diff --git a/docs/docs/use_cases/tool_use/agents.ipynb b/docs/docs/use_cases/tool_use/agents.ipynb
index eede7740d6..797a05c69d 100644
--- a/docs/docs/use_cases/tool_use/agents.ipynb
+++ b/docs/docs/use_cases/tool_use/agents.ipynb
@@ -17,13 +17,13 @@
     "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
-    "## Agents\n",
+    "## Repeated tool use with agents\n",
     "\n",
     "Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/modules/agents/) let us do just this.\n",
     "\n",
     "LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/modules/agents/agent_types/).\n",
     "\n",
-    "As an example, let's try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that the model can return multiple function invocations at once).\n",
+    "We'll use the [tool calling agent](/docs/modules/agents/agent_types/tool_calling/), which is generally the most reliable kind and the recommended one for most use cases. \"Tool calling\" in this case refers to a specific type of model API that allows for explicitly passing tool definitions to models and getting explicit tool invocations out. For more on tool calling models see [this guide].(/docs/modules/model_io/chat/function_calling/)\n",
     "\n",
     "![agent](../../../static/img/tool_agent.svg)"
    ]
@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain langchainhub"
    ]
   },
   {
@@ -53,12 +53,12 @@
    "id": "a33915ce-00c5-4379-8a83-c0053e471cdb",
    "metadata": {},
    "source": [
-    "And set these environment variables:"
+    "If you'd like to use LangSmith, set the environment variables below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "54667a49-c226-486d-a887-33120c90cc91",
    "metadata": {},
    "outputs": [],
@@ -66,9 +66,7 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
     "\n",
-    "# If you'd like to use LangSmith, uncomment the below\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
@@ -85,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "1c44ba79-6ab2-4d55-8247-82fca4d9b70c",
    "metadata": {},
    "outputs": [],
@@ -124,19 +122,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "e27a4e1a-938b-4b60-8e32-25e4ee530274",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain.agents import AgentExecutor, create_tool_calling_agent"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "bcc9536e-0328-4e29-9d3d-133f3e63e589",
    "metadata": {},
    "outputs": [
@@ -173,27 +170,45 @@
    "id": "85e9875a-d8d4-4712-b3f0-b513c684451b",
    "metadata": {},
    "source": [
-    "## Create agent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a1c5319d-6609-449d-8dd0-127e9a600656",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose the LLM that will drive the agent\n",
-    "# Only certain models support this\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)\n",
+    "## Create agent\n",
     "\n",
-    "# Construct the OpenAI Tools agent\n",
-    "agent = create_openai_tools_agent(model, tools, prompt)"
+    "We'll need to use a model with tool calling capabilities. You can see which models support tool calling [here](/docs/integrations/chat/).\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "9583aef3-a2cf-461e-8506-8a22f4c730b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a1c5319d-6609-449d-8dd0-127e9a600656",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the tool calling agent\n",
+    "agent = create_tool_calling_agent(llm, tools, prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "c86bfe50-c5b3-49ed-86c8-1fe8dcd0c83a",
    "metadata": {},
    "outputs": [],
@@ -212,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "c098f8df-fd7f-4c13-963a-8e34194d3f84",
    "metadata": {},
    "outputs": [
@@ -225,21 +240,23 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m\n",
       "Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`\n",
-      "\n",
+      "responded: [{'text': \"Okay, let's break this down step-by-step:\", 'type': 'text'}, {'id': 'toolu_01CjdiDhDmMtaT1F4R7hSV5D', 'input': {'base': 3, 'exponent': 5}, 'name': 'exponentiate', 'type': 'tool_use'}]\n",
       "\n",
       "\u001b[0m\u001b[38;5;200m\u001b[1;3m243\u001b[0m\u001b[32;1m\u001b[1;3m\n",
       "Invoking: `add` with `{'first_int': 12, 'second_int': 3}`\n",
-      "\n",
+      "responded: [{'text': '3 to the 5th power is 243.', 'type': 'text'}, {'id': 'toolu_01EKqn4E5w3Zj7bQ8s8xmi4R', 'input': {'first_int': 12, 'second_int': 3}, 'name': 'add', 'type': 'tool_use'}]\n",
       "\n",
       "\u001b[0m\u001b[33;1m\u001b[1;3m15\u001b[0m\u001b[32;1m\u001b[1;3m\n",
       "Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`\n",
-      "\n",
+      "responded: [{'text': '12 + 3 = 15', 'type': 'text'}, {'id': 'toolu_017VZJgZBYbwMo2KGD6o6hsQ', 'input': {'first_int': 243, 'second_int': 15}, 'name': 'multiply', 'type': 'tool_use'}]\n",
       "\n",
       "\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 3645, 'exponent': 2}`\n",
+      "Invoking: `multiply` with `{'first_int': 3645, 'second_int': 3645}`\n",
+      "responded: [{'text': '243 * 15 = 3645', 'type': 'text'}, {'id': 'toolu_01RtFCcQgbVGya3NVDgTYKTa', 'input': {'first_int': 3645, 'second_int': 3645}, 'name': 'multiply', 'type': 'tool_use'}]\n",
       "\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mSo 3645 squared is 13,286,025.\n",
       "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.\u001b[0m\n",
+      "Therefore, the final result of taking 3 to the 5th power (243), multiplying by 12 + 3 (15), and then squaring the whole result is 13,286,025.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -248,10 +265,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
-       " 'output': 'The result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.'}"
+       " 'output': 'So 3645 squared is 13,286,025.\\n\\nTherefore, the final result of taking 3 to the 5th power (243), multiplying by 12 + 3 (15), and then squaring the whole result is 13,286,025.'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,13 +280,21 @@
     "    }\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ecc190c-c133-493e-bd3e-f73e9690bae1",
+   "metadata": {},
+   "source": [
+    "You can see the [LangSmith trace here](https://smith.langchain.com/public/92694ff3-71b7-44ed-bc45-04bdf04d4689/r)."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/human_in_the_loop.ipynb b/docs/docs/use_cases/tool_use/human_in_the_loop.ipynb
index 9dd15837bd..188d8e8d5a 100644
--- a/docs/docs/use_cases/tool_use/human_in_the_loop.ipynb
+++ b/docs/docs/use_cases/tool_use/human_in_the_loop.ipynb
@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain"
    ]
   },
   {
@@ -48,8 +48,6 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
     "# If you'd like to use LangSmith, uncomment the below:\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
@@ -62,33 +60,57 @@
    "source": [
     "## Chain\n",
     "\n",
-    "Suppose we have the following (dummy) tools and tool-calling chain:"
+    "Suppose we have the following (dummy) tools and tool-calling chain:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "e0ff02ac-e750-493b-9b09-4578711a6726",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | outout: false\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "id": "0221fdfd-2a18-4449-a123-e6b0b15bb3d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'count_emails', 'args': {'last_n_days': 5}, 'output': 10}]"
+       "[{'name': 'count_emails',\n",
+       "  'args': {'last_n_days': 5},\n",
+       "  'id': 'toolu_012VHuh7vk5dVNct5SgZj3gh',\n",
+       "  'output': 10}]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from operator import itemgetter\n",
+    "from typing import Dict, List\n",
     "\n",
-    "from langchain.output_parsers import JsonOutputToolsParser\n",
-    "from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.messages import AIMessage\n",
+    "from langchain_core.runnables import Runnable, RunnablePassthrough\n",
     "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "@tool\n",
@@ -104,19 +126,19 @@
     "\n",
     "\n",
     "tools = [count_emails, send_email]\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0).bind_tools(tools)\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
     "\n",
     "\n",
-    "def call_tool(tool_invocation: dict) -> Runnable:\n",
-    "    \"\"\"Function for dynamically constructing the end of the chain based on the model-selected tool.\"\"\"\n",
+    "def call_tools(msg: AIMessage) -> List[Dict]:\n",
+    "    \"\"\"Simple sequential tool calling helper.\"\"\"\n",
     "    tool_map = {tool.name: tool for tool in tools}\n",
-    "    tool = tool_map[tool_invocation[\"type\"]]\n",
-    "    return RunnablePassthrough.assign(output=itemgetter(\"args\") | tool)\n",
+    "    tool_calls = msg.tool_calls.copy()\n",
+    "    for tool_call in tool_calls:\n",
+    "        tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
+    "    return tool_calls\n",
     "\n",
     "\n",
-    "# .map() allows us to apply a function to a list of inputs.\n",
-    "call_tool_list = RunnableLambda(call_tool).map()\n",
-    "chain = model | JsonOutputToolsParser() | call_tool_list\n",
+    "chain = llm_with_tools | call_tools\n",
     "chain.invoke(\"how many emails did i get in the last 5 days?\")"
    ]
   },
@@ -132,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "id": "341fb055-0315-47bc-8f72-ed6103d2981f",
    "metadata": {},
    "outputs": [],
@@ -140,23 +162,23 @@
     "import json\n",
     "\n",
     "\n",
-    "def human_approval(tool_invocations: list) -> Runnable:\n",
+    "def human_approval(msg: AIMessage) -> Runnable:\n",
     "    tool_strs = \"\\n\\n\".join(\n",
-    "        json.dumps(tool_call, indent=2) for tool_call in tool_invocations\n",
+    "        json.dumps(tool_call, indent=2) for tool_call in msg.tool_calls\n",
     "    )\n",
-    "    msg = (\n",
+    "    input_msg = (\n",
     "        f\"Do you approve of the following tool invocations\\n\\n{tool_strs}\\n\\n\"\n",
     "        \"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\"\n",
     "    )\n",
-    "    resp = input(msg)\n",
+    "    resp = input(input_msg)\n",
     "    if resp.lower() not in (\"yes\", \"y\"):\n",
     "        raise ValueError(f\"Tool invocations not approved:\\n\\n{tool_strs}\")\n",
-    "    return tool_invocations"
+    "    return msg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "id": "25dca07b-56ca-4b94-9955-d4f3e9895e03",
    "metadata": {},
    "outputs": [
@@ -167,34 +189,38 @@
       "Do you approve of the following tool invocations\n",
       "\n",
       "{\n",
-      "  \"type\": \"count_emails\",\n",
+      "  \"name\": \"count_emails\",\n",
       "  \"args\": {\n",
       "    \"last_n_days\": 5\n",
-      "  }\n",
+      "  },\n",
+      "  \"id\": \"toolu_01LCpjpFxrRspygDscnHYyPm\"\n",
       "}\n",
       "\n",
-      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. y\n"
+      "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. yes\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[{'type': 'count_emails', 'args': {'last_n_days': 5}, 'output': 10}]"
+       "[{'name': 'count_emails',\n",
+       "  'args': {'last_n_days': 5},\n",
+       "  'id': 'toolu_01LCpjpFxrRspygDscnHYyPm',\n",
+       "  'output': 10}]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain = model | JsonOutputToolsParser() | human_approval | call_tool_list\n",
+    "chain = llm_with_tools | human_approval | call_tools\n",
     "chain.invoke(\"how many emails did i get in the last 5 days?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 11,
    "id": "f558f2cd-847b-4ef9-a770-3961082b540c",
    "metadata": {},
    "outputs": [
@@ -205,11 +231,12 @@
       "Do you approve of the following tool invocations\n",
       "\n",
       "{\n",
-      "  \"type\": \"send_email\",\n",
+      "  \"name\": \"send_email\",\n",
       "  \"args\": {\n",
       "    \"message\": \"What's up homie\",\n",
       "    \"recipient\": \"sally@gmail.com\"\n",
-      "  }\n",
+      "  },\n",
+      "  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n",
       "}\n",
       "\n",
       "Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no. no\n"
@@ -217,20 +244,20 @@
     },
     {
      "ename": "ValueError",
-     "evalue": "Tool invocations not approved:\n\n{\n  \"type\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  }\n}",
+     "evalue": "Tool invocations not approved:\n\n{\n  \"name\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  },\n  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n}",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSend sally@gmail.com an email saying \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mWhat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms up homie\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:1774\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   1772\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1773\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 1774\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1775\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1776\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   1777\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1778\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1779\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1780\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1781\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   1782\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:3074\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3072\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   3073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3074\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3075\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3076\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3077\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3078\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3079\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3080\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3081\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3082\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3083\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3084\u001b[0m     )\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:975\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    972\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m    973\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m    974\u001b[0m         Output,\n\u001b[0;32m--> 975\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    978\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    979\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    982\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    983\u001b[0m     )\n\u001b[1;32m    984\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    985\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:323\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    322\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 323\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2950\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m   2948\u001b[0m                 output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m   2949\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2950\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2951\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   2952\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2953\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   2954\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:323\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    322\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 323\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[30], line 11\u001b[0m, in \u001b[0;36mhuman_approval\u001b[0;34m(tool_invocations)\u001b[0m\n\u001b[1;32m      9\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(msg)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myes\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool invocations not approved:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtool_strs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_invocations\n",
-      "\u001b[0;31mValueError\u001b[0m: Tool invocations not approved:\n\n{\n  \"type\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  }\n}"
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSend sally@gmail.com an email saying \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mWhat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms up homie\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2498\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2500\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2501\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   2502\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2503\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2504\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2505\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:3961\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3959\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   3960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3961\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3968\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3969\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3970\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3971\u001b[0m     )\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:1625\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1621\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m   1622\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m   1623\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m   1624\u001b[0m         Output,\n\u001b[0;32m-> 1625\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1626\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1627\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1628\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   1629\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1630\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1631\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1632\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m   1633\u001b[0m     )\n\u001b[1;32m   1634\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1635\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:347\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    346\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:3835\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m   3833\u001b[0m                 output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m   3834\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3835\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3836\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   3837\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3838\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   3839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/config.py:347\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    346\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[9], line 14\u001b[0m, in \u001b[0;36mhuman_approval\u001b[0;34m(msg)\u001b[0m\n\u001b[1;32m     12\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(input_msg)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resp\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myes\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool invocations not approved:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtool_strs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg\n",
+      "\u001b[0;31mValueError\u001b[0m: Tool invocations not approved:\n\n{\n  \"name\": \"send_email\",\n  \"args\": {\n    \"message\": \"What's up homie\",\n    \"recipient\": \"sally@gmail.com\"\n  },\n  \"id\": \"toolu_0158qJVd1AL32Y1xxYUAtNEy\"\n}"
      ]
     }
    ],
@@ -249,9 +276,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/multiple_tools.ipynb b/docs/docs/use_cases/tool_use/multiple_tools.ipynb
index 2bae4f24d3..cdc27dcb3c 100644
--- a/docs/docs/use_cases/tool_use/multiple_tools.ipynb
+++ b/docs/docs/use_cases/tool_use/multiple_tools.ipynb
@@ -37,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain-core"
    ]
   },
   {
@@ -45,12 +45,12 @@
    "id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
    "metadata": {},
    "source": [
-    "And set these environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4185e74b-0500-4cad-ace0-bac37de466ac",
    "metadata": {},
    "outputs": [],
@@ -58,9 +58,6 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
-    "# If you'd like to use LangSmith, uncomment the below\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
@@ -77,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "e13ec98c-8521-4d63-b521-caf92da87b70",
    "metadata": {},
    "outputs": [],
@@ -96,12 +93,12 @@
    "id": "3de233af-b3bd-4f0c-8b1a-83527143a8db",
    "metadata": {},
    "source": [
-    "And now we can add to it a `exponentiate` and `add` tool:"
+    "And now we can add to it an `exponentiate` and `add` tool:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "e93661cd-a2ba-4ada-91ad-baf1b60879ec",
    "metadata": {},
    "outputs": [],
@@ -123,60 +120,78 @@
    "id": "bbea4555-ed10-4a18-b802-e9a3071f132b",
    "metadata": {},
    "source": [
-    "The main difference between using one Tool and many, is that in the case of many we can't be sure which Tool the model will invoke. So we cannot hardcode, like we did in the [Quickstart](/docs/use_cases/tool_use/quickstart), a specific tool into our chain. Instead we'll add `call_tool_list`, a `RunnableLambda` that takes the `JsonOutputToolsParser` output and actually builds the end of the chain based on it, meaning it appends the Tools that were envoked to the end of the chain at runtime. We can do this because LCEL has the cool property that in any Runnable (the core building block of LCEL) sequence, if one component returns more Runnables, those are run as part of the chain."
+    "The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the [Quickstart](/docs/use_cases/tool_use/quickstart), a specific tool into our chain. Instead we'll add `call_tools`, a `RunnableLambda` that takes the output AI message with tools calls and routes to the correct tools.\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
+   "id": "f00f0f3f-8530-4c1d-a26c-d20824e31faf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_anthropic import ChatAnthropic\n",
+    "\n",
+    "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
    "metadata": {},
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
-    "from typing import Union\n",
+    "from typing import Dict, List, Union\n",
     "\n",
-    "from langchain.output_parsers import JsonOutputToolsParser\n",
+    "from langchain_core.messages import AIMessage\n",
     "from langchain_core.runnables import (\n",
     "    Runnable,\n",
     "    RunnableLambda,\n",
     "    RunnableMap,\n",
     "    RunnablePassthrough,\n",
     ")\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "tools = [multiply, exponentiate, add]\n",
-    "model_with_tools = model.bind_tools(tools)\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
     "tool_map = {tool.name: tool for tool in tools}\n",
     "\n",
     "\n",
-    "def call_tool(tool_invocation: dict) -> Union[str, Runnable]:\n",
-    "    \"\"\"Function for dynamically constructing the end of the chain based on the model-selected tool.\"\"\"\n",
-    "    tool = tool_map[tool_invocation[\"type\"]]\n",
-    "    return RunnablePassthrough.assign(output=itemgetter(\"args\") | tool)\n",
+    "def call_tools(msg: AIMessage) -> Runnable:\n",
+    "    \"\"\"Simple sequential tool calling helper.\"\"\"\n",
+    "    tool_map = {tool.name: tool for tool in tools}\n",
+    "    tool_calls = msg.tool_calls.copy()\n",
+    "    for tool_call in tool_calls:\n",
+    "        tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
+    "    return tool_calls\n",
     "\n",
     "\n",
-    "# .map() allows us to apply a function to a list of inputs.\n",
-    "call_tool_list = RunnableLambda(call_tool).map()\n",
-    "chain = model_with_tools | JsonOutputToolsParser() | call_tool_list"
+    "chain = llm_with_tools | call_tools"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'multiply',\n",
+       "[{'name': 'multiply',\n",
        "  'args': {'first_int': 23, 'second_int': 7},\n",
+       "  'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',\n",
        "  'output': 161}]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,19 +202,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "b1c6c0f8-6d04-40d4-a40e-8719ca7b27c2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'add',\n",
+       "[{'name': 'add',\n",
        "  'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
+       "  'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',\n",
        "  'output': 1001000000}]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -210,19 +226,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "ce76f299-1a4d-421c-afa4-a6346e34285c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'exponentiate',\n",
+       "[{'name': 'exponentiate',\n",
        "  'args': {'base': 37, 'exponent': 3},\n",
+       "  'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',\n",
        "  'output': 50653}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -234,9 +251,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/parallel.ipynb b/docs/docs/use_cases/tool_use/parallel.ipynb
index e851328168..f0a22567a9 100644
--- a/docs/docs/use_cases/tool_use/parallel.ipynb
+++ b/docs/docs/use_cases/tool_use/parallel.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Parallel tool use\n",
     "\n",
-    "In the [Chains with multiple tools](/docs/use_cases/tool_use/multiple_tools) guide we saw how to build function-calling chains that select between multiple tools. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. Our previous chain from the multiple tools guides actually already supports this, we just need to use an OpenAI model capable of parallel function calling."
+    "In the [Chains with multiple tools](/docs/use_cases/tool_use/multiple_tools) guide we saw how to build function-calling chains that select between multiple tools. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. Our previous chain from the multiple tools guides actually already supports this."
    ]
   },
   {
@@ -27,7 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain-core"
    ]
   },
   {
@@ -35,7 +35,7 @@
    "id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
    "metadata": {},
    "source": [
-    "And set these environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {
@@ -48,9 +48,6 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
-    "# If you'd like to use LangSmith, uncomment the below\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
@@ -65,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "e13ec98c-8521-4d63-b521-caf92da87b70",
    "metadata": {},
    "outputs": [],
@@ -98,67 +95,91 @@
    "source": [
     "# Chain\n",
     "\n",
-    "Notice we use an `-1106` model, which as of this writing is the only kind that supports parallel function calling:"
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\" hideGoogle=\"true\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
+   "id": "f67d91d8-cc38-4065-8f80-901e079954dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
    "metadata": {},
    "outputs": [],
    "source": [
     "from operator import itemgetter\n",
-    "from typing import Union\n",
+    "from typing import Dict, List, Union\n",
     "\n",
-    "from langchain.output_parsers import JsonOutputToolsParser\n",
+    "from langchain_core.messages import AIMessage\n",
     "from langchain_core.runnables import (\n",
     "    Runnable,\n",
     "    RunnableLambda,\n",
     "    RunnableMap,\n",
     "    RunnablePassthrough,\n",
     ")\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")\n",
     "tools = [multiply, exponentiate, add]\n",
-    "model_with_tools = model.bind_tools(tools)\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
     "tool_map = {tool.name: tool for tool in tools}\n",
     "\n",
     "\n",
-    "def call_tool(tool_invocation: dict) -> Union[str, Runnable]:\n",
-    "    \"\"\"Function for dynamically constructing the end of the chain based on the model-selected tool.\"\"\"\n",
-    "    tool = tool_map[tool_invocation[\"type\"]]\n",
-    "    return RunnablePassthrough.assign(output=itemgetter(\"args\") | tool)\n",
+    "def call_tools(msg: AIMessage) -> Runnable:\n",
+    "    \"\"\"Simple sequential tool calling helper.\"\"\"\n",
+    "    tool_map = {tool.name: tool for tool in tools}\n",
+    "    tool_calls = msg.tool_calls.copy()\n",
+    "    for tool_call in tool_calls:\n",
+    "        tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
+    "    return tool_calls\n",
     "\n",
     "\n",
-    "# .map() allows us to apply a function to a list of inputs.\n",
-    "call_tool_list = RunnableLambda(call_tool).map()\n",
-    "chain = model_with_tools | JsonOutputToolsParser() | call_tool_list"
+    "chain = llm_with_tools | call_tools"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'multiply',\n",
+       "[{'name': 'multiply',\n",
        "  'args': {'first_int': 23, 'second_int': 7},\n",
+       "  'id': 'call_22tgOrsVLyLMsl2RLbUhtycw',\n",
        "  'output': 161},\n",
-       " {'type': 'add', 'args': {'first_int': 5, 'second_int': 18}, 'output': 23},\n",
-       " {'type': 'add',\n",
+       " {'name': 'multiply',\n",
+       "  'args': {'first_int': 5, 'second_int': 18},\n",
+       "  'id': 'call_EbKHEG3TjqBhEwb7aoxUtgzf',\n",
+       "  'output': 90},\n",
+       " {'name': 'add',\n",
        "  'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
+       "  'id': 'call_LUhu2IT3vINxlTc5fCVY6Nhi',\n",
        "  'output': 1001000000},\n",
-       " {'type': 'exponentiate',\n",
+       " {'name': 'exponentiate',\n",
        "  'args': {'base': 37, 'exponent': 3},\n",
+       "  'id': 'call_bnCZIXelOKkmcyd4uGXId9Ct',\n",
        "  'output': 50653}]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -172,9 +193,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/prompting.ipynb b/docs/docs/use_cases/tool_use/prompting.ipynb
index 09dcf0b460..6e36db4330 100644
--- a/docs/docs/use_cases/tool_use/prompting.ipynb
+++ b/docs/docs/use_cases/tool_use/prompting.ipynb
@@ -15,9 +15,9 @@
    "id": "14b94240",
    "metadata": {},
    "source": [
-    "# Tool use without function calling\n",
+    "# Using models that don't support tool calling\n",
     "\n",
-    "In this guide we'll build a Chain that does not rely on any special model APIs (like function-calling, which we showed in the [Quickstart](/docs/use_cases/tool_use/quickstart)) and instead just prompts the model directly to invoke tools."
+    "In this guide we'll build a Chain that does not rely on any special model APIs (like tool calling, which we showed in the [Quickstart](/docs/use_cases/tool_use/quickstart)) and instead just prompts the model directly to invoke tools."
    ]
   },
   {
@@ -393,9 +393,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/quickstart.ipynb b/docs/docs/use_cases/tool_use/quickstart.ipynb
index d363ab853a..3b5a476d48 100644
--- a/docs/docs/use_cases/tool_use/quickstart.ipynb
+++ b/docs/docs/use_cases/tool_use/quickstart.ipynb
@@ -37,7 +37,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain"
    ]
   },
   {
@@ -45,7 +45,7 @@
    "id": "36a9c6fc-8264-462f-b8d7-9c7bbec22ef9",
    "metadata": {},
    "source": [
-    "And set these environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {
@@ -58,9 +58,6 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
-    "# If you'd like to use LangSmith, uncomment the below\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
@@ -77,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "90187d07",
    "metadata": {},
    "outputs": [],
@@ -145,22 +142,31 @@
     "\n",
     "![chain](../../../static/img/tool_chain.svg)\n",
     "\n",
-    "### Function calling\n",
-    "One of the most reliable ways to use tools with LLMs is with function calling APIs (also sometimes called tool calling or parallel function calling). This only works with models that explicitly support function calling, like OpenAI models. To learn more head to the [function calling guide](/docs/modules/model_io/chat/function_calling).\n",
+    "### Tool/function calling\n",
+    "One of the most reliable ways to use tools with LLMs is with tool calling APIs (also sometimes called function calling). This only works with models that explicitly support tool calling. You can see which models support tool calling [here](/docs/integrations/chat/), and learn more about how to use tool calling in [this guide](/docs/modules/model_io/chat/function_calling).\n",
     "\n",
-    "First we'll define our model and tools. We'll start with just a single tool, `multiply`."
+    "First we'll define our model and tools. We'll start with just a single tool, `multiply`.\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "9bce8935-1465-45ac-8a93-314222c753c4",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "\n",
     "from langchain_openai.chat_models import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
    ]
   },
   {
@@ -168,131 +174,57 @@
    "id": "c22e6f0f-c5ad-4c0f-9514-e626704ea51c",
    "metadata": {},
    "source": [
-    "Next we'll convert our LangChain Tool to an OpenAI format JSONSchema function, and bind this as the `tools` argument to be passed to all ChatOpenAI calls. Since we only have a single Tool and in this initial chain we want to make sure it's always used, we'll also specify `tool_choice`. See the [OpenAI chat API reference](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice) for more on these parameters:"
+    "We'll use `bind_tools` to pass the definition of our tool in as part of each call to the model, so that the model can invoke the tool when appropriate:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "3bfe2cdc-7d72-457c-a9a1-5fa1e0bcde55",
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_with_tools = model.bind_tools([multiply], tool_choice=\"multiply\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "19f6285f-d8b1-432c-8c07-f7aee3fc0fa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'type': 'function',\n",
-       "  'function': {'name': 'multiply',\n",
-       "   'description': 'multiply(first_int: int, second_int: int) -> int - Multiply two integers together.',\n",
-       "   'parameters': {'type': 'object',\n",
-       "    'properties': {'first_int': {'type': 'integer'},\n",
-       "     'second_int': {'type': 'integer'}},\n",
-       "    'required': ['first_int', 'second_int']}}}]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_tools.kwargs[\"tools\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "340c1b04-38cb-4467-83ca-8aa2b59176d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'type': 'function', 'function': {'name': 'multiply'}}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_with_tools.kwargs[\"tool_choice\"]"
+    "llm_with_tools = llm.bind_tools([multiply])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9fa2ba14-9a97-4960-a6c7-422edecdaf4b",
+   "id": "07fc830e-a6d2-4fac-904b-b94072e64018",
    "metadata": {},
    "source": [
-    "Now we'll compose out tool-calling model with a `JsonOutputToolsParser`, a built-in LangChain output parser that converts an OpenAI function-calling response to a list of `{\"type\": \"TOOL_NAME\", \"args\": {...}}` dicts with the tools to invoke and arguments to invoke them with."
+    "When the model invokes the tool, this will show up in the `AIMessage.tool_calls` attribute of the output:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "5518aba4-c44d-4896-9b63-fc9d56c245df",
+   "execution_count": 9,
+   "id": "68f30343-14ef-48f1-badd-b6a03977316d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[{'type': 'multiply', 'args': {'first_int': 4, 'second_int': 23}}]"
+       "[{'name': 'multiply',\n",
+       "  'args': {'first_int': 5, 'second_int': 42},\n",
+       "  'id': 'call_cCP9oA3tRz7HDrjFn1FdmDaG'}]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from langchain.output_parsers import JsonOutputToolsParser\n",
-    "\n",
-    "chain = model_with_tools | JsonOutputToolsParser()\n",
-    "chain.invoke(\"What's four times 23\")"
+    "msg = llm_with_tools.invoke(\"whats 5 times forty two\")\n",
+    "msg.tool_calls"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7f712d8d-0314-4d3d-b563-378b72fd8bb5",
+   "id": "330015a3-a5a7-433a-826a-6277766f6c27",
    "metadata": {},
    "source": [
-    "Since we know we're always invoking the `multiply` tool, we can simplify our output a bit to return only the args for the `multiply` tool using the `JsonoutputKeyToolsParser`. To further simplify we'll specify `first_tool_only=True`, so that instead of a list of tool invocations our output parser returns only the first tool invocation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "cfacfcdc-8a45-4c60-a175-7efe9534f83e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'first_int': 4, 'second_int': 23}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.output_parsers import JsonOutputKeyToolsParser\n",
-    "\n",
-    "chain = model_with_tools | JsonOutputKeyToolsParser(\n",
-    "    key_name=\"multiply\", first_tool_only=True\n",
-    ")\n",
-    "chain.invoke(\"What's four times 23\")"
+    "Check out the [LangSmith trace here](https://smith.langchain.com/public/81ff0cbd-e05b-4720-bf61-2c9807edb708/r)."
    ]
   },
   {
@@ -302,12 +234,12 @@
    "source": [
     "### Invoking the tool\n",
     "\n",
-    "Great! We're able to generate tool invocations. But what if we want to actually call the tool? To do that we just need to pass them to the tool:"
+    "Great! We're able to generate tool invocations. But what if we want to actually call the tool? To do so we'll need to pass the generated tool args to our tool. As a simple example we'll just extract the arguments of the first tool_call:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "4f5325ca-e5dc-4d1a-ba36-b085a029c90a",
    "metadata": {},
    "outputs": [
@@ -317,7 +249,7 @@
        "92"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,15 +257,18 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "# Note: the `.map()` at the end of `multiply` allows us to pass in a list of `multiply` arguments instead of a single one.\n",
-    "chain = (\n",
-    "    model_with_tools\n",
-    "    | JsonOutputKeyToolsParser(key_name=\"multiply\", first_tool_only=True)\n",
-    "    | multiply\n",
-    ")\n",
+    "chain = llm_with_tools | (lambda x: x.tool_calls[0][\"args\"]) | multiply\n",
     "chain.invoke(\"What's four times 23\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "79a9eb63-383d-4dd4-a162-08b4a52ef4d9",
+   "metadata": {},
+   "source": [
+    "Check out the [LangSmith trace here](https://smith.langchain.com/public/16bbabb9-fc9b-41e5-a33d-487c42df4f85/r)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0521d3d5",
@@ -345,47 +280,54 @@
     "\n",
     "LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/modules/agents/agent_types/).\n",
     "\n",
-    "As an example, let's try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that the model can return multiple function invocations at once)\n",
+    "We'll use the [tool calling agent](/docs/modules/agents/agent_types/tool_calling/), which is generally the most reliable kind and the recommended one for most use cases.\n",
     "\n",
     "![agent](../../../static/img/tool_agent.svg)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 13,
    "id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain import hub\n",
-    "from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain.agents import AgentExecutor, create_tool_calling_agent"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 14,
    "id": "6be83879-9da3-4dd9-b147-a79f76affd7a",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a helpful assistant')),\n",
-       " MessagesPlaceholder(variable_name='chat_history', optional=True),\n",
-       " HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template='{input}')),\n",
-       " MessagesPlaceholder(variable_name='agent_scratchpad')]"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m System Message \u001b[0m================================\n",
+      "\n",
+      "You are a helpful assistant\n",
+      "\n",
+      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
+      "\n",
+      "\u001b[33;1m\u001b[1;3m{chat_history}\u001b[0m\n",
+      "\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n",
+      "\n",
+      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
+      "\n",
+      "\u001b[33;1m\u001b[1;3m{agent_scratchpad}\u001b[0m\n"
+     ]
     }
    ],
    "source": [
-    "# Get the prompt to use - you can modify this!\n",
+    "# Get the prompt to use - can be replaced with any prompt that includes variables \"agent_scratchpad\" and \"input\"!\n",
     "prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
-    "prompt.messages"
+    "prompt.pretty_print()"
    ]
   },
   {
@@ -398,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "95c86d32-ee45-4c87-a28c-14eff19b49e9",
    "metadata": {},
    "outputs": [],
@@ -420,22 +362,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 16,
    "id": "17b09ac6-c9b7-4340-a8a0-3d3061f7888c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Choose the LLM that will drive the agent\n",
-    "# Only certain models support this\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)\n",
-    "\n",
-    "# Construct the OpenAI Tools agent\n",
-    "agent = create_openai_tools_agent(model, tools, prompt)"
+    "# Construct the tool calling agent\n",
+    "agent = create_tool_calling_agent(llm, tools, prompt)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 17,
    "id": "675091d2-cac9-45c4-a5d7-b760ee6c1986",
    "metadata": {},
    "outputs": [],
@@ -454,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 18,
    "id": "f7dbb240-809e-4e41-8f63-1a4636e8e26d",
    "metadata": {},
    "outputs": [
@@ -478,10 +416,16 @@
       "\n",
       "\n",
       "\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `exponentiate` with `{'base': 3645, 'exponent': 2}`\n",
+      "Invoking: `exponentiate` with `{'base': 405, 'exponent': 2}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.\u001b[0m\n",
+      "\u001b[0m\u001b[38;5;200m\u001b[1;3m164025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
+      "\n",
+      "The sum of twelve and three is 15. \n",
+      "\n",
+      "Multiplying 243 by 15 gives 3645. \n",
+      "\n",
+      "Finally, squaring 3645 gives 164025.\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -490,10 +434,10 @@
      "data": {
       "text/plain": [
        "{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
-       " 'output': 'The result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.'}"
+       " 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 164025.'}"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -506,6 +450,14 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8fdb0ed9-1763-4778-a7d6-026578cd9585",
+   "metadata": {},
+   "source": [
+    "Check out the [LangSmith trace here](https://smith.langchain.com/public/eeeb27a4-a2f8-4f06-a3af-9c983f76146c/r)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b0e4b7f4-58ce-4ca0-a986-d05a436a7ccf",
@@ -524,9 +476,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/docs/docs/use_cases/tool_use/tool_error_handling.ipynb b/docs/docs/use_cases/tool_use/tool_error_handling.ipynb
index c129b0ebee..db0fe2a196 100644
--- a/docs/docs/use_cases/tool_use/tool_error_handling.ipynb
+++ b/docs/docs/use_cases/tool_use/tool_error_handling.ipynb
@@ -5,7 +5,7 @@
    "id": "5d60cbb9-2a6a-43ea-a9e9-f67b16ddd2b2",
    "metadata": {},
    "source": [
-    "# Tool error handling\n",
+    "# Handling tool errors\n",
     "\n",
     "Using a model to invoke a tool has some obvious potential failure modes. Firstly, the model needs to return a output that can be parsed at all. Secondly, the model needs to return tool arguments that are valid.\n",
     "\n",
@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet langchain langchain-openai"
+    "%pip install --upgrade --quiet langchain-core langchain-openai"
    ]
   },
   {
@@ -37,7 +37,7 @@
    "id": "68107597-0c8c-4bb5-8c12-9992fabdf71a",
    "metadata": {},
    "source": [
-    "And set these environment variables:"
+    "If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
    ]
   },
   {
@@ -50,9 +50,6 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
-    "\n",
-    "# If you'd like to use LangSmith, uncomment the below:\n",
     "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
@@ -64,12 +61,33 @@
    "source": [
     "## Chain\n",
     "\n",
-    "Suppose we have the following (dummy) tool and tool-calling chain. We'll make our tool intentionally convoluted to try and trip up the model."
+    "Suppose we have the following (dummy) tool and tool-calling chain. We'll make our tool intentionally convoluted to try and trip up the model.\n",
+    "\n",
+    "```{=mdx}\n",
+    "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
+    "\n",
+    "<ChatModelTabs customVarName=\"llm\"/>\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "86258950-5e61-4340-81b9-84a5d26e8773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# | echo: false\n",
+    "# | output: false\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "1d20604e-c4d1-4d21-841b-23e4f61aec36",
    "metadata": {},
    "outputs": [],
@@ -91,13 +109,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define model and bind tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
-    "model_with_tools = model.bind_tools(\n",
+    "llm_with_tools = llm.bind_tools(\n",
     "    [complex_tool],\n",
-    "    tool_choice=\"complex_tool\",\n",
     ")"
    ]
   },
@@ -109,16 +122,7 @@
    "outputs": [],
    "source": [
     "# Define chain\n",
-    "from operator import itemgetter\n",
-    "\n",
-    "from langchain.output_parsers import JsonOutputKeyToolsParser\n",
-    "from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough\n",
-    "\n",
-    "chain = (\n",
-    "    model_with_tools\n",
-    "    | JsonOutputKeyToolsParser(key_name=\"complex_tool\", first_tool_only=True)\n",
-    "    | complex_tool\n",
-    ")"
+    "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool"
    ]
   },
   {
@@ -131,25 +135,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "d354664c-ac44-4967-a35f-8912b3ad9477",
    "metadata": {},
    "outputs": [
     {
      "ename": "ValidationError",
-     "evalue": "1 validation error for complex_toolSchemaSchema\ndict_arg\n  field required (type=value_error.missing)",
+     "evalue": "1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:1774\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   1772\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1773\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 1774\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1775\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1776\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   1777\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1778\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1779\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1780\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1781\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   1782\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:210\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    205\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    206\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    208\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    209\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 210\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    211\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:315\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m    302\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    303\u001b[0m     tool_input: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    312\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    313\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    314\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 315\u001b[0m     parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m verbose \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    317\u001b[0m         verbose_ \u001b[38;5;241m=\u001b[39m verbose\n",
-      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:250\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 250\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    251\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    252\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    253\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    254\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    255\u001b[0m         }\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2498\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2500\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2501\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   2502\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2503\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2504\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2505\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:241\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    236\u001b[0m     \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m    237\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    239\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    240\u001b[0m     config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 241\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    242\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    243\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    244\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    245\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    248\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    249\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:387\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 387\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m    389\u001b[0m         observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:378\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    364\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_tool_start(\n\u001b[1;32m    365\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdescription\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdescription},\n\u001b[1;32m    366\u001b[0m     tool_input \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tool_input, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(tool_input),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    376\u001b[0m )\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m     parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    379\u001b[0m     tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m    380\u001b[0m     observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    381\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    382\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    383\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m    384\u001b[0m     )\n",
+      "File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:283\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    282\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 283\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    284\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    285\u001b[0m             k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m    286\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    287\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m    288\u001b[0m         }\n\u001b[1;32m    289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
       "File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:526\u001b[0m, in \u001b[0;36mBaseModel.parse_obj\u001b[0;34m(cls, obj)\u001b[0m\n\u001b[1;32m    524\u001b[0m         exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m expected dict not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    525\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ValidationError([ErrorWrapper(exc, loc\u001b[38;5;241m=\u001b[39mROOT_KEY)], \u001b[38;5;28mcls\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m--> 526\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:341\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m    339\u001b[0m values, fields_set, validation_error \u001b[38;5;241m=\u001b[39m validate_model(__pydantic_self__\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, data)\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[0;32m--> 341\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    343\u001b[0m     object_setattr(__pydantic_self__, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__dict__\u001b[39m\u001b[38;5;124m'\u001b[39m, values)\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchemaSchema\ndict_arg\n  field required (type=value_error.missing)"
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n  field required (type=value_error.missing)"
      ]
     }
    ],
@@ -171,14 +176,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "8fedb550-683d-45ae-8876-ae7acb332019",
    "metadata": {},
    "outputs": [],
    "source": [
     "from typing import Any\n",
     "\n",
-    "from langchain_core.runnables import RunnableConfig\n",
+    "from langchain_core.runnables import Runnable, RunnableConfig\n",
     "\n",
     "\n",
     "def try_except_tool(tool_args: dict, config: RunnableConfig) -> Runnable:\n",
@@ -188,16 +193,12 @@
     "        return f\"Calling tool with arguments:\\n\\n{tool_args}\\n\\nraised the following error:\\n\\n{type(e)}: {e}\"\n",
     "\n",
     "\n",
-    "chain = (\n",
-    "    model_with_tools\n",
-    "    | JsonOutputKeyToolsParser(key_name=\"complex_tool\", first_tool_only=True)\n",
-    "    | try_except_tool\n",
-    ")"
+    "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | try_except_tool"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "71a2c98d-c0be-4c0a-bb3d-41ad4596526c",
    "metadata": {},
    "outputs": [
@@ -211,7 +212,7 @@
       "\n",
       "raised the following error:\n",
       "\n",
-      "<class 'pydantic.v1.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchemaSchema\n",
+      "<class 'pydantic.v1.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
       "dict_arg\n",
       "  field required (type=value_error.missing)\n"
      ]
@@ -237,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "02cc4223-35fa-4240-976a-012299ca703c",
    "metadata": {},
    "outputs": [
@@ -247,25 +248,17 @@
        "10.5"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain = (\n",
-    "    model_with_tools\n",
-    "    | JsonOutputKeyToolsParser(key_name=\"complex_tool\", first_tool_only=True)\n",
-    "    | complex_tool\n",
-    ")\n",
+    "chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
     "better_model = ChatOpenAI(model=\"gpt-4-1106-preview\", temperature=0).bind_tools(\n",
     "    [complex_tool], tool_choice=\"complex_tool\"\n",
     ")\n",
-    "better_chain = (\n",
-    "    better_model\n",
-    "    | JsonOutputKeyToolsParser(key_name=\"complex_tool\", first_tool_only=True)\n",
-    "    | complex_tool\n",
-    ")\n",
+    "better_chain = better_model | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
     "\n",
     "chain_with_fallback = chain.with_fallbacks([better_chain])\n",
     "chain_with_fallback.invoke(\n",
@@ -278,7 +271,7 @@
    "id": "412f8c4e-cc83-4d87-84a1-5ba2f8edb1e9",
    "metadata": {},
    "source": [
-    "Looking at the [Langsmith trace](https://smith.langchain.com/public/241e1266-8555-4d49-99dc-b8df46109c39/r) for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds."
+    "Looking at the [Langsmith trace](https://smith.langchain.com/public/00e91fc2-e1a4-4b0f-a82e-e6b3119d196c/r) for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds."
    ]
   },
   {
@@ -293,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "b5659956-9454-468a-9753-a3ff9052b8f5",
    "metadata": {},
    "outputs": [],
@@ -301,7 +294,7 @@
     "import json\n",
     "from typing import Any\n",
     "\n",
-    "from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, ToolCall, ToolMessage\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
@@ -309,36 +302,30 @@
     "class CustomToolException(Exception):\n",
     "    \"\"\"Custom LangChain tool exception.\"\"\"\n",
     "\n",
-    "    def __init__(self, tool_call: dict, exception: Exception) -> None:\n",
+    "    def __init__(self, tool_call: ToolCall, exception: Exception) -> None:\n",
     "        super().__init__()\n",
     "        self.tool_call = tool_call\n",
     "        self.exception = exception\n",
     "\n",
     "\n",
-    "def tool_custom_exception(tool_call: dict, config: RunnableConfig) -> Runnable:\n",
+    "def tool_custom_exception(msg: AIMessage, config: RunnableConfig) -> Runnable:\n",
     "    try:\n",
-    "        return complex_tool.invoke(tool_call[\"args\"], config=config)\n",
+    "        return complex_tool.invoke(msg.tool_calls[0][\"args\"], config=config)\n",
     "    except Exception as e:\n",
-    "        raise CustomToolException(tool_call, e)\n",
+    "        raise CustomToolException(msg.tool_calls[0], e)\n",
     "\n",
     "\n",
     "def exception_to_messages(inputs: dict) -> dict:\n",
     "    exception = inputs.pop(\"exception\")\n",
-    "    tool_call = {\n",
-    "        \"type\": \"function\",\n",
-    "        \"function\": {\n",
-    "            \"name\": \"complex_tool\",\n",
-    "            \"arguments\": json.dumps(exception.tool_call[\"args\"]),\n",
-    "        },\n",
-    "        \"id\": exception.tool_call[\"id\"],\n",
-    "    }\n",
     "\n",
     "    # Add historical messages to the original input, so the model knows that it made a mistake with the last tool call.\n",
     "    messages = [\n",
-    "        AIMessage(content=\"\", additional_kwargs={\"tool_calls\": [tool_call]}),\n",
-    "        ToolMessage(tool_call_id=tool_call[\"id\"], content=str(exception.exception)),\n",
+    "        AIMessage(content=\"\", tool_calls=[exception.tool_call]),\n",
+    "        ToolMessage(\n",
+    "            tool_call_id=exception.tool_call[\"id\"], content=str(exception.exception)\n",
+    "        ),\n",
     "        HumanMessage(\n",
-    "            content=\"The last tool calls raised exceptions. Try calling the tools again with corrected arguments.\"\n",
+    "            content=\"The last tool call raised an exception. Try calling the tool again with corrected arguments. Do not repeat mistakes.\"\n",
     "        ),\n",
     "    ]\n",
     "    inputs[\"last_output\"] = messages\n",
@@ -351,14 +338,7 @@
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [(\"human\", \"{input}\"), MessagesPlaceholder(\"last_output\", optional=True)]\n",
     ")\n",
-    "chain = (\n",
-    "    prompt\n",
-    "    | model_with_tools\n",
-    "    | JsonOutputKeyToolsParser(\n",
-    "        key_name=\"complex_tool\", return_id=True, first_tool_only=True\n",
-    "    )\n",
-    "    | tool_custom_exception\n",
-    ")\n",
+    "chain = prompt | llm_with_tools | tool_custom_exception\n",
     "\n",
     "# If the initial chain call fails, we rerun it withe the exception passed in as a message.\n",
     "self_correcting_chain = chain.with_fallbacks(\n",
@@ -368,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "4c45f5bd-cbb4-47d5-b4b6-aec50673c750",
    "metadata": {},
    "outputs": [
@@ -378,7 +358,7 @@
        "10.5"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,15 +376,15 @@
    "id": "50d269a9-3cab-4a37-ba2f-805296453627",
    "metadata": {},
    "source": [
-    "And our chain succeeds! Looking at the [LangSmith trace](https://smith.langchain.com/public/b780b740-daf5-43aa-a217-6d4600aba41b/r), we can see that indeed our initial chain still fails, and it's only on retrying that the chain succeeds."
+    "And our chain succeeds! Looking at the [LangSmith trace](https://smith.langchain.com/public/c11e804c-e14f-4059-bd09-64766f999c14/r), we can see that indeed our initial chain still fails, and it's only on retrying that the chain succeeds."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "poetry-venv-2",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "poetry-venv-2"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py
index 2039422742..faca14a49e 100644
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -344,7 +344,6 @@ class ChatAnthropic(BaseChatModel):
     ) -> Iterator[ChatGenerationChunk]:
         params = self._format_params(messages=messages, stop=stop, **kwargs)
         if _tools_in_params(params):
-            warnings.warn("stream: Tool use is not yet supported in streaming mode.")
             result = self._generate(
                 messages, stop=stop, run_manager=run_manager, **kwargs
             )