diff --git a/docs/extras/modules/agents/agent_types/anthropic_agent.ipynb b/docs/extras/modules/agents/agent_types/anthropic_agent.ipynb
deleted file mode 100644
index ab5575c247f..00000000000
--- a/docs/extras/modules/agents/agent_types/anthropic_agent.ipynb
+++ /dev/null
@@ -1,274 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9926203f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
-    "os.environ[\"LANGCHAIN_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "45bc4149",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_instructions = \"\"\"You are a helpful assistant. Help the user answer any questions.\n",
-    "\n",
-    "You have access to the following tools:\n",
-    "\n",
-    "{tools}\n",
-    "\n",
-    "In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. \\\n",
-    "You will then get back a response in the form <observation></observation>\n",
-    "For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:\n",
-    "\n",
-    "<tool>search</tool><tool_input>weather in SF</tool_input>\n",
-    "<observation>64 degrees</observation>\n",
-    "\n",
-    "When you are done, respond with a final answer between <final_answer></final_answer>. For example:\n",
-    "\n",
-    "<final_answer>The weather in SF is 64 degrees</final_answer>\n",
-    "\n",
-    "Begin!\n",
-    "\n",
-    "Question: {question}\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4da4c0d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatAnthropic\n",
-    "from langchain.prompts import ChatPromptTemplate, AIMessagePromptTemplate\n",
-    "from langchain.agents import tool"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b81e9120",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatAnthropic(model=\"claude-2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5271f612",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt_template = ChatPromptTemplate.from_template(agent_instructions) + AIMessagePromptTemplate.from_template(\"{intermediate_steps}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "83780d81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = prompt_template | model.bind(stop=[\"</tool_input>\", \"</final_answer>\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c091d0e1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@tool\n",
-    "def search(query: str) -> str:\n",
-    "    \"\"\"Search things about current events.\"\"\"\n",
-    "    return \"32 degrees\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1e81b05d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tool_list = [search]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5f0d986f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import Tool, AgentExecutor, BaseSingleActionAgent\n",
-    "from typing import List, Tuple, Any, Union\n",
-    "from langchain.schema import AgentAction, AgentFinish\n",
-    "\n",
-    "\n",
-    "class AnthropicAgent(BaseSingleActionAgent):\n",
-    "    \n",
-    "    tools: List[Tool]\n",
-    "    chain: Any\n",
-    "\n",
-    "    @property\n",
-    "    def input_keys(self):\n",
-    "        return [\"input\"]\n",
-    "\n",
-    "    def plan(\n",
-    "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
-    "    ) -> Union[AgentAction, AgentFinish]:\n",
-    "        \"\"\"Given input, decided what to do.\n",
-    "\n",
-    "        Args:\n",
-    "            intermediate_steps: Steps the LLM has taken to date,\n",
-    "                along with observations\n",
-    "            **kwargs: User inputs.\n",
-    "\n",
-    "        Returns:\n",
-    "            Action specifying what tool to use.\n",
-    "        \"\"\"\n",
-    "        log = \"\"\n",
-    "        for action, observation in intermediate_steps:\n",
-    "            log += f\"<tool>{action.tool}</tool><tool_input>{action.tool_input}</tool_input><observation>{observation}</observation>\"\n",
-    "        tools = \"\"\n",
-    "        for tool in self.tools:\n",
-    "            tools += f\"{tool.name}: {tool.description}\\n\"\n",
-    "        response = self.chain.invoke({\"intermediate_steps\": log, \"tools\": tools, \"question\": kwargs[\"input\"]})\n",
-    "        if \"</tool>\" in response.content:\n",
-    "            t, ti = response.content.split(\"</tool>\")\n",
-    "            _t = t.split(\"<tool>\")[1]\n",
-    "            _ti = ti.split(\"<tool_input>\")[1]\n",
-    "            return AgentAction(tool=_t, tool_input=_ti, log=response.content)\n",
-    "        elif \"<final_answer>\" in response.content:\n",
-    "            t, ti = response.content.split(\"<final_answer>\")\n",
-    "            return AgentFinish(return_values={\"output\": ti}, log=response.content)\n",
-    "        else:\n",
-    "            raise ValueError\n",
-    "\n",
-    "    async def aplan(\n",
-    "        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
-    "    ) -> Union[AgentAction, AgentFinish]:\n",
-    "        \"\"\"Given input, decided what to do.\n",
-    "\n",
-    "        Args:\n",
-    "            intermediate_steps: Steps the LLM has taken to date,\n",
-    "                along with observations\n",
-    "            **kwargs: User inputs.\n",
-    "\n",
-    "        Returns:\n",
-    "            Action specifying what tool to use.\n",
-    "        \"\"\"\n",
-    "        raise ValueError"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "315361c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent = AnthropicAgent(tools=tool_list, chain=chain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bca6096f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "71b872b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
-      "<tool_input>weather in new york\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
-      "\n",
-      "<final_answer>The weather in New York is 32 degrees\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The weather in New York is 32 degrees'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent_executor.run(\"whats the weather in New york?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cca87246",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/extras/modules/agents/agent_types/xml_agent.ipynb b/docs/extras/modules/agents/agent_types/xml_agent.ipynb
new file mode 100644
index 00000000000..ed183d04678
--- /dev/null
+++ b/docs/extras/modules/agents/agent_types/xml_agent.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3c284df8",
+   "metadata": {},
+   "source": [
+    "# XML Agent\n",
+    "\n",
+    "Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML. This goes over how to use an agent that uses XML when prompting. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f9d2ead2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import XMLAgent, tool, AgentExecutor\n",
+    "from langchain.chat_models import ChatAnthropic\n",
+    "from langchain.chains import LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ebadf04f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ChatAnthropic(model=\"claude-2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6ce9f9a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tool\n",
+    "def search(query: str) -> str:\n",
+    "    \"\"\"Search things about current events.\"\"\"\n",
+    "    return \"32 degrees\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c589944e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_list = [search]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2d8454be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = LLMChain(\n",
+    "    llm=model,\n",
+    "    prompt=XMLAgent.get_default_prompt(),\n",
+    "    output_parser=XMLAgent.get_default_output_parser()\n",
+    ")\n",
+    "agent = XMLAgent(tools=tool_list, llm_chain=chain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bca6096f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "71b872b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
+      "<tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "<final_answer>The weather in New York is 32 degrees\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'The weather in New York is 32 degrees'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent_executor.run(\"whats the weather in New york?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cca87246",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}