diff --git a/docs/modules/indexes/document_loaders/examples/html.ipynb b/docs/modules/indexes/document_loaders/examples/html.ipynb
index 445ec597..9646dec6 100644
--- a/docs/modules/indexes/document_loaders/examples/html.ipynb
+++ b/docs/modules/indexes/document_loaders/examples/html.ipynb
@@ -90,7 +90,6 @@
    "execution_count": 2,
    "id": "4be99e6c",
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     },
@@ -131,7 +130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/modules/indexes/document_loaders/examples/tomarkdown.ipynb b/docs/modules/indexes/document_loaders/examples/tomarkdown.ipynb
new file mode 100644
index 00000000..da9d262e
--- /dev/null
+++ b/docs/modules/indexes/document_loaders/examples/tomarkdown.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "77b854df",
+   "metadata": {},
+   "source": [
+    "# 2Markdown\n",
+    "\n",
+    "Uses [2markdown](https://2markdown.com/) to convert any webpage into a standard markdown file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "497736aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You will need to get your own API key\n",
+    "\n",
+    "api_key = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "009e0036",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import ToMarkdownLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "910fb6ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = ToMarkdownLoader.from_api_key(url=\"https://python.langchain.com/en/latest/\", api_key=api_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ac8db139",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "706304e9",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## Contents\n",
+      "\n",
+      "- [Getting Started](#getting-started)\n",
+      "- [Modules](#modules)\n",
+      "- [Use Cases](#use-cases)\n",
+      "- [Reference Docs](#reference-docs)\n",
+      "- [LangChain Ecosystem](#langchain-ecosystem)\n",
+      "- [Additional Resources](#additional-resources)\n",
+      "\n",
+      "## Welcome to LangChain [\\#](\\#welcome-to-langchain \"Permalink to this headline\")\n",
+      "\n",
+      "**LangChain** is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:\n",
+      "\n",
+      "1. _Data-aware_: connect a language model to other sources of data\n",
+      "\n",
+      "2. _Agentic_: allow a language model to interact with its environment\n",
+      "\n",
+      "\n",
+      "The LangChain framework is designed around these principles.\n",
+      "\n",
+      "This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see [here](https://docs.langchain.com/docs/). For the JavaScript documentation, see [here](https://js.langchain.com/docs/).\n",
+      "\n",
+      "## Getting Started [\\#](\\#getting-started \"Permalink to this headline\")\n",
+      "\n",
+      "How to get started using LangChain to create an Language Model application.\n",
+      "\n",
+      "- [Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html)\n",
+      "\n",
+      "\n",
+      "Concepts and terminology.\n",
+      "\n",
+      "- [Concepts and terminology](https://python.langchain.com/en/latest/getting_started/concepts.html)\n",
+      "\n",
+      "\n",
+      "Tutorials created by community experts and presented on YouTube.\n",
+      "\n",
+      "- [Tutorials](https://python.langchain.com/en/latest/getting_started/tutorials.html)\n",
+      "\n",
+      "\n",
+      "## Modules [\\#](\\#modules \"Permalink to this headline\")\n",
+      "\n",
+      "These modules are the core abstractions which we view as the building blocks of any LLM-powered application.\n",
+      "\n",
+      "For each module LangChain provides standard, extendable interfaces. LanghChain also provides external integrations and even end-to-end implementations for off-the-shelf use.\n",
+      "\n",
+      "The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.\n",
+      "\n",
+      "The modules are (from least to most complex):\n",
+      "\n",
+      "- [Models](https://python.langchain.com/en/latest/modules/models.html): Supported model types and integrations.\n",
+      "\n",
+      "- [Prompts](https://python.langchain.com/en/latest/modules/prompts.html): Prompt management, optimization, and serialization.\n",
+      "\n",
+      "- [Memory](https://python.langchain.com/en/latest/modules/memory.html): Memory refers to state that is persisted between calls of a chain/agent.\n",
+      "\n",
+      "- [Indexes](https://python.langchain.com/en/latest/modules/indexes.html): Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.\n",
+      "\n",
+      "- [Chains](https://python.langchain.com/en/latest/modules/chains.html): Chains are structured sequences of calls (to an LLM or to a different utility).\n",
+      "\n",
+      "- [Agents](https://python.langchain.com/en/latest/modules/agents.html): An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.\n",
+      "\n",
+      "- [Callbacks](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html): Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.\n",
+      "\n",
+      "\n",
+      "## Use Cases [\\#](\\#use-cases \"Permalink to this headline\")\n",
+      "\n",
+      "Best practices and built-in implementations for common LangChain use cases:\n",
+      "\n",
+      "- [Autonomous Agents](https://python.langchain.com/en/latest/use_cases/autonomous_agents.html): Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.\n",
+      "\n",
+      "- [Agent Simulations](https://python.langchain.com/en/latest/use_cases/agent_simulations.html): Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.\n",
+      "\n",
+      "- [Personal Assistants](https://python.langchain.com/en/latest/use_cases/personal_assistants.html): One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.\n",
+      "\n",
+      "- [Question Answering](https://python.langchain.com/en/latest/use_cases/question_answering.html): Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.\n",
+      "\n",
+      "- [Chatbots](https://python.langchain.com/en/latest/use_cases/chatbots.html): Language models love to chat, making this a very natural use of them.\n",
+      "\n",
+      "- [Querying Tabular Data](https://python.langchain.com/en/latest/use_cases/tabular.html): Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).\n",
+      "\n",
+      "- [Code Understanding](https://python.langchain.com/en/latest/use_cases/code.html): Recommended reading if you want to use language models to analyze code.\n",
+      "\n",
+      "- [Interacting with APIs](https://python.langchain.com/en/latest/use_cases/apis.html): Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.\n",
+      "\n",
+      "- [Extraction](https://python.langchain.com/en/latest/use_cases/extraction.html): Extract structured information from text.\n",
+      "\n",
+      "- [Summarization](https://python.langchain.com/en/latest/use_cases/summarization.html): Compressing longer documents. A type of Data-Augmented Generation.\n",
+      "\n",
+      "- [Evaluation](https://python.langchain.com/en/latest/use_cases/evaluation.html): Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.\n",
+      "\n",
+      "\n",
+      "## Reference Docs [\\#](\\#reference-docs \"Permalink to this headline\")\n",
+      "\n",
+      "Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n",
+      "\n",
+      "- [Reference Documentation](https://python.langchain.com/en/latest/reference.html)\n",
+      "\n",
+      "\n",
+      "## LangChain Ecosystem [\\#](\\#langchain-ecosystem \"Permalink to this headline\")\n",
+      "\n",
+      "Guides for how other companies/products can be used with LangChain.\n",
+      "\n",
+      "- [LangChain Ecosystem](https://python.langchain.com/en/latest/ecosystem.html)\n",
+      "\n",
+      "\n",
+      "## Additional Resources [\\#](\\#additional-resources \"Permalink to this headline\")\n",
+      "\n",
+      "Additional resources we think may be useful as you develop your application!\n",
+      "\n",
+      "- [LangChainHub](https://github.com/hwchase17/langchain-hub): The LangChainHub is a place to share and explore other prompts, chains, and agents.\n",
+      "\n",
+      "- [Gallery](https://python.langchain.com/en/latest/additional_resources/gallery.html): A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\n",
+      "\n",
+      "- [Deployments](https://python.langchain.com/en/latest/additional_resources/deployments.html): A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\n",
+      "\n",
+      "- [Tracing](https://python.langchain.com/en/latest/additional_resources/tracing.html): A guide on using tracing in LangChain to visualize the execution of chains and agents.\n",
+      "\n",
+      "- [Model Laboratory](https://python.langchain.com/en/latest/additional_resources/model_laboratory.html): Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\n",
+      "\n",
+      "- [Discord](https://discord.gg/6adMQxSpJS): Join us on our Discord to discuss all things LangChain!\n",
+      "\n",
+      "- [YouTube](https://python.langchain.com/en/latest/additional_resources/youtube.html): A collection of the LangChain tutorials and videos.\n",
+      "\n",
+      "- [Production Support](https://forms.gle/57d8AmXBYp8PP8tZA): As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dde17e7",
+   "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/langchain/document_loaders/__init__.py b/langchain/document_loaders/__init__.py
index 271afda2..a456f2c8 100644
--- a/langchain/document_loaders/__init__.py
+++ b/langchain/document_loaders/__init__.py
@@ -86,6 +86,7 @@ from langchain.document_loaders.telegram import (
     TelegramChatFileLoader,
 )
 from langchain.document_loaders.text import TextLoader
+from langchain.document_loaders.tomarkdown import ToMarkdownLoader
 from langchain.document_loaders.toml import TomlLoader
 from langchain.document_loaders.twitter import TwitterTweetLoader
 from langchain.document_loaders.unstructured import (
@@ -213,4 +214,5 @@ __all__ = [
     "WikipediaLoader",
     "YoutubeLoader",
     "TelegramChatLoader",
+    "ToMarkdownLoader",
 ]
diff --git a/langchain/document_loaders/tomarkdown.py b/langchain/document_loaders/tomarkdown.py
new file mode 100644
index 00000000..a3fbf6f7
--- /dev/null
+++ b/langchain/document_loaders/tomarkdown.py
@@ -0,0 +1,35 @@
+"""Loader that loads HTML to markdown using 2markdown."""
+from __future__ import annotations
+
+from typing import Iterator, List
+
+import requests
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+
+class ToMarkdownLoader(BaseLoader):
+    """Loader that loads HTML to markdown using 2markdown."""
+
+    def __init__(self, url: str, api_key: str):
+        """Initialize with url and api key."""
+        self.url = url
+        self.api_key = api_key
+
+    def lazy_load(
+        self,
+    ) -> Iterator[Document]:
+        """Lazily load the file."""
+        response = requests.post(
+            "https://2markdown.com/api/2md",
+            headers={"X-Api-Key": self.api_key},
+            json={"url": self.url},
+        )
+        text = response.json()["article"]
+        metadata = {"source": self.url}
+        yield Document(page_content=text, metadata=metadata)
+
+    def load(self) -> List[Document]:
+        """Load file."""
+        return list(self.lazy_load())