diff --git a/libs/cli/langchain_cli/integration_template/docs/tools.ipynb b/libs/cli/langchain_cli/integration_template/docs/tools.ipynb
index 7ca2ac5378d..f62e83fc6d8 100644
--- a/libs/cli/langchain_cli/integration_template/docs/tools.ipynb
+++ b/libs/cli/langchain_cli/integration_template/docs/tools.ipynb
@@ -181,11 +181,11 @@
    "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
    "metadata": {},
    "source": [
-    "## Chaining\n",
+    "## Use within an agent\n",
     "\n",
     "- TODO: Add user question and run cells\n",
     "\n",
-    "We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n",
+    "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
     "\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
     "\n",
@@ -208,6 +208,19 @@
     "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea35fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "tools = [tool]\n",
+    "agent = create_react_agent(llm, tools)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -215,32 +228,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnableConfig, chain\n",
+    "example_query = \"...\"\n",
     "\n",
-    "prompt = ChatPromptTemplate(\n",
-    "    [\n",
-    "        (\"system\", \"You are a helpful assistant.\"),\n",
-    "        (\"human\", \"{user_input}\"),\n",
-    "        (\"placeholder\", \"{messages}\"),\n",
-    "    ]\n",
+    "events = agent.stream(\n",
+    "    {\"messages\": [(\"user\", example_query)]},\n",
+    "    stream_mode=\"values\",\n",
     ")\n",
-    "\n",
-    "# specifying tool_choice will force the model to call this tool.\n",
-    "llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n",
-    "\n",
-    "llm_chain = prompt | llm_with_tools\n",
-    "\n",
-    "\n",
-    "@chain\n",
-    "def tool_chain(user_input: str, config: RunnableConfig):\n",
-    "    input_ = {\"user_input\": user_input}\n",
-    "    ai_msg = llm_chain.invoke(input_, config=config)\n",
-    "    tool_msgs = tool.batch(ai_msg.tool_calls, config=config)\n",
-    "    return llm_chain.invoke({**input_, \"messages\": [ai_msg, *tool_msgs]}, config=config)\n",
-    "\n",
-    "\n",
-    "tool_chain.invoke(\"...\")"
+    "for event in events:\n",
+    "    event[\"messages\"][-1].pretty_print()"
    ]
   },
   {