diff --git a/docs/modules/models/llms/integrations/textgen.ipynb b/docs/modules/models/llms/integrations/textgen.ipynb
new file mode 100644
index 00000000..490e3a4b
--- /dev/null
+++ b/docs/modules/models/llms/integrations/textgen.ipynb
@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TextGen\n",
+    "\n",
+    "[GitHub:oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with LLM models via the `text-generation-webui` API integration.\n",
+    "\n",
+    "Please ensure that you have `text-generation-webui` configured and an LLM installed.  Recommended installation via the [one-click installer appropriate](https://github.com/oobabooga/text-generation-webui#one-click-installers) for your OS.\n",
+    "\n",
+    "Once `text-generation-webui` is installed and confirmed working via the web interface, please enable the `api` option either through the web model configuration tab, or by adding the run-time arg `--api` to your start command."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set model_url and run the example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model_url = \"http://localhost:5000\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import langchain\n",
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.llms import TextGen\n",
+    "\n",
+    "langchain.debug = True\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm = TextGen(model_url=model_url)\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  }
+ ],
+ "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": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/langchain/llms/__init__.py b/langchain/llms/__init__.py
index 42d7c030..d7f0cda8 100644
--- a/langchain/llms/__init__.py
+++ b/langchain/llms/__init__.py
@@ -42,6 +42,7 @@ from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
 from langchain.llms.self_hosted import SelfHostedPipeline
 from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
 from langchain.llms.stochasticai import StochasticAI
+from langchain.llms.textgen import TextGen
 from langchain.llms.vertexai import VertexAI
 from langchain.llms.writer import Writer
 
@@ -64,6 +65,7 @@ __all__ = [
     "GooseAI",
     "GPT4All",
     "LlamaCpp",
+    "TextGen",
     "Modal",
     "MosaicML",
     "NLPCloud",
@@ -114,6 +116,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "huggingface_hub": HuggingFaceHub,
     "huggingface_endpoint": HuggingFaceEndpoint,
     "llamacpp": LlamaCpp,
+    "textgen": TextGen,
     "modal": Modal,
     "mosaic": MosaicML,
     "sagemaker_endpoint": SagemakerEndpoint,
diff --git a/langchain/llms/textgen.py b/langchain/llms/textgen.py
new file mode 100644
index 00000000..29fa7ce0
--- /dev/null
+++ b/langchain/llms/textgen.py
@@ -0,0 +1,211 @@
+"""Wrapper around text-generation-webui."""
+import logging
+from typing import Any, Dict, List, Optional
+
+import requests
+from pydantic import Field
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+
+logger = logging.getLogger(__name__)
+
+
+class TextGen(LLM):
+    """Wrapper around the text-generation-webui model.
+
+    To use, you should have the text-generation-webui installed, a model loaded,
+    and --api added as a command-line option.
+
+    Suggested installation, use one-click installer for your OS:
+    https://github.com/oobabooga/text-generation-webui#one-click-installers
+
+    Paremeters below taken from text-generation-webui api example:
+    https://github.com/oobabooga/text-generation-webui/blob/main/api-examples/api-example.py
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import TextGen
+            llm = TextGen(model_url="http://localhost:8500")
+    """
+
+    model_url: str
+    """The full URL to the textgen webui including http[s]://host:port """
+
+    max_new_tokens: Optional[int] = 250
+    """The maximum number of tokens to generate."""
+
+    do_sample: bool = Field(True, alias="do_sample")
+    """Do sample"""
+
+    temperature: Optional[float] = 1.3
+    """Primary factor to control randomness of outputs. 0 = deterministic
+    (only the most likely token is used). Higher value = more randomness."""
+
+    top_p: Optional[float] = 0.1
+    """If not set to 1, select tokens with probabilities adding up to less than this
+    number. Higher value = higher range of possible random results."""
+
+    typical_p: Optional[float] = 1
+    """If not set to 1, select only tokens that are at least this much more likely to
+    appear than random tokens, given the prior text."""
+
+    epsilon_cutoff: Optional[float] = 0  # In units of 1e-4
+    """Epsilon cutoff"""
+
+    eta_cutoff: Optional[float] = 0  # In units of 1e-4
+    """ETA cutoff"""
+
+    repetition_penalty: Optional[float] = 1.18
+    """Exponential penalty factor for repeating prior tokens. 1 means no penalty,
+    higher value = less repetition, lower value = more repetition."""
+
+    top_k: Optional[float] = 40
+    """Similar to top_p, but select instead only the top_k most likely tokens.
+    Higher value = higher range of possible random results."""
+
+    min_length: Optional[int] = 0
+    """Minimum generation length in tokens."""
+
+    no_repeat_ngram_size: Optional[int] = 0
+    """If not set to 0, specifies the length of token sets that are completely blocked
+    from repeating at all. Higher values = blocks larger phrases,
+    lower values = blocks words or letters from repeating.
+    Only 0 or high values are a good idea in most cases."""
+
+    num_beams: Optional[int] = 1
+    """Number of beams"""
+
+    penalty_alpha: Optional[float] = 0
+    """Penalty Alpha"""
+
+    length_penalty: Optional[float] = 1
+    """Length Penalty"""
+
+    early_stopping: bool = Field(False, alias="early_stopping")
+    """Early stopping"""
+
+    seed: int = Field(-1, alias="seed")
+    """Seed (-1 for random)"""
+
+    add_bos_token: bool = Field(True, alias="add_bos_token")
+    """Add the bos_token to the beginning of prompts.
+    Disabling this can make the replies more creative."""
+
+    truncation_length: Optional[int] = 2048
+    """Truncate the prompt up to this length. The leftmost tokens are removed if
+    the prompt exceeds this length. Most models require this to be at most 2048."""
+
+    ban_eos_token: bool = Field(False, alias="ban_eos_token")
+    """Ban the eos_token. Forces the model to never end the generation prematurely."""
+
+    skip_special_tokens: bool = Field(True, alias="skip_special_tokens")
+    """Skip special tokens. Some specific models need this unset."""
+
+    stopping_strings: Optional[List[str]] = []
+    """A list of strings to stop generation when encountered."""
+
+    streaming: bool = False
+    """Whether to stream the results, token by token (currently unimplemented)."""
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling textgen."""
+        return {
+            "max_new_tokens": self.max_new_tokens,
+            "do_sample": self.do_sample,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "typical_p": self.typical_p,
+            "epsilon_cutoff": self.epsilon_cutoff,
+            "eta_cutoff": self.eta_cutoff,
+            "repetition_penalty": self.repetition_penalty,
+            "top_k": self.top_k,
+            "min_length": self.min_length,
+            "no_repeat_ngram_size": self.no_repeat_ngram_size,
+            "num_beams": self.num_beams,
+            "penalty_alpha": self.penalty_alpha,
+            "length_penalty": self.length_penalty,
+            "early_stopping": self.early_stopping,
+            "seed": self.seed,
+            "add_bos_token": self.add_bos_token,
+            "truncation_length": self.truncation_length,
+            "ban_eos_token": self.ban_eos_token,
+            "skip_special_tokens": self.skip_special_tokens,
+            "stopping_strings": self.stopping_strings,
+        }
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model_url": self.model_url}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "textgen"
+
+    def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
+        """
+        Performs sanity check, preparing paramaters in format needed by textgen.
+
+        Args:
+            stop (Optional[List[str]]): List of stop sequences for textgen.
+
+        Returns:
+            Dictionary containing the combined parameters.
+        """
+
+        # Raise error if stop sequences are in both input and default params
+        # if self.stop and stop is not None:
+        if self.stopping_strings and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+
+        params = self._default_params
+
+        # then sets it as configured, or default to an empty list:
+        params["stop"] = self.stopping_strings or stop or []
+
+        return params
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call the textgen web API and return the output.
+
+        Args:
+            prompt: The prompt to use for generation.
+            stop: A list of strings to stop generation when encountered.
+
+        Returns:
+            The generated text.
+
+        Example:
+            .. code-block:: python
+
+                from langchain.llms import TextGen
+                llm = TextGen(model_url="http://localhost:5000")
+                llm("Write a story about llamas.")
+        """
+        if self.streaming:
+            raise ValueError("`streaming` option currently unsupported.")
+
+        url = f"{self.model_url}/api/v1/generate"
+        params = self._get_parameters(stop)
+        request = params.copy()
+        request["prompt"] = prompt
+        response = requests.post(url, json=request)
+
+        if response.status_code == 200:
+            result = response.json()["results"][0]["text"]
+            print(prompt + result)
+        else:
+            print(f"ERROR: response: {response}")
+            result = ""
+
+        return result