diff --git a/docs/extras/integrations/chat/anthropic_functions.ipynb b/docs/extras/integrations/chat/anthropic_functions.ipynb
new file mode 100644
index 00000000000..9b636153860
--- /dev/null
+++ b/docs/extras/integrations/chat/anthropic_functions.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5125a1e3",
+   "metadata": {},
+   "source": [
+    "# Anthropic Functions\n",
+    "\n",
+    "This notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "378be79b",
+   "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_experimental.llms.anthropic_functions import AnthropicFunctions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65499965",
+   "metadata": {},
+   "source": [
+    "## Initialize Model\n",
+    "\n",
+    "You can initialize this wrapper the same way you'd initialize ChatAnthropic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e1d535f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AnthropicFunctions(model='claude-2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc9eaf4",
+   "metadata": {},
+   "source": [
+    "## Passing in functions\n",
+    "\n",
+    "You can now pass in functions in a similar way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0779c320",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "functions=[\n",
+    "    {\n",
+    "      \"name\": \"get_current_weather\",\n",
+    "      \"description\": \"Get the current weather in a given location\",\n",
+    "      \"parameters\": {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "          \"location\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"description\": \"The city and state, e.g. San Francisco, CA\"\n",
+    "          },\n",
+    "          \"unit\": {\n",
+    "            \"type\": \"string\",\n",
+    "            \"enum\": [\"celsius\", \"fahrenheit\"]\n",
+    "          }\n",
+    "        },\n",
+    "        \"required\": [\"location\"]\n",
+    "      }\n",
+    "    }\n",
+    "  ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ad75a933",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import HumanMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fc703085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = model.predict_messages(\n",
+    "    [HumanMessage(content=\"whats the weater in boston?\")], \n",
+    "    functions=functions\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "04d7936a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' ', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\", \"unit\": \"fahrenheit\"}'}}, example=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0072fdba",
+   "metadata": {},
+   "source": [
+    "## Using for extraction\n",
+    "\n",
+    "You can now use this for extraction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7af5c567",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_extraction_chain\n",
+    "schema = {\n",
+    "    \"properties\": {\n",
+    "        \"name\": {\"type\": \"string\"},\n",
+    "        \"height\": {\"type\": \"integer\"},\n",
+    "        \"hair_color\": {\"type\": \"string\"},\n",
+    "    },\n",
+    "    \"required\": [\"name\", \"height\"],\n",
+    "}\n",
+    "inp = \"\"\"\n",
+    "Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
+    "        \"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bd01082a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = create_extraction_chain(schema, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b5a23e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
+       " {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(inp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90ec959e",
+   "metadata": {},
+   "source": [
+    "## Using for tagging\n",
+    "\n",
+    "You can now use this for tagging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "03c1eb0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import create_tagging_chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "581c0ece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "schema = {\n",
+    "    \"properties\": {\n",
+    "        \"sentiment\": {\"type\": \"string\"},\n",
+    "        \"aggressiveness\": {\"type\": \"integer\"},\n",
+    "        \"language\": {\"type\": \"string\"},\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d9a8570e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = create_tagging_chain(schema, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cf37d679",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentiment': 'positive', 'aggressiveness': '0', 'language': 'english'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"this is really cool\")"
+   ]
+  }
+ ],
+ "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/libs/experimental/langchain_experimental/llms/anthropic_functions.py b/libs/experimental/langchain_experimental/llms/anthropic_functions.py
new file mode 100644
index 00000000000..e8c362aeb6d
--- /dev/null
+++ b/libs/experimental/langchain_experimental/llms/anthropic_functions.py
@@ -0,0 +1,206 @@
+import json
+from collections import defaultdict
+from html.parser import HTMLParser
+from typing import Any, DefaultDict, Dict, List, Optional
+
+from langchain.callbacks.manager import (
+    CallbackManagerForLLMRun,
+    Callbacks,
+)
+from langchain.chat_models.anthropic import ChatAnthropic
+from langchain.chat_models.base import BaseChatModel
+from langchain.schema import (
+    ChatGeneration,
+    ChatResult,
+    LLMResult,
+)
+from langchain.schema.messages import (
+    AIMessage,
+    BaseMessage,
+    SystemMessage,
+)
+from pydantic import root_validator
+
+prompt = """In addition to responding, you can use tools. \
+You have access to the following tools.
+
+{tools}
+
+In order to use a tool, you can use <tool></tool> to specify the name, \
+and the <tool_input></tool_input> tags to specify the parameters. \
+Each parameter should be passed in as <$param_name>$value</$param_name>, \
+Where $param_name is the name of the specific parameter, and $value \
+is the value for that parameter.
+
+You will then get back a response in the form <observation></observation>
+For example, if you have a tool called 'search' that accepts a single \
+parameter 'query' that could run a google search, in order to search \
+for the weather in SF you would respond:
+
+<tool>search</tool><tool_input><query>weather in SF</query></tool_input>
+<observation>64 degrees</observation>"""
+
+
+class TagParser(HTMLParser):
+    def __init__(self) -> None:
+        """A heavy-handed solution, but it's fast for prototyping.
+
+        Might be re-implemented later to restrict scope to the limited grammar, and
+        more efficiency.
+
+        Uses an HTML parser to parse a limited grammar that allows
+        for syntax of the form:
+
+            INPUT -> JUNK? VALUE*
+            JUNK -> JUNK_CHARACTER+
+            JUNK_CHARACTER -> whitespace | ,
+            VALUE -> <IDENTIFIER>DATA</IDENTIFIER> | OBJECT
+            OBJECT -> <IDENTIFIER>VALUE+</IDENTIFIER>
+            IDENTIFIER -> [a-Z][a-Z0-9_]*
+            DATA -> .*
+
+        Interprets the data to allow repetition of tags and recursion
+        to support representation of complex types.
+
+        ^ Just another approximately wrong grammar specification.
+        """
+        super().__init__()
+
+        self.parse_data: DefaultDict[str, List[Any]] = defaultdict(list)
+        self.stack: List[DefaultDict[str, List[str]]] = [self.parse_data]
+        self.success = True
+        self.depth = 0
+        self.data: Optional[str] = None
+
+    def handle_starttag(self, tag: str, attrs: Any) -> None:
+        """Hook when a new tag is encountered."""
+        self.depth += 1
+        self.stack.append(defaultdict(list))
+        self.data = None
+
+    def handle_endtag(self, tag: str) -> None:
+        """Hook when a tag is closed."""
+        self.depth -= 1
+        top_of_stack = dict(self.stack.pop(-1))  # Pop the dictionary we don't need it
+
+        # If a lead node
+        is_leaf = self.data is not None
+        # Annoying to type here, code is tested, hopefully OK
+        value = self.data if is_leaf else top_of_stack
+        # Difficult to type this correctly with mypy (maybe impossible?)
+        # Can be nested indefinitely, so requires self referencing type
+        self.stack[-1][tag].append(value)  # type: ignore
+        # Reset the data so we if we encounter a sequence of end tags, we
+        # don't confuse an outer end tag for belonging to a leaf node.
+        self.data = None
+
+    def handle_data(self, data: str) -> None:
+        """Hook when handling data."""
+        stripped_data = data.strip()
+        # The only data that's allowed is whitespace or a comma surrounded by whitespace
+        if self.depth == 0 and stripped_data not in (",", ""):
+            # If this is triggered the parse should be considered invalid.
+            self.success = False
+        if stripped_data:  # ignore whitespace-only strings
+            self.data = stripped_data
+
+
+def _destrip(tool_input: Any) -> Any:
+    if isinstance(tool_input, dict):
+        return {k: _destrip(v) for k, v in tool_input.items()}
+    elif isinstance(tool_input, list):
+        if isinstance(tool_input[0], str):
+            if len(tool_input) == 1:
+                return tool_input[0]
+            else:
+                raise ValueError
+        elif isinstance(tool_input[0], dict):
+            return [_destrip(v) for v in tool_input]
+        else:
+            raise ValueError
+    else:
+        raise ValueError
+
+
+class AnthropicFunctions(BaseChatModel):
+    model: ChatAnthropic
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        return {"model": ChatAnthropic(**values)}
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        forced = False
+        function_call = ""
+        if "functions" in kwargs:
+            content = prompt.format(tools=json.dumps(kwargs["functions"], indent=2))
+            system = SystemMessage(content=content)
+            messages = [system] + messages
+            del kwargs["functions"]
+            if stop is None:
+                stop = ["</tool_input>"]
+            else:
+                stop.append("</tool_input>")
+            if "function_call" in kwargs:
+                forced = True
+                function_call = kwargs["function_call"]["name"]
+                AIMessage(content=f"<tool>{function_call}</tool>")
+                del kwargs["function_call"]
+        else:
+            if "function_call" in kwargs:
+                raise ValueError(
+                    "if `function_call` provided, `functions` must also be"
+                )
+        response = self.model.predict_messages(
+            messages, stop=stop, callbacks=run_manager, **kwargs
+        )
+        completion = response.content
+        if forced:
+            tag_parser = TagParser()
+            tag_parser.feed(completion.strip() + "</tool_input>")
+            v1 = tag_parser.parse_data["tool_input"][0]
+            kwargs = {
+                "function_call": {
+                    "name": function_call,
+                    "arguments": json.dumps(_destrip(v1)),
+                }
+            }
+            message = AIMessage(content="", additional_kwargs=kwargs)
+            return ChatResult(generations=[ChatGeneration(message=message)])
+        elif "<tool>" in completion:
+            tag_parser = TagParser()
+            tag_parser.feed(completion.strip() + "</tool_input>")
+            msg = completion.split("<tool>")[0]
+            v1 = tag_parser.parse_data["tool_input"][0]
+            kwargs = {
+                "function_call": {
+                    "name": tag_parser.parse_data["tool"][0],
+                    "arguments": json.dumps(_destrip(v1)),
+                }
+            }
+            message = AIMessage(content=msg, additional_kwargs=kwargs)
+            return ChatResult(generations=[ChatGeneration(message=message)])
+        else:
+            return ChatResult(generations=[ChatGeneration(message=response)])
+
+    async def agenerate(
+        self,
+        messages: List[List[BaseMessage]],
+        stop: Optional[List[str]] = None,
+        callbacks: Callbacks = None,
+        *,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        raise NotImplementedError
+
+    @property
+    def _llm_type(self) -> str:
+        return "anthropic_functions"