diff --git a/examples/customer-support/customer-support.ipynb b/examples/customer-support/customer-support.ipynb
index b00dff705..de56f7c9c 100644
--- a/examples/customer-support/customer-support.ipynb
+++ b/examples/customer-support/customer-support.ipynb
@@ -236,7 +236,7 @@
    "outputs": [],
    "source": [
     "import sqlite3\n",
-    "from datetime import date, datetime, timedelta\n",
+    "from datetime import date, datetime\n",
     "from typing import Optional\n",
     "\n",
     "import pytz\n",
@@ -904,6 +904,7 @@
    "outputs": [],
    "source": [
     "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_core.messages import ToolMessage\n",
     "\n",
     "from langgraph.prebuilt import ToolNode\n",
     "\n",
@@ -2322,9 +2323,9 @@
     "        _print_event(event, _printed)\n",
     "    snapshot = part_2_graph.get_state(config)\n",
     "    while snapshot.next:\n",
-    "        # We have an interrupt! The agent is\n",
-    "        # trying to use a tool.\n",
-    "        # The user can approve or deny it\n",
+    "        # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n",
+    "        # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n",
+    "        # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n",
     "        user_input = input(\n",
     "            \"Do you approve of the above actions? Type 'y' to continue;\"\n",
     "            \" otherwise, explain your requested changed.\\n\\n\"\n",
@@ -2418,7 +2419,6 @@
     "\n",
     "    def __call__(self, state: State, config: RunnableConfig):\n",
     "        while True:\n",
-    "            passenger_id = config.get(\"passenger_id\", None)\n",
     "            result = self.runnable.invoke(state)\n",
     "            # If the LLM happens to return an empty response, we will re-prompt it\n",
     "            # for an actual response.\n",
@@ -2514,7 +2514,7 @@
     "\n",
     "from langgraph.checkpoint.sqlite import SqliteSaver\n",
     "from langgraph.graph import END, StateGraph\n",
-    "from langgraph.prebuilt import ToolNode, tools_condition\n",
+    "from langgraph.prebuilt import tools_condition\n",
     "\n",
     "builder = StateGraph(State)\n",
     "\n",
@@ -2944,9 +2944,9 @@
     "        _print_event(event, _printed)\n",
     "    snapshot = part_3_graph.get_state(config)\n",
     "    while snapshot.next:\n",
-    "        # We have an interrupt! The agent is\n",
-    "        # trying to use a tool.\n",
-    "        # The user can approve or deny it\n",
+    "        # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n",
+    "        # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n",
+    "        # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n",
     "        user_input = input(\n",
     "            \"Do you approve of the above actions? Type 'y' to continue;\"\n",
     "            \" otherwise, explain your requested changed.\\n\\n\"\n",
@@ -4352,9 +4352,9 @@
     "        _print_event(event, _printed)\n",
     "    snapshot = part_4_graph.get_state(config)\n",
     "    while snapshot.next:\n",
-    "        # We have an interrupt! The agent is\n",
-    "        # trying to use a tool.\n",
-    "        # The user can approve or deny it\n",
+    "        # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n",
+    "        # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n",
+    "        # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n",
     "        user_input = input(\n",
     "            \"Do you approve of the above actions? Type 'y' to continue;\"\n",
     "            \" otherwise, explain your requested changed.\\n\\n\"\n",