgraph langchain-openai langchain-community tiktoken"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9c5ed37d-8670-4f5a-b830-1c1c3991d7cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OPENAI_API_KEY:  ········\n",
+      "TAVILY_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",
+    "_set_env(\"TAVILY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dab4e96a-8a90-4df9-8818-5a6edf5805d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from typing import List, Literal, Optional\n",
+    "\n",
+    "import tiktoken\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_core.embeddings import Embeddings\n",
+    "from langchain_core.messages import get_buffer_string\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnableConfig\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
+    "from langgraph.checkpoint.memory import MemorySaver\n",
+    "from langgraph.graph import END, START, MessagesState, StateGraph\n",
+    "from langgraph.prebuilt import ToolNode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e032423c-f7d8-4ee1-8313-bf49a6129d44",
+   "metadata": {},
+   "source": [
+    "## Define vectorstore for memories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4ccb43-bf32-4a1d-89ae-22826adbe860",
+   "metadata": {},
+   "source": [
+    "First, let's define the vectorstore where we will be storing our memories. Memories will be stored as embeddings and later looked up based on the conversation context. We will be using an in-memory vectorstore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "77b45742-a1ec-43b1-9df0-4df70c03c762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall_vector_store = InMemoryVectorStore(OpenAIEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6338ccb4-2810-4f7a-9592-27a23c263d6f",
+   "metadata": {},
+   "source": [
+    "### Define tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b084b78f-639f-4caf-869b-7fcb93dae813",
+   "metadata": {},
+   "source": [
+    "Next, let's define our memory tools. We will need a tool to store the memories and another tool to search them to find the most relevant memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a1d29985-7276-4a93-80b6-dc7217e57a0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "\n",
+    "\n",
+    "def get_user_id(config: RunnableConfig) -> str:\n",
+    "    user_id = config[\"configurable\"].get(\"user_id\")\n",
+    "    if user_id is None:\n",
+    "        raise ValueError(\"User ID needs to be provided to save a memory.\")\n",
+    "\n",
+    "    return user_id\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def save_recall_memory(memory: str, config: RunnableConfig) -> str:\n",
+    "    \"\"\"Save memory to vectorstore for later semantic retrieval.\"\"\"\n",
+    "    user_id = get_user_id(config)\n",
+    "    document = Document(\n",
+    "        page_content=memory, id=str(uuid.uuid4()), metadata={\"user_id\": user_id}\n",
+    "    )\n",
+    "    recall_vector_store.add_documents([document])\n",
+    "    return memory\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def search_recall_memories(query: str, config: RunnableConfig) -> List[str]:\n",
+    "    \"\"\"Search for relevant memories.\"\"\"\n",
+    "    user_id = get_user_id(config)\n",
+    "\n",
+    "    def _filter_function(doc: Document) -> bool:\n",
+    "        return doc.metadata.get(\"user_id\") == user_id\n",
+    "\n",
+    "    documents = recall_vector_store.similarity_search(\n",
+    "        query, k=3, filter=_filter_function\n",
+    "    )\n",
+    "    return [document.page_content for document in documents]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b19a1a9f-e5ab-4d4a-9571-d8ac29420e09",
+   "metadata": {},
+   "source": [
+    "Additionally, let's give our agent ability to search the web using [Tavily](https://tavily.com/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f41baaf4-b71a-47a9-8c38-fbc604d932ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "search = TavilySearchResults(max_results=1)\n",
+    "tools = [save_recall_memory, search_recall_memories, search]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "853242a2-6ae1-4427-9f31-6041cb72833d",
+   "metadata": {},
+   "source": [
+    "### Define state, nodes and edges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0038574b-738a-4ace-b620-60eca665e5a5",
+   "metadata": {},
+   "source": [
+    "Our graph state will contain just two channels -- `messages` for keeping track of the chat history and `recall_memories` -- contextual memories that will be pulled in before calling the agent and passed to the agent's system prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0767095b-7d17-4c18-afeb-ed6ec74d215f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class State(MessagesState):\n",
+    "    # add memories that will be retrieved based on the conversation context\n",
+    "    recall_memories: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "64144945-b7d9-4202-a567-bc1a48d7e5b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the prompt template for the agent\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant with advanced long-term memory\"\n",
+    "            \" capabilities. Powered by a stateless LLM, you must rely on\"\n",
+    "            \" external memory to store information between conversations.\"\n",
+    "            \" Utilize the available memory tools to store and retrieve\"\n",
+    "            \" important details that will help you better attend to the user's\"\n",
+    "            \" needs and understand their context.\\n\\n\"\n",
+    "            \"Memory Usage Guidelines:\\n\"\n",
+    "            \"1. Actively use memory tools (save_core_memory, save_recall_memory)\"\n",
+    "            \" to build a comprehensive understanding of the user.\\n\"\n",
+    "            \"2. Make informed suppositions and extrapolations based on stored\"\n",
+    "            \" memories.\\n\"\n",
+    "            \"3. Regularly reflect on past interactions to identify patterns and\"\n",
+    "            \" preferences.\\n\"\n",
+    "            \"4. Update your mental model of the user with each new piece of\"\n",
+    "            \" information.\\n\"\n",
+    "            \"5. Cross-reference new information with existing memories for\"\n",
+    "            \" consistency.\\n\"\n",
+    "            \"6. Prioritize storing emotional context and personal values\"\n",
+    "            \" alongside facts.\\n\"\n",
+    "            \"7. Use memory to anticipate needs and tailor responses to the\"\n",
+    "            \" user's style.\\n\"\n",
+    "            \"8. Recognize and acknowledge changes in the user's situation or\"\n",
+    "            \" perspectives over time.\\n\"\n",
+    "            \"9. Leverage memories to provide personalized examples and\"\n",
+    "            \" analogies.\\n\"\n",
+    "            \"10. Recall past challenges or successes to inform current\"\n",
+    "            \" problem-solving.\\n\\n\"\n",
+    "            \"## Recall Memories\\n\"\n",
+    "            \"Recall memories are contextually retrieved based on the current\"\n",
+    "            \" conversation:\\n{recall_memories}\\n\\n\"\n",
+    "            \"## Instructions\\n\"\n",
+    "            \"Engage with the user naturally, as a trusted colleague or friend.\"\n",
+    "            \" There's no need to explicitly mention your memory capabilities.\"\n",
+    "            \" Instead, seamlessly incorporate your understanding of the user\"\n",
+    "            \" into your responses. Be attentive to subtle cues and underlying\"\n",
+    "            \" emotions. Adapt your communication style to match the user's\"\n",
+    "            \" preferences and current emotional state. Use tools to persist\"\n",
+    "            \" information you want to retain in the next conversation. If you\"\n",
+    "            \" do call tools, all text preceding the tool call is an internal\"\n",
+    "            \" message. Respond AFTER calling the tool, once you have\"\n",
+    "            \" confirmation that the tool completed successfully.\\n\\n\",\n",
+    "        ),\n",
+    "        (\"placeholder\", \"{messages}\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "09b37846-11c7-4f79-af53-69584969ab16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ChatOpenAI(model_name=\"gpt-4o\")\n",
+    "model_with_tools = model.bind_tools(tools)\n",
+    "\n",
+    "tokenizer = tiktoken.encoding_for_model(\"gpt-4o\")\n",
+    "\n",
+    "\n",
+    "def agent(state: State) -> State:\n",
+    "    \"\"\"Process the current state and generate a response using the LLM.\n",
+    "\n",
+    "    Args:\n",
+    "        state (schemas.State): The current state of the conversation.\n",
+    "\n",
+    "    Returns:\n",
+    "        schemas.State: The updated state with the agent's response.\n",
+    "    \"\"\"\n",
+    "    bound = prompt | model_with_tools\n",
+    "    recall_str = (\n",
+    "        \"<recall_memory>\\n\" + \"\\n\".join(state[\"recall_memories\"]) + \"\\n</recall_memory>\"\n",
+    "    )\n",
+    "    prediction = bound.invoke(\n",
+    "        {\n",
+    "            \"messages\": state[\"messages\"],\n",
+    "            \"recall_memories\": recall_str,\n",
+    "        }\n",
+    "    )\n",
+    "    return {\n",
+    "        \"messages\": [prediction],\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def load_memories(state: State, config: RunnableConfig) -> State:\n",
+    "    \"\"\"Load memories for the current conversation.\n",
+    "\n",
+    "    Args:\n",
+    "        state (schemas.State): The current state of the conversation.\n",
+    "        config (RunnableConfig): The runtime configuration for the agent.\n",
+    "\n",
+    "    Returns:\n",
+    "        State: The updated state with loaded memories.\n",
+    "    \"\"\"\n",
+    "    convo_str = get_buffer_string(state[\"messages\"])\n",
+    "    convo_str = tokenizer.decode(tokenizer.encode(convo_str)[:2048])\n",
+    "    recall_memories = search_recall_memories.invoke(convo_str, config)\n",
+    "    return {\n",
+    "        \"recall_memories\": recall_memories,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def route_tools(state: State):\n",
+    "    \"\"\"Determine whether to use tools or end the conversation based on the last message.\n",
+    "\n",
+    "    Args:\n",
+    "        state (schemas.State): The current state of the conversation.\n",
+    "\n",
+    "    Returns:\n",
+    "        Literal[\"tools\", \"__end__\"]: The next step in the graph.\n",
+    "    \"\"\"\n",
+    "    msg = state[\"messages\"][-1]\n",
+    "    if msg.tool_calls:\n",
+    "        return \"tools\"\n",
+    "\n",
+    "    return END"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "854c9825-6ccf-450d-bc0f-88cf16ac5442",
+   "metadata": {},
+   "source": [
+    "## Build the graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f1aa06c-69b0-4f86-94bc-6be588c9a778",
+   "metadata": {},
+   "source": [
+    "Our agent graph is going to be very similar to simple [ReAct agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent). The only important modification is adding a node to load memories BEFORE calling the agent for the first time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6122f234-3be0-48a8-960b-011fa2a6ce6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the graph and add nodes\n",
+    "builder = StateGraph(State)\n",
+    "builder.add_node(load_memories)\n",
+    "builder.add_node(agent)\n",
+    "builder.add_node(\"tools\", ToolNode(tools))\n",
+    "\n",
+    "# Add edges to the graph\n",
+    "builder.add_edge(START, \"load_memories\")\n",
+    "builder.add_edge(\"load_memories\", \"agent\")\n",
+    "builder.add_conditional_edges(\"agent\", route_tools, [\"tools\", END])\n",
+    "builder.add_edge(\"tools\", \"agent\")\n",
+    "\n",
+    "# Compile the graph\n",
+    "memory = MemorySaver()\n",
+    "graph = builder.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d587a860-9859-4cf3-be01-4e7e17d64190",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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12O2Pk6tcQQQQQSCCCQsuhwh971lx/DY4vT9vJSslyU2aht5M2SxnIwje0whwHlO538/Xqsl2hNQ6wo28RxBvYDPYCcRydyYrG2aEnbRzRyxuMhtybtDoweUAbnbc7bg5Wm1hqW3oS8OLusdKR661pHh6ul6+Zrs7/TmSK2+WxGXiQnn5QIWns9+TcndpGwFUxGu+IHGS/oXBwWJnl2iKWftMq6hkwUt2xK90b5TLDBI57W8g8Qcrd5CTv0A9VP0VhX6lv6gdS3y96gzGWLHav8esxz3NZy83KNnSPO4APXy9Aqxlfc/aBzOC0/iLOB2qYCAVcZJBcsQ2K0WwbyNnZIJS0gDcFx3267qTTPYNSswuvHat4T6O1lqjI1pLkOe7sdg8tI19qvGa76zZZ2Mic6RjXAGRrWOPjdRzuBrWGs6h0/oiDVY1rqbIZLEa/wDB+OO9k3ywT0BkxUMUsXwZHFjie0cC/m22cAAF6ax3DHTGJs6bsU8W2vLpyvPVxZZLJtXjmDRK3bm2dzcjertz06EbldDuEmk3YObDnFb46XLd/Hw90y9bvdAsdrzc+/8AOgO5d+XzbbdEzZHnfUud1Fe4c8UOKLtZ5nF57TObv18dioLpZjoIqk4jjry1h4kplA8ZzgXHtBykdFeNAYjIcQeMnE2xltR6ir4/E5DHCjh6uUmrwV3Px8EkgLWOHMC53wD4u/Mdt3Eq/wCY4B6Cz+qn6iv6eisZSSeO1LvPM2CeZm3JJJAHiKR42GznMJ6DqrRh9I4nA5nOZWhU7C/m5o7F+btHu7aRkTYmHYkhuzGNGzQB038vVIpkeTamstY+BWjNH4zL5G5Lm9X5zGS5C/nJa9p8FWWcxV+7nMmfGXBjQHBpcQzlBbzbi3W+HnGivorM49mSsGq3K0rdXHVdTyWMnLTa1/ddVuQkgicwuPZuYXdR47S4Ahblv8FNFZPSc+mreCisYaa9LkzA+aUuZakkdK+ZknNzxu53uILXDbcgbDosX3g9DeCjtOd6Jhi3XRkTtkbXbmyG8glNjte15uUBu/P5BspmyO3ghqLGak4dUZ8XbzNqKCaerMNRSGTIQTRyvbJDM477uY7du+56NHU+U3xQ2kNHYbQWArYTAUI8bjK/MY4Iy53Vzi5znOcS5zi4klziSSepUytkbBicNfsXlvztc/elW5VHhr9i8t+drn70q3Lz5V11Xqs7REReVBERAREQEREFU4pfaJk/7L96xZCk9QYaLUOFu42Z7omWYzH2jPhMPmcPnB2P6FU329SUXGGfTM2Qe3p3TjrMAik/rBssjHN3/B67eTc+U9HAmKsPMvF4mZ1zEbbb/RltiyZRQnfbPehmV9ape3TvtnvQzK+tUvbrdmeaPdHMsm0UJ32z3oZlfWqXt077Z70MyvrVL26Znmj3RzLJtFCd9s96GZX1ql7dO+2e9DMr61S9umZ5o90cyybRQnfbPehmV9ape3UdNre/BqKpgpNKZVuUt1ZrsMHb1PGhifEyR3N22w2dPENidzzdAdjszPNHujmWWxFCd9s96GZX1ql7dO+2e9DMr61S9umZ5o90cyybRQnfbPehmV9ape3TvtnvQzK+tUvbpmeaPdHMsm0UJ32z3oZlfWqXt077Z70MyvrVL26Znmj3RzLJtFCd9s96GZX1ql7dcm5DUVjxItJW4JD0a+5crMiB+Nxjke4D8jSfmKZnmj3RzSzO4a/YvLfna5+9KtyitM4PwfxLazpu6J3ySTzzBvKHyPeXuIG52budgNzsABudlKrn49UV4tVVOy5O0REWhBERAREQEREBERAREQEREBERAVByo/8Abzpg7eTTWWG+3/msd59v/wBj8h26X5a+yzN+P2lncrtxpjLjm5eg3tY3pvv83k28x+LqGwUREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBa9yxb/AMQOlRuebwXzGw5R5O68Z5/N5un1LYS1/lQ/3/NLkGTk8GctuAPE37qxu25+Py7fpQbAREQEREBERAREQEREBERAREQEREBERAREQEREBEUZmdTYjTojOVylPHdpvyC1O2Mv28uwJ67fMsqaZqm1MXkSaKre+lo70pxHrsf1p76WjvSnEeux/Wt2j43cnhLLNnctK0fleMXD93HPTdzw302a8WnMrC+fvtX5GPdax5DC7tNgSGOIG3XlPxHfY/vpaO9KcR67H9a/PDiN7l/TGf8AdmVJ6mUxnvb5eXv1fsR2YxDAQd5qxIIAL3jxQPI2T+qU0fG7k8JM2dz9OEVW99LR3pTiPXY/rT30tHelOI9dj+tNHxu5PCTNnctKKre+lo70pxHrsf1qSw2rsHqGV0WLzFHIStbzujrWGSODd9t9gd9t+m6xqwcWmL1UzEekpaUuiItKCIiAiIgIiICIiAiIgIiICIiAiIgLX2lHDIR3srKBJdtXLMb5XDxhHHPIyOMfE1rWjoOm5cdt3FbBWvNC/YB/067/AKqVe/J+rrnxj98l7FgREWxBERAREQFXte8tXS2RyjAGXcXXkvVZ2jx4pI2FwIPTodi0jfZzXOadwSFYVXOJH3O9U/mq1+5ct2B1tMeMMqdsNig7gH419XFnwG/kXJcZiIiICIiAiIgIiICIiAiIgIiICIiAteaF+wD/AKdd/wBVKthrXmhfsA/6dd/1Uq9+T9XV6x+17FgWlNce6Nm4d8QqmCzeBx9fE2r8FGK23UFd19wmc1jJ+4ducxBzgCebmA3PLst1rzJqf3Omt7kGrMdjJNKPq5XUY1JHlr3b93zObYZPHVk2YQxjSwMEgc/ZgAEY33Fqv2Ismv8A3SuT0w3Vt7C6MGa07pi/Hisjl7GUFbktOEe4ZEI3ufGwzR8ztwep5Wu2WLxA913itH6nz+LpVMPfi0+/ssi6/qSrj7L5QwPfHVryeNMWhwG5LAXbtBJBWoeMmVpaT4x6wc92Lz1WxfqX5dERZTIVJsnPHHEWHuVtV8diUua08zZOzdysD2gtdvuyDhjr3R+qtT3tGnTFrC6nu995YNSsmFjHWnxsbLy9k0iVh5A7lLmbHcb+dYXqnYO6f3RGUzGTy8Gj9GeEdTH4WjnzbsZRtMSV7MckjGtaY3ntNozs34J67ub03iclxn1dqHifwwdo/GVb2l9SadsZfuW9kO5XSbmud3kQSFromyDZoOzzI7fl5ATeqHDXJVOI3ELPmamKOocRQx9SJjnc8b4G2Q8vHLsGntmbbEnodwOm9Jx3BTW2kMNwmt4C1gbOodIYSXCXa2RlmZUsMljhDnxyMjLwWvgBALBzAn4Ky/yG/wBVziR9zvVP5qtfuXKxjfYb+VVziR9zvVP5qtfuXL1YHW0esfllTthsRnwG/kXJcWfAb+RclxmIiIgIiICIiAiIgIiICIiAiIgIiIC15oX7AP8Ap13/AFUq2Gte6V5cc29iJnCO9WuWZHQuPjGOSeSSOQDztc13lG43Dm77tK9+T68OuPGP3zXsT6Ii2IIiICIiAq5xI+53qn81Wv3LlY1XOIEsU2lcljOdpt5OvJSrw82znvkYW9PmAJc4+RrWucdgCVuwOtpnxhlTthsRnwG/kXJfANgB8S+rjMRERAREQEREBERAREQEREBERARFHZLKPqWalSCtLZsWXOaHMb/JwAMc7nlO/Ru7Q0bbklw2G25Acszlm4emZu57F2XdrWVajOeWQue1g2G42AL27uJDWjcuIAJUNY0PT1JZZb1RTpZezXksMqRmNxgigkc3YGNxLXv5WN3eRuC54bytcQZHCYBuNcLlt8d3OS14q9vJCIRunDOYgBu55GBz3kM3O3MepJJMusqappm9M2kVb3q9GeieE/w+L+FPer0Z6J4T/D4v4VaUW7SMbvzxlbzvVb3q9GeieE/w+L+FfnbxF905pfBe7NpxVMPijw5xEveS9WipxmGwSeWayWgbOcx58U/FH/WK/TtaOyvBnh97+enKvgPpvueXTmUlkr96K/I97bWPDXub2exIDngE9RzO28pTSMbvzxkvO9sj3q9GeieE/wAPi/hT3q9GeieE/wAPi/hVpRNIxu/PGS871W96vRnonhP8Pi/hUji9HYHCNlGPwmPoiVhik7nqsZzsPladh1HzeRTCLGrGxa4tVVMx6l5VrvZc0lXHeaE3cTXrQVoMHGGMMQa/ZzopHEeSM/zbuh7NoBbud5fF5uhmu6+4bcVo1LD6lhsbt3QzN25mPHladiDsfKHNI6EE5yjr+Fju26luOaarZrSOlDoHlrZSWFhbK3yPbtsdj5C1pBBAWlEiihcLm55J6+Ly0TK+d7kFmZldsjqzxzFjjFI5oDtiAS34TQ9m/wAIEzSAiIgIiICIiAiIgIiICIiCIzuSsxcuOxx7PLW4ZTWsTVJJ61ctA/lJuUtGwLhsznY5/UNI2c5uTi8LUw/dTq8LWz25e6LVjlAksy8jWdpIQBzO5WMaPiaxrRsGgCO0bBYkx8uUu1rtC/lJBampXbXbGr4rWNjbt4rAGtaS1vTmLiS4kuM+gIiICIiAqBjGuzvGrMXmFxqYHFR4rm68pszvFiVu2227Y2VTvuf53zbdZrXOrZNM4+GDH1m5HUOQf3PjMe5/KJpdurnkblsTB4737HZoOwc4ta7I0XpWPR+BioCd120977Fy9IwMfbsSOLpZXAdAXOJ2A6NGzR0AQTqIiAiIgIiIMLM4ern8bNQuse+vLtv2cjo3tIIc1zXtIc1zXAODmkEEAgghYdbJ2aGQNLKvjc+1Yl7hlrQScjogGuDZXbFrJBu5oBd44buOu7WzKxMtjIczi7dCw6ZkFmJ0L315nwytDhtuyRhDmOG+4c0gg7EEEIMtFFafu3LEFiDIV+57VWZ8IJnZKZ4gf5OY8obyl7diWlo5XcwG4AcZVAREQEREBERAREQF1WqzLlaaCUExysLHAHY7EbHqu1Vfifd1XjdA5q3oipQyGqoIO1o1MmH9hO5pBcw8jmndzQ4N6gcxbudt0GXoWq+hovBVJMdJiH1qUMBoSz9u6DkYG8hk+/2225vP5fOp1eBPcYe6J4ocZeO1jAZBlDTmmMRXu38phKFNwbJYlneXFz53SSsd205PI17WgMADQAQffaAiIgKH1RqmnpOhHYtNlsT2JRWp0azQ+xcncCWxRNJALiGucSSGta1z3uaxjnD5qjVVXStKKSaOW5dsvMFLG1eU2Ls3KXCKIOLRvs1xJcWta1rnPc1rXOGDprTFqLJSZ7PSw2tQTRmBorlxr0IC7m7CDm6nfZpklIa6VzGkhrWxxxhx0jpi3Ut2M9npI7GpLzAyRsLy+vQiHUVa5IaSwHq6QtDpXeMQ1oZHHaURAREQEREBERAREQV3uUVOIInip0Wd3YwtsW+12tSGCUdkzk++jb3RKeb70uA++ViVduwh3ELDS9wU5C3F3m93vmAsxbzVD2bGeV0b9uZzvMYox98FYkBERAREQEREHXZsR1K8s8ruWKJpe53xADclUKCfPamrw5EZyzg4LDBLDTpQQOLGEbt53SxvJdt5dgAPJ123Nt1V9rGY+hzfsFV7TX2uYr6JF+wF0MniKaJrtEze2uL/AJZbIuxu8+d9NMx6tR/26d5876aZj1aj/t1Not/SeWPbTyS7Xen+C9PSusNQapxGbyNDPZ/szkrcUFMd0FgPKS3sOUHqSS0AuPU7nqrT3nzvppmPVqP+3U2idJ5Y9tPIuhO8+d9NMx6tR/26d5876aZj1aj/ALdTaJ0nlj208i6qR6VyeKz8mo6+anzGaFfuYNy0cJjdCHBxiYY2N7HmIG7mjqWsLg/ka1bBwmWhz2Ho5KuHtgtwMnY2QbOaHNBAcPMRvsR8aiV08LPuc6c+gxfsrTjxFWHn2iJiY2REbb7vRdsLSiIucxEREBEXTcuQY+rNatTR160LDJJLK4NaxoG5JJ6ABWIvqgdyLSepuMuWysr4sAxuKojcNuWYuexJ/WbG7pGPi5gT1G4adwqrLqnUszy5+p8nuT966No/uDAF38L6LlGJTnVTFPhO34XV2vSyLzN4Saj9J8r+tb/CnhJqP0nyv61v8K3fwWN34+eRq3vNHEviD7orBe68qaDoaxfPkpZZKeGyMmHokjHWXxSPcdq+xAEEZcdjsYj86/TBeTLWOlu6spannyNuXUNKu+pWyTywzRRPO7mNdy9Aev8AefjO814Saj9J8r+tb/Cn8Fjd+Pnkat70yi8zeEmo/SfK/rW/wrk3U+pGHdup8oD872O/yLCE/gsbvx88jVvel0WisBxe1BhpmjJ8uepb+MQxsVlg/qkbMf8AkIbv+F8e6MNmaWoMZBkMfO2zUnG7JGgjz7EEHq1wIILSAQQQQCFycqyHGyOY6SNU9sbBmoiLnoi9VfaxmPoc37BVe019rmK+iRfsBWHVX2sZj6HN+wVXtNfa5ivokX7AXRwepn1/S9iSUNpHWGK1zhu+uGsG1QNiesJTG5m74ZXxSDZwB6PY4b+fbdStivHagkgmY2WGRpY9jhuHNI2IP6F4m07jMFon3LOrp9Nx08FqLvzZoZy1jOWK/Xx7cy6OXm5fGaGV39D9607jbopM2R7dWPkbseMx9m5KHOirxOlcGDdxDQSdvn6LyBrxtLhlqLVtLgu+OvC7QN3IX62HsGaGGdkkYrWBsXATljp9j8JwaCd9t1IV8NpDSmuOHdfhjYisHO4PJuzbaFozm7VFPmis2RzHeTt+QB58Yl7m7+YTOHpvQ+rqev8ARuE1Lj4p4aOXpxXYI7LWtlayRoc0ODSQDseuxI+dTi8XzYXFap9z5wXzjshp/O1sBp577Gk8zku5ockGV4mylj2nxZ4S3YFzSGmQ78u+69W8N85Q1Pw90zl8VWmp4y9ja9irXsbmSKJ0bSxriSdyAQN9zv8AGVYm4sa6eFn3OdOfQYv2V3Lp4Wfc5059Bi/ZVxepn1j8SvYtKIi5yCIiAtNcbNQyXMxU09G4ipBE27baPJI8uPZNP/aWOft8fIfN13KvPHEmJ8XEzOdp/wBSOtIzf8Ds+X9pr/8ANd36Nh015VersiZj11R+13oBERfdtYi4Tl7YZDE0OkDSWtJ2BO3QLy3wz0vY1dQwWoJtV4HG6qlvh9qd9eZuVNhkpMlZ5dZ2IIDm8nZ8vKejR0K8uLjTh1U0003mfG27mr1Oo/UObg01gMnl7TJJK2Pqy25WQgF7mRsLiGgkDfYHbcheccpp+hX0FrrVccJbqHG6wsup5Dnd2lcDIMBaw7+K0hzt2joeY79Vka0o6f1OzjFb1ZNDJqHEsngxda3ZMZq1hUa6B8LeYfDe5xJHwj08nRearK6raqddrxr9fDwHozE5KLM4qlkIWvbDahZOxsgAcGuaHAHYnrsVlKD0J9o+nvzdX/dNU4ujTN6YmUFbuE2oZMFrGPHFx7gzHM0s+9ZYawua/wCbmYwtPxkM+LrUVmaeifPrHTMcX84clE4beXZoc53/AOLXLz5Xh04uBXTVstLKna9NIiL8xVF6q+1jMfQ5v2Cq9pr7XMV9Ei/YCsWqGl2mcs0DcmpMAB/2FV3TJB03iiCCDUi2IPl8QLo4PUz6/pexJKEr6H05Uy+QysGn8XDlMjGYrt6OlG2eyw7btkeG8zwdh0cSOgU2iqIXTWitO6Mgng0/gcZgoZ3c8seMpx12yO+NwY0bn5yuOntCaa0jZt2MFp7FYWxbPNYlx1KKB8x8u7yxoLv0qcRLCrXeFWisnVjrXNH4C3XjnfZZDPjIHsbM8gvkALdg5xA3d5TsN1Z442xRtYxoYxoDWtaNgAPIAFyRAXTws+5zpz6DF+yu5dPC0bcOdN/PRiII8hHKNipi9TPrH4lexaURFzkEREBaw4y6PmvNrahoxOmsVIzBahjbzPkgJ3DgPKSxxJ2Hme/ykALZ6L05NlFWTYsYtHYPJ92F+Rx0sdW6+m+aPaO3XDHuZuOjmhwc0/pBCq40RqAf/ELOH/7PH/7ZektU8F6OWsSW8PbOEtSEufEIhLWe4ncuMe4LSf6rgOpJBPVVCXgvqxjiI7WGlbv0c6SVh/u5Hf8AqvtqPqOSY8RVVXmzu1x+NRm7mn6+jM9DPG9+vs3Oxrg50T6lANeAfIdqwOx+YgqYbo/AszZzLcJjm5d3lyAqR90Hzfzm3N/mti+81rD+lwfrE3sk95rWH9Lg/WJvZLdGV5HH+yP7mZ/JmyoD9NYiWjapPxVJ9O1MbFiu6uwxzSlwcXvbts5xcA7c9dxusfL6L09qC221lMFjMlaawxNmuU45Xhh33aHOBOx3PT51sf3mtYf0uD9Ym9knvNaw/pcH6xN7JZTluRzqmuDNlqa9ozKy2XGhrHKYekAGw0KdSiYoGgABreeu523TzkrH8CNQ/wDzDzvqeP8A9stw+81rD+lwfrE3slyZwY1c47OsYVg/CE0zv8uzH/qtc5Xke3pfmTNlQcLQs4zHR17eTsZedpJNu0yJkj9zuARGxrenk6DzLaHBvSUl7KDUtmMtqQRuioBw27Vzhs+Yf1eXdrT5+Z58nKTJ6e4HV4J2z5/Id9gDuKUMXY1z8zwSXP8Aybhp67tK2exjYmNYxoYxo2a1o2AHxBcf6h9Uoqw5wMnm99s+H96yNTkiIvlB8c0PaWuAc0jYg+Qqlu0dm8V/IYXK0mY5vSKvkKr5Xwt/AbI2Ru7R5ACNwPOVdUW7DxasK+bzW9lJ7w6w+U8H6jN7ZO8OsPlPB+oze2V2RbtKxN0cILqT3h1h8p4P1Gb2yd4dYfKeD9Rm9srsiaVibo4QXUnvDrD5TwfqM3tk7w6w+U8H6jN7ZXZE0rE3RwgupbdK6ivgwZHMUYqj+kne6pJHM5vnDXukPJuNxuAT16bEbq31KsNGrDWrxtighY2OONo2DWgbAD8gC7UWnExq8TVVyL3ERFpQREQEREBERAREQEREBERAREQEREH/2Q==",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "display(Image(graph.get_graph().draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "898c7a41-571a-45cb-b1d7-990c361b26da",
+   "metadata": {},
+   "source": [
+    "## Run the agent!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "812f0d36-9966-47dd-8bd4-5341eb219525",
+   "metadata": {},
+   "source": [
+    "Let's run the agent for the first time and tell it some information about the user!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2f815342-e79a-479d-a43d-e4c0225f26b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pretty_print_stream_chunk(chunk):\n",
+    "    for node, updates in chunk.items():\n",
+    "        print(f\"Update from node: {node}\")\n",
+    "        if \"messages\" in updates:\n",
+    "            updates[\"messages\"][-1].pretty_print()\n",
+    "        else:\n",
+    "            print(updates)\n",
+    "\n",
+    "        print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ead8ea5e-76db-47ea-81e1-2582fcd033c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': []}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  save_recall_memory (call_OqfbWodmrywjMnB1v3p19QLt)\n",
+      " Call ID: call_OqfbWodmrywjMnB1v3p19QLt\n",
+      "  Args:\n",
+      "    memory: User's name is John.\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: save_recall_memory\n",
+      "\n",
+      "User's name is John.\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Nice to meet you, John! How can I assist you today?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NOTE: we're specifying `user_id` to save memories for a given user\n",
+    "config = {\"configurable\": {\"user_id\": \"1\", \"thread_id\": \"1\"}}\n",
+    "\n",
+    "for chunk in graph.stream({\"messages\": [(\"user\", \"my name is John\")]}, config=config):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb1132d8-4a1a-4fa1-8dc9-7da220f16710",
+   "metadata": {},
+   "source": [
+    "You can see that the agent saved the memory about user's name. Let's add some more information about the user!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bb972962-5cbb-4273-b9b8-80810b55ff46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': [\"User's name is John.\"]}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  save_recall_memory (call_xxEivMuWCURJrGxMZb02Eh31)\n",
+      " Call ID: call_xxEivMuWCURJrGxMZb02Eh31\n",
+      "  Args:\n",
+      "    memory: John loves pizza.\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: save_recall_memory\n",
+      "\n",
+      "John loves pizza.\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Pizza is amazing! Do you have a favorite type or topping?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in graph.stream({\"messages\": [(\"user\", \"i love pizza\")]}, config=config):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0868024d-bf69-40f6-8fc8-c04607443aa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': [\"User's name is John.\", 'John loves pizza.']}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  save_recall_memory (call_AFrtCVwIEr48Fim80zlhe6xg)\n",
+      " Call ID: call_AFrtCVwIEr48Fim80zlhe6xg\n",
+      "  Args:\n",
+      "    memory: John's favorite pizza topping is pepperoni.\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: save_recall_memory\n",
+      "\n",
+      "John's favorite pizza topping is pepperoni.\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Pepperoni is a classic choice! Do you have a favorite pizza place, or do you enjoy making it at home?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"yes -- pepperoni!\")]},\n",
+    "    config={\"configurable\": {\"user_id\": \"1\", \"thread_id\": \"1\"}},\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "196c2fc5-34e8-4f42-90b0-60d2dd747203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': [\"User's name is John.\", 'John loves pizza.', \"John's favorite pizza topping is pepperoni.\"]}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  save_recall_memory (call_Na86uY9eBzaJ0sS0GM4Z9tSf)\n",
+      " Call ID: call_Na86uY9eBzaJ0sS0GM4Z9tSf\n",
+      "  Args:\n",
+      "    memory: John just moved to New York.\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: save_recall_memory\n",
+      "\n",
+      "John just moved to New York.\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Welcome to New York! That's a fantastic place for a pizza lover. Have you had a chance to explore any of the famous pizzerias there yet?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"i also just moved to new york\")]},\n",
+    "    config={\"configurable\": {\"user_id\": \"1\", \"thread_id\": \"1\"}},\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0880c6c-5111-4fe5-9e25-1ffd6ef756c5",
+   "metadata": {},
+   "source": [
+    "Now we can use the saved information about our user on a different thread. Let's try it out:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d503c838-f280-49c1-871f-b02b36a9904e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': ['John loves pizza.', \"User's name is John.\", 'John just moved to New York.']}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Considering you just moved to New York and love pizza, I'd recommend checking out some of the iconic pizza places in the city. Some popular spots include:\n",
+      "\n",
+      "1. **Di Fara Pizza** in Brooklyn – Known for its classic New York-style pizza.\n",
+      "2. **Joe's Pizza** in Greenwich Village – A historic pizzeria with a great reputation.\n",
+      "3. **Lucali** in Carroll Gardens, Brooklyn – Often ranked among the best for its delicious thin-crust pies.\n",
+      "\n",
+      "Would you like more recommendations or information about any of these places?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = {\"configurable\": {\"user_id\": \"1\", \"thread_id\": \"2\"}}\n",
+    "\n",
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"where should i go for dinner?\")]}, config=config\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "247e2634-7120-4de3-b1d5-a16c2d1e611e",
+   "metadata": {},
+   "source": [
+    "Notice how the agent is loading the most relevant memories before answering, and in our case suggests the dinner recommendations based on both the food preferences as well as location.\n",
+    "\n",
+    "Finally, let's use the search tool together with the rest of the conversation context and memory to find location of a pizzeria:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d235dbb3-3d5b-4206-888b-fef628241b14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': ['John loves pizza.', 'John just moved to New York.', \"John's favorite pizza topping is pepperoni.\"]}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  tavily_search_results_json (call_aespiB28jpTFvaC4d0qpfY6t)\n",
+      " Call ID: call_aespiB28jpTFvaC4d0qpfY6t\n",
+      "  Args:\n",
+      "    query: Joe's Pizza Greenwich Village NYC address\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: tavily_search_results_json\n",
+      "\n",
+      "[{\"url\": \"https://www.joespizzanyc.com/locations-1-1\", \"content\": \"Joe's Pizza Greenwich Village (Original Location) 7 Carmine Street New York, NY 10014 (212) 366-1182 Joe's Pizza Times Square 1435 Broadway New York, NY 10018 (646) 559-4878. TIMES SQUARE MENU. ORDER JOE'S TIMES SQUARE Joe's Pizza Williamsburg 216 Bedford Avenue Brooklyn, NY 11249\"}]\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The address for Joe's Pizza in Greenwich Village is:\n",
+      "\n",
+      "**7 Carmine Street, New York, NY 10014**\n",
+      "\n",
+      "Enjoy your pizza!\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"what's the address for joe's in greenwich village?\")]},\n",
+    "    config=config,\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0449b949-e7ea-4273-8194-b64751d764c6",
+   "metadata": {},
+   "source": [
+    "If you were to pass a different user ID, the agent's response will not be personalized as we haven't saved any information about the other user:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "260b0ee3-107f-4bcc-8ef2-edeab4fe11b5",
+   "metadata": {},
+   "source": [
+    "## Adding structured memories\n",
+    "\n",
+    "So far we've represented memories as strings, e.g., `\"John loves pizza\"`. This is a natural representation when persisting memories to a vector store. If your use-case would benefit from other persistence backends-- such as a graph database-- we can update our application to generate memories with additional structure.\n",
+    "\n",
+    "Below, we update the `save_recall_memory` tool to accept a list of \"knowledge triples\", or 3-tuples with a `subject`, `predicate`, and `object`, suitable for storage in a knolwedge graph. Our model will then generate these representations as part of its tool calls.\n",
+    "\n",
+    "For simplicity, we use the same vector database as before, but the `save_recall_memory` and `search_recall_memories` tools could be further updated to interact with a graph database. For now, we only need to update the `save_recall_memory` tool:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1e1569ef-1c00-46be-9616-1f046c38e74f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall_vector_store = InMemoryVectorStore(OpenAIEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "60ca4cf7-a16a-4f5c-8ca5-4974bcc6bbc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class KnowledgeTriple(TypedDict):\n",
+    "    subject: str\n",
+    "    predicate: str\n",
+    "    object_: str\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def save_recall_memory(memories: List[KnowledgeTriple], config: RunnableConfig) -> str:\n",
+    "    \"\"\"Save memory to vectorstore for later semantic retrieval.\"\"\"\n",
+    "    user_id = get_user_id(config)\n",
+    "    for memory in memories:\n",
+    "        serialized = \" \".join(memory.values())\n",
+    "        document = Document(\n",
+    "            serialized,\n",
+    "            id=str(uuid.uuid4()),\n",
+    "            metadata={\n",
+    "                \"user_id\": user_id,\n",
+    "                **memory,\n",
+    "            },\n",
+    "        )\n",
+    "        recall_vector_store.add_documents([document])\n",
+    "    return memories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b171ba75-bbf1-4474-a1db-2333a05a5da7",
+   "metadata": {},
+   "source": [
+    "We can then compile the graph exactly as before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "93bedf95-36ca-42d9-b2b6-bfcb1f7a1cf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [save_recall_memory, search_recall_memories, search]\n",
+    "model_with_tools = model.bind_tools(tools)\n",
+    "\n",
+    "\n",
+    "# Create the graph and add nodes\n",
+    "builder = StateGraph(State)\n",
+    "builder.add_node(load_memories)\n",
+    "builder.add_node(agent)\n",
+    "builder.add_node(\"tools\", ToolNode(tools))\n",
+    "\n",
+    "# Add edges to the graph\n",
+    "builder.add_edge(START, \"load_memories\")\n",
+    "builder.add_edge(\"load_memories\", \"agent\")\n",
+    "builder.add_conditional_edges(\"agent\", route_tools, [\"tools\", END])\n",
+    "builder.add_edge(\"tools\", \"agent\")\n",
+    "\n",
+    "# Compile the graph\n",
+    "memory = MemorySaver()\n",
+    "graph = builder.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7d3491af-81b2-4994-8754-7f07b8b8fc7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': []}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Hello, Alice! How can I assist you today?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = {\"configurable\": {\"user_id\": \"3\", \"thread_id\": \"1\"}}\n",
+    "\n",
+    "for chunk in graph.stream({\"messages\": [(\"user\", \"Hi, I'm Alice.\")]}, config=config):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c3c337-c48e-430d-a336-204b6904015d",
+   "metadata": {},
+   "source": [
+    "Note that the application elects to extract knowledge-triples from the user's statements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "846a9971-ff7e-4c86-b5dc-153bc4aac692",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': []}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  save_recall_memory (call_EQSZlvZLZpPa0OGS5Kyzy2Yz)\n",
+      " Call ID: call_EQSZlvZLZpPa0OGS5Kyzy2Yz\n",
+      "  Args:\n",
+      "    memories: [{'subject': 'Alice', 'predicate': 'has a friend', 'object_': 'John'}, {'subject': 'John', 'predicate': 'likes', 'object_': 'Pizza'}]\n",
+      "\n",
+      "\n",
+      "Update from node: tools\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: save_recall_memory\n",
+      "\n",
+      "[{\"subject\": \"Alice\", \"predicate\": \"has a friend\", \"object_\": \"John\"}, {\"subject\": \"John\", \"predicate\": \"likes\", \"object_\": \"Pizza\"}]\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Got it! If you need any suggestions related to pizza or anything else, feel free to ask. What else is on your mind today?\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"My friend John likes Pizza.\")]}, config=config\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "209a49b7-c015-42f9-9066-83e308c63a56",
+   "metadata": {},
+   "source": [
+    "As before, the memories generated from one thread are accessed in another thread from the same user:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c0cfc5d9-c69d-4839-990b-5edf004dd8b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Update from node: load_memories\n",
+      "{'recall_memories': ['John likes Pizza', 'Alice has a friend John']}\n",
+      "\n",
+      "\n",
+      "Update from node: agent\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "Since John likes pizza, bringing some delicious pizza would be a great choice for the party. You might also consider asking if there are any specific toppings he prefers or if there are any dietary restrictions among the guests. This way, you can ensure everyone enjoys the food!\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = {\"configurable\": {\"user_id\": \"3\", \"thread_id\": \"2\"}}\n",
+    "\n",
+    "for chunk in graph.stream(\n",
+    "    {\"messages\": [(\"user\", \"What food should I bring to John's party?\")]}, config=config\n",
+    "):\n",
+    "    pretty_print_stream_chunk(chunk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5227e196-6418-4af4-bc05-fb9c339148cd",
+   "metadata": {},
+   "source": [
+    "Optionally, for illustrative purposes we can visualize the knowledge graph extracted by the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8ad4d11a-b0cf-4720-b901-3d501365c033",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U --quiet matplotlib networkx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0eae5dbd-962f-48ed-9c7c-172fae9cadda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import networkx as nx\n",
+    "\n",
+    "# Fetch records\n",
+    "records = recall_vector_store.similarity_search(\n",
+    "    \"Alice\", k=2, filter=lambda doc: doc.metadata[\"user_id\"] == \"3\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Plot graph\n",
+    "plt.figure(figsize=(6, 4), dpi=80)\n",
+    "G = nx.DiGraph()\n",
+    "\n",
+    "for record in records:\n",
+    "    G.add_edge(\n",
+    "        record.metadata[\"subject\"],\n",
+    "        record.metadata[\"object_\"],\n",
+    "        label=record.metadata[\"predicate\"],\n",
+    "    )\n",
+    "\n",
+    "pos = nx.spring_layout(G)\n",
+    "nx.draw(\n",
+    "    G,\n",
+    "    pos,\n",
+    "    with_labels=True,\n",
+    "    node_size=3000,\n",
+    "    node_color=\"lightblue\",\n",
+    "    font_size=10,\n",
+    "    font_weight=\"bold\",\n",
+    "    arrows=True,\n",
+    ")\n",
+    "edge_labels = nx.get_edge_attributes(G, \"label\")\n",
+    "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color=\"red\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}