diff --git a/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb b/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
index ca665342f..364fb7250 100644
--- a/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
+++ b/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
@@ -19,7 +19,7 @@
     "\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). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \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 stop graph execution at a specific step. At this breakpoint, we can wait for human input. Once we have input from the user, we can add it to the graph state and proceed."
+    "We can implement this in LangGraph using [`interrupt()`][langgraph.types.interrupt]: `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input."
    ]
   },
   {
@@ -40,7 +40,7 @@
    "outputs": [],
    "source": [
     "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_anthropic langchain_openai"
+    "%pip install --quiet -U langgraph langchain_anthropic"
    ]
   },
   {
@@ -53,10 +53,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANTHROPIC_API_KEY:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -67,7 +75,6 @@
     "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
     "\n",
     "\n",
-    "_set_env(\"OPENAI_API_KEY\")\n",
     "_set_env(\"ANTHROPIC_API_KEY\")"
    ]
   },
@@ -93,25 +100,22 @@
     "\n",
     "Let's look at very basic usage of this. One intuitive approach is simply to create a node, `human_feedback`, that will get user feedback. This allows us to place our feedback gathering at a specific, chosen point in our graph.\n",
     " \n",
-    "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` our `human_feedback` node.\n",
+    "1) We call `interrupt()` inside our `human_feedback` 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 with the human response we get.\n",
-    "\n",
-    "* We [use the `as_node` parameter](https://langchain-ai.github.io/langgraph/concepts/low_level/#update-state) to apply this state update as the specified node, `human_feedback`.\n",
-    "* The graph will then resume execution as if the `human_feedback` node just acted."
+    "3) We use `Command(resume=...)` to provide the requested value to the human feedback node and resume execution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "58eae42d-be32-48da-8d0a-ab64471657d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -123,6 +127,7 @@
    "source": [
     "from typing_extensions import TypedDict\n",
     "from langgraph.graph import StateGraph, START, END\n",
+    "from langgraph.types import Command, interrupt\n",
     "from langgraph.checkpoint.memory import MemorySaver\n",
     "from IPython.display import Image, display\n",
     "\n",
@@ -139,7 +144,8 @@
     "\n",
     "def human_feedback(state):\n",
     "    print(\"---human_feedback---\")\n",
-    "    pass\n",
+    "    feedback = interrupt(\"Please provide feedback:\")\n",
+    "    return {\"user_feedback\": feedback}\n",
     "\n",
     "\n",
     "def step_3(state):\n",
@@ -160,7 +166,7 @@
     "memory = MemorySaver()\n",
     "\n",
     "# Add\n",
-    "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_feedback\"])\n",
+    "graph = builder.compile(checkpointer=memory)\n",
     "\n",
     "# View\n",
     "display(Image(graph.get_graph().draw_mermaid_png()))"
@@ -176,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "eb8e7d47-e7c9-4217-b72c-08394a2c4d3e",
    "metadata": {},
    "outputs": [
@@ -184,8 +190,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'input': 'hello world'}\n",
-      "---Step 1---\n"
+      "---Step 1---\n",
+      "{'step_1': None}\n",
+      "\n",
+      "\n",
+      "---human_feedback---\n",
+      "{'__interrupt__': (Interrupt(value='Please provide feedback:', resumable=True, ns=['human_feedback:e9a51d27-22ed-8c01-3f17-0ed33209b554'], when='during'),)}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
@@ -197,8 +209,9 @@
     "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)"
+    "for event in graph.stream(initial_input, thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -211,58 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "2165a1bc-1c5b-411f-9e9c-a2b9627e5d56",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--State after update--\n",
-      "StateSnapshot(values={'input': 'hello world', 'user_feedback': 'go to step 3!'}, next=('step_3',), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7830e-b807-6142-8002-1b511e4caf96'}}, metadata={'source': 'update', 'step': 2, 'writes': {'human_feedback': {'user_feedback': 'go to step 3!'}}, 'parents': {}}, created_at='2024-09-21T15:48:17.660131+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef7830e-36d1-6f1e-8001-4d4c913ae8a8'}}, tasks=(PregelTask(id='6b5486bf-eb6c-0e27-4784-cad2a69b86a2', name='step_3', path=('__pregel_pull', 'step_3'), error=None, interrupts=(), state=None),))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "('step_3',)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get user input\n",
-    "try:\n",
-    "    user_input = input(\"Tell me how you want to update the state: \")\n",
-    "except:\n",
-    "    user_input = \"go to step 3!\"\n",
-    "\n",
-    "# We now update the state as if we are the human_feedback node\n",
-    "graph.update_state(thread, {\"user_feedback\": user_input}, as_node=\"human_feedback\")\n",
-    "\n",
-    "# We can check the state\n",
-    "print(\"--State after update--\")\n",
-    "print(graph.get_state(thread))\n",
-    "\n",
-    "# We can check the next node, showing that it is node 3 (which follows human_feedback)\n",
-    "graph.get_state(thread).next"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ccc4a84a-02f2-4b79-a5a5-22173645526d",
-   "metadata": {},
-   "source": [
-    "We can proceed after our breakpoint - "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 5,
    "id": "3cca588f-e8d8-416b-aba7-0f3ae5e51598",
    "metadata": {},
    "outputs": [
@@ -270,14 +232,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "---Step 3---\n"
+      "---human_feedback---\n",
+      "{'human_feedback': {'user_feedback': 'go to step 3!'}}\n",
+      "\n",
+      "\n",
+      "---Step 3---\n",
+      "{'step_3': None}\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
     "# Continue the graph execution\n",
-    "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
-    "    print(event)"
+    "for event in graph.stream(Command(resume=\"go to step 3!\"), thread, stream_mode=\"updates\"):\n",
+    "    print(event)\n",
+    "    print(\"\\n\")"
    ]
   },
   {
@@ -290,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 6,
    "id": "2b83e5ca-8497-43ca-bff7-7203e654c4d3",
    "metadata": {},
    "outputs": [
@@ -300,7 +270,7 @@
        "{'input': 'hello world', 'user_feedback': 'go to step 3!'}"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -320,7 +290,7 @@
     " \n",
     "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n",
     "\n",
-    "We will use OpenAI and / or Anthropic's models and a fake tool (just for demo purposes)."
+    "We will use Anthropic's models and a fake tool (just for demo purposes)."
    ]
   },
   {
@@ -338,13 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "f5319e01",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -378,10 +348,8 @@
     "\n",
     "# Set up the model\n",
     "from langchain_anthropic import ChatAnthropic\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
-    "model = ChatOpenAI(model=\"gpt-4o\")\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
     "\n",
     "from pydantic import BaseModel\n",
     "\n",
@@ -407,7 +375,7 @@
     "    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",
+    "        return END\n",
     "    # If tool call is asking Human, we return that node\n",
     "    # You could also add logic here to let some system know that there's something that requires Human input\n",
     "    # For example, send a slack message, etc\n",
@@ -415,7 +383,7 @@
     "        return \"ask_human\"\n",
     "    # Otherwise if there is, we continue\n",
     "    else:\n",
-    "        return \"continue\"\n",
+    "        return \"action\"\n",
     "\n",
     "\n",
     "# Define the function that calls the model\n",
@@ -428,7 +396,12 @@
     "\n",
     "# We define a fake node to ask the human\n",
     "def ask_human(state):\n",
-    "    pass\n",
+    "    tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n",
+    "    location = interrupt(\"Please provide your location:\")\n",
+    "    tool_message = [\n",
+    "        {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": location}\n",
+    "    ]\n",
+    "    return {\"messages\": tool_message}\n",
     "\n",
     "\n",
     "# Build the graph\n",
@@ -453,21 +426,7 @@
     "    # 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",
-    "        # We may ask the human\n",
-    "        \"ask_human\": \"ask_human\",\n",
-    "        # Otherwise we finish.\n",
-    "        \"end\": END,\n",
-    "    },\n",
+    "    should_continue\n",
     ")\n",
     "\n",
     "# We now add a normal edge from `tools` to `agent`.\n",
@@ -486,7 +445,7 @@
     "# This compiles it into a LangChain Runnable,\n",
     "# meaning you can use it as you would any other runnable\n",
     "# We add a breakpoint BEFORE the `ask_human` node so it never executes\n",
-    "app = workflow.compile(checkpointer=memory, interrupt_before=[\"ask_human\"])\n",
+    "app = workflow.compile(checkpointer=memory)\n",
     "\n",
     "display(Image(app.get_graph().draw_mermaid_png()))"
    ]
@@ -505,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 8,
    "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
    "metadata": {},
    "outputs": [
@@ -517,11 +476,13 @@
       "\n",
       "Use the search tool to ask the user where they are, then look up the weather there\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"I'll help you with that. Let me first ask the user about their location.\", 'type': 'text'}, {'id': 'toolu_01KNvb7RCVu8yKYUuQQSKN1x', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  AskHuman (call_LDo62KBPQKZWxPI5IHxPBF0w)\n",
-      " Call ID: call_LDo62KBPQKZWxPI5IHxPBF0w\n",
+      "  AskHuman (toolu_01KNvb7RCVu8yKYUuQQSKN1x)\n",
+      " Call ID: toolu_01KNvb7RCVu8yKYUuQQSKN1x\n",
       "  Args:\n",
-      "    question: Can you tell me where you are located?\n"
+      "    question: Where are you located?\n"
      ]
     }
    ],
@@ -529,63 +490,32 @@
     "from langchain_core.messages import HumanMessage\n",
     "\n",
     "config = {\"configurable\": {\"thread_id\": \"2\"}}\n",
-    "input_message = HumanMessage(\n",
-    "    content=\"Use the search tool to ask the user where they are, then look up the weather there\"\n",
-    ")\n",
-    "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n",
+    "for event in app.stream(\n",
+    "    {\"messages\": [(\"user\", \"Use the search tool to ask the user where they are, then look up the weather there\")]},\n",
+    "    config,\n",
+    "    stream_mode=\"values\"\n",
+    "):\n",
     "    event[\"messages\"][-1].pretty_print()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "cc168c90-a374-4280-a9a6-8bc232dbb006",
-   "metadata": {},
-   "source": [
-    "We now want to update this thread with a response from the user. We then can kick off another run. \n",
-    "\n",
-    "Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "63598092-d565-4170-9773-e092d345f8c1",
+   "execution_count": 9,
+   "id": "924a30ea-94c0-468e-90fe-47eb9c08584d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('agent',)"
+       "('ask_human',)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tool_call_id = app.get_state(config).values[\"messages\"][-1].tool_calls[0][\"id\"]\n",
-    "\n",
-    "# We now create the tool call with the id and the response we want\n",
-    "tool_message = [\n",
-    "    {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": \"san francisco\"}\n",
-    "]\n",
-    "\n",
-    "# # This is equivalent to the below, either one works\n",
-    "# from langchain_core.messages import ToolMessage\n",
-    "# tool_message = [ToolMessage(tool_call_id=tool_call_id, content=\"san francisco\")]\n",
-    "\n",
-    "# We now update the state\n",
-    "# Notice that we are also specifying `as_node=\"ask_human\"`\n",
-    "# This will apply this update as this node,\n",
-    "# which will make it so that afterwards it continues as normal\n",
-    "app.update_state(config, {\"messages\": tool_message}, as_node=\"ask_human\")\n",
-    "\n",
-    "# We can check the state\n",
-    "# We can see that the state currently has the `agent` node next\n",
-    "# This is based on how we define our graph,\n",
-    "# where after the `ask_human` node goes (which we just triggered)\n",
-    "# there is an edge to the `agent` node\n",
     "app.get_state(config).next"
    ]
   },
@@ -594,12 +524,12 @@
    "id": "6a30c9fb-2a40-45cc-87ba-406c11c9f0cf",
    "metadata": {},
    "source": [
-    "We can now tell the agent to continue. We can just pass in `None` as the input to the graph, since no additional input is needed"
+    "You can see that our graph got interrupted inside the `ask_human` node, which is now waiting for a `location` to be provided. We can provide this value by invoking the graph with a `Command(resume=\"<location>\")` input:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 10,
    "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975",
    "metadata": {},
    "outputs": [
@@ -608,23 +538,40 @@
      "output_type": "stream",
      "text": [
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"I'll help you with that. Let me first ask the user about their location.\", 'type': 'text'}, {'id': 'toolu_01KNvb7RCVu8yKYUuQQSKN1x', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}]\n",
       "Tool Calls:\n",
-      "  search (call_LJlkCFfHvAS2taKHTaMmORE5)\n",
-      " Call ID: call_LJlkCFfHvAS2taKHTaMmORE5\n",
+      "  AskHuman (toolu_01KNvb7RCVu8yKYUuQQSKN1x)\n",
+      " Call ID: toolu_01KNvb7RCVu8yKYUuQQSKN1x\n",
       "  Args:\n",
-      "    query: current weather in San Francisco\n",
+      "    question: Where are you located?\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "\n",
+      "san francisco\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "[{'text': \"Now I'll search for the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01Y5C4rU9WcxBqFLYSMGjV1F', 'input': {'query': 'current weather in san francisco'}, 'name': 'search', 'type': 'tool_use'}]\n",
+      "Tool Calls:\n",
+      "  search (toolu_01Y5C4rU9WcxBqFLYSMGjV1F)\n",
+      " Call ID: toolu_01Y5C4rU9WcxBqFLYSMGjV1F\n",
+      "  Args:\n",
+      "    query: current weather in san francisco\n",
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: search\n",
       "\n",
-      "[\"I looked up: current weather in San Francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n",
+      "I looked up: current weather in san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
-      "The current weather in San Francisco is sunny. Enjoy the good weather! 🌞\n"
+      "Based on the search results, it's currently sunny in San Francisco. Note that this is the current weather at the time of our conversation, and conditions can change throughout the day.\n"
      ]
     }
    ],
    "source": [
-    "for event in app.stream(None, config, stream_mode=\"values\"):\n",
+    "for event in app.stream(\n",
+    "    Command(resume=\"san francisco\"), \n",
+    "    config, \n",
+    "    stream_mode=\"values\"\n",
+    "):\n",
     "    event[\"messages\"][-1].pretty_print()"
    ]
   }
@@ -645,7 +592,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,