diff --git a/docs/docs/modules/agents/how_to/custom_agent.ipynb b/docs/docs/modules/agents/how_to/custom_agent.ipynb
index 665db61683..2bd19eba28 100644
--- a/docs/docs/modules/agents/how_to/custom_agent.ipynb
+++ b/docs/docs/modules/agents/how_to/custom_agent.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "This notebook goes through how to create your own custom agent.\n",
     "\n",
-    "In this example, we will use OpenAI Function Calling to create this agent.\n",
+    "In this example, we will use OpenAI Tool Calling to create this agent.\n",
     "**This is generally the most reliable way to create agents.**\n",
     "\n",
     "We will first create it WITHOUT memory, but we will then show how to add memory in.\n",
@@ -61,10 +61,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "fbe32b5f",
+   "execution_count": 2,
+   "id": "490bab35-adbb-4b45-8d0d-232414121e97",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from langchain.agents import tool\n",
     "\n",
@@ -75,6 +86,16 @@
     "    return len(word)\n",
     "\n",
     "\n",
+    "get_word_length.invoke(\"abc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c9821fb3-4449-49a0-a708-88a18d39e068",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "tools = [get_word_length]"
    ]
   },
@@ -91,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "aa4b50ea",
    "metadata": {},
    "outputs": [],
@@ -116,22 +137,24 @@
    "metadata": {},
    "source": [
     "## Bind tools to LLM\n",
-    "How does the agent know what tools it can use?\n",
-    "In this case we're relying on OpenAI function calling LLMs, which take functions as a separate argument and have been specifically trained to know when to invoke those functions.\n",
     "\n",
-    "To pass in our tools to the agent, we just need to format them to the [OpenAI function format](https://openai.com/blog/function-calling-and-other-api-updates) and pass them to our model. (By `bind`-ing the functions, we're making sure that they're passed in each time the model is invoked.)"
+    "How does the agent know what tools it can use?\n",
+    "\n",
+    "In this case we're relying on OpenAI tool calling LLMs, which take tools as a separate argument and have been specifically trained to know when to invoke those tools.\n",
+    "\n",
+    "To pass in our tools to the agent, we just need to format them to the [OpenAI tool format](https://platform.openai.com/docs/api-reference/chat/create) and pass them to our model. (By `bind`-ing the functions, we're making sure that they're passed in each time the model is invoked.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "e82713b6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.tools.convert_to_openai import format_tool_to_openai_function\n",
+    "from langchain_community.tools.convert_to_openai import format_tool_to_openai_tool\n",
     "\n",
-    "llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])"
+    "llm_with_tools = llm.bind(tools=[format_tool_to_openai_tool(tool) for tool in tools])"
    ]
   },
   {
@@ -146,30 +169,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "925a8ca4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
-    "from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
+    "from langchain.agents.format_scratchpad.openai_tools import (\n",
+    "    format_to_openai_tool_messages,\n",
+    ")\n",
+    "from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n",
     "\n",
     "agent = (\n",
     "    {\n",
     "        \"input\": lambda x: x[\"input\"],\n",
-    "        \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
+    "        \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
     "            x[\"intermediate_steps\"]\n",
     "        ),\n",
     "    }\n",
     "    | prompt\n",
     "    | llm_with_tools\n",
-    "    | OpenAIFunctionsAgentOutputParser()\n",
+    "    | OpenAIToolsAgentOutputParser()\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "9af9734e",
    "metadata": {},
    "outputs": [],
@@ -181,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "653b1617",
    "metadata": {},
    "outputs": [
@@ -193,10 +218,10 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `get_word_length` with `{'word': 'educa'}`\n",
+      "Invoking: `get_word_length` with `{'word': 'eudca'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"educa\".\u001b[0m\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"eudca\".\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -204,17 +229,21 @@
     {
      "data": {
       "text/plain": [
-       "{'input': 'How many letters in the word educa',\n",
-       " 'output': 'There are 5 letters in the word \"educa\".'}"
+       "[{'actions': [OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log=\"\\nInvoking: `get_word_length` with `{'word': 'eudca'}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n  \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})], tool_call_id='call_U9SR78eT398r9UbzID2N9LXh')],\n",
+       "  'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n  \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})]},\n",
+       " {'steps': [AgentStep(action=OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log=\"\\nInvoking: `get_word_length` with `{'word': 'eudca'}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_U9SR78eT398r9UbzID2N9LXh', 'function': {'arguments': '{\\n  \"word\": \"eudca\"\\n}', 'name': 'get_word_length'}, 'type': 'function'}]})], tool_call_id='call_U9SR78eT398r9UbzID2N9LXh'), observation=5)],\n",
+       "  'messages': [FunctionMessage(content='5', name='get_word_length')]},\n",
+       " {'output': 'There are 5 letters in the word \"eudca\".',\n",
+       "  'messages': [AIMessage(content='There are 5 letters in the word \"eudca\".')]}]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "agent_executor.invoke({\"input\": \"How many letters in the word educa\"})"
+    "list(agent_executor.stream({\"input\": \"How many letters in the word eudca\"}))"
    ]
   },
   {
@@ -227,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "60f5dc19",
    "metadata": {},
    "outputs": [
@@ -237,7 +266,7 @@
        "AIMessage(content='There are 6 letters in the word \"educa\".')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -270,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "169006d5",
    "metadata": {},
    "outputs": [],
@@ -301,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "8c03f36c",
    "metadata": {},
    "outputs": [],
@@ -321,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "5429d97f",
    "metadata": {},
    "outputs": [],
@@ -329,14 +358,14 @@
     "agent = (\n",
     "    {\n",
     "        \"input\": lambda x: x[\"input\"],\n",
-    "        \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
+    "        \"agent_scratchpad\": lambda x: format_to_openai_tool_messages(\n",
     "            x[\"intermediate_steps\"]\n",
     "        ),\n",
     "        \"chat_history\": lambda x: x[\"chat_history\"],\n",
     "    }\n",
     "    | prompt\n",
     "    | llm_with_tools\n",
-    "    | OpenAIFunctionsAgentOutputParser()\n",
+    "    | OpenAIToolsAgentOutputParser()\n",
     ")\n",
     "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
    ]
@@ -351,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "id": "9d9da346",
    "metadata": {},
    "outputs": [
@@ -386,7 +415,7 @@
        " 'output': 'No, \"educa\" is not a real word in English.'}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,14 +431,6 @@
     ")\n",
     "agent_executor.invoke({\"input\": \"is that a real word?\", \"chat_history\": chat_history})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f21bcd99",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -428,7 +449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {