diff --git a/langchain/llms/huggingface_endpoint.py b/langchain/llms/huggingface_endpoint.py
new file mode 100644
index 0000000000..dcc5e76155
--- /dev/null
+++ b/langchain/llms/huggingface_endpoint.py
@@ -0,0 +1,140 @@
+"""Wrapper around HuggingFace APIs."""
+from typing import Any, Dict, List, Mapping, Optional
+
+import requests
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+from langchain.utils import get_from_dict_or_env
+
+VALID_TASKS = ("text2text-generation", "text-generation")
+
+
+class HuggingFaceEndpoint(LLM, BaseModel):
+    """Wrapper around HuggingFaceHub Inference Endpoints.
+
+    To use, you should have the ``huggingface_hub`` python package installed, and the
+    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
+    it as a named parameter to the constructor.
+
+    Only supports `text-generation` and `text2text-generation` for now.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import HuggingFaceEndpoint
+            endpoint_url = (
+                "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
+            )
+            hf = HuggingFaceEndpoint(
+                endpoint_url=endpoint_url,
+                huggingfacehub_api_token="my-api-key"
+            )
+    """
+
+    endpoint_url: str = ""
+    """Endpoint URL to use."""
+    task: Optional[str] = None
+    """Task to call the model with. Should be a task that returns `generated_text`."""
+    model_kwargs: Optional[dict] = None
+    """Key word arguments to pass to the model."""
+
+    huggingfacehub_api_token: Optional[str] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        huggingfacehub_api_token = get_from_dict_or_env(
+            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
+        )
+        try:
+            from huggingface_hub.hf_api import HfApi
+
+            try:
+                HfApi(
+                    endpoint="https://huggingface.co",  # Can be a Private Hub endpoint.
+                    token=huggingfacehub_api_token,
+                ).whoami()
+            except Exception as e:
+                raise ValueError(
+                    "Could not authenticate with huggingface_hub. "
+                    "Please check your API token."
+                ) from e
+
+        except ImportError:
+            raise ValueError(
+                "Could not import huggingface_hub python package. "
+                "Please it install it with `pip install huggingface_hub`."
+            )
+        return values
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        _model_kwargs = self.model_kwargs or {}
+        return {
+            **{"endpoint_url": self.endpoint_url, "task": self.task},
+            **{"model_kwargs": _model_kwargs},
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "huggingface_endpoint"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        """Call out to HuggingFace Hub's inference endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = hf("Tell me a joke.")
+        """
+        _model_kwargs = self.model_kwargs or {}
+
+        # payload samples
+        parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
+
+        # HTTP headers for authorization
+        headers = {
+            "Authorization": f"Bearer {self.huggingfacehub_api_token}",
+            "Content-Type": "application/json",
+        }
+
+        # send request
+        try:
+            response = requests.post(
+                self.endpoint_url, headers=headers, json=parameter_payload
+            )
+        except requests.exceptions.RequestException as e:  # This is the correct syntax
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+        if self.task == "text-generation":
+            # Text generation return includes the starter text.
+            generated_text = response.json()
+            text = generated_text[0]["generated_text"][len(prompt) :]
+        elif self.task == "text2text-generation":
+            generated_text = response.json()
+            text = generated_text[0]["generated_text"]
+        else:
+            raise ValueError(
+                f"Got invalid task {self.task}, "
+                f"currently only {VALID_TASKS} are supported"
+            )
+        if stop is not None:
+            # This is a bit hacky, but I can't figure out a better way to enforce
+            # stop tokens when making calls to huggingface_hub.
+            text = enforce_stop_tokens(text, stop)
+        return text
diff --git a/tests/integration_tests/llms/test_huggingface_endpoint.py b/tests/integration_tests/llms/test_huggingface_endpoint.py
new file mode 100644
index 0000000000..61639669d3
--- /dev/null
+++ b/tests/integration_tests/llms/test_huggingface_endpoint.py
@@ -0,0 +1,50 @@
+"""Test HuggingFace API wrapper."""
+
+import unittest
+from pathlib import Path
+
+import pytest
+
+from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
+from langchain.llms.loading import load_llm
+from tests.integration_tests.llms.utils import assert_llm_equality
+
+
+@unittest.skip(
+    "This test requires an inference endpoint. Tested with Hugging Face endpoints"
+)
+def test_huggingface_endpoint_text_generation() -> None:
+    """Test valid call to HuggingFace text generation model."""
+    llm = HuggingFaceEndpoint(
+        endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10}
+    )
+    output = llm("Say foo:")
+    print(output)
+    assert isinstance(output, str)
+
+
+@unittest.skip(
+    "This test requires an inference endpoint. Tested with Hugging Face endpoints"
+)
+def test_huggingface_endpoint_text2text_generation() -> None:
+    """Test valid call to HuggingFace text2text model."""
+    llm = HuggingFaceEndpoint(endpoint_url="", task="text2text-generation")
+    output = llm("The capital of New York is")
+    assert output == "Albany"
+
+
+def test_huggingface_endpoint_call_error() -> None:
+    """Test valid call to HuggingFace that errors."""
+    llm = HuggingFaceEndpoint(model_kwargs={"max_new_tokens": -1})
+    with pytest.raises(ValueError):
+        llm("Say foo:")
+
+
+def test_saving_loading_endpoint_llm(tmp_path: Path) -> None:
+    """Test saving/loading an HuggingFaceHub LLM."""
+    llm = HuggingFaceEndpoint(
+        endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10}
+    )
+    llm.save(file_path=tmp_path / "hf.yaml")
+    loaded_llm = load_llm(tmp_path / "hf.yaml")
+    assert_llm_equality(llm, loaded_llm)