diff --git a/docs/modules/agents/getting_started.ipynb b/docs/modules/agents/getting_started.ipynb
index 1279ad83..70bb6495 100644
--- a/docs/modules/agents/getting_started.ipynb
+++ b/docs/modules/agents/getting_started.ipynb
@@ -166,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/modules/llms/examples/fake_llm.ipynb b/docs/modules/llms/examples/fake_llm.ipynb
new file mode 100644
index 00000000..c58d2546
--- /dev/null
+++ b/docs/modules/llms/examples/fake_llm.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "052dfe58",
+   "metadata": {},
+   "source": [
+    "# Fake LLM\n",
+    "We expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.\n",
+    "\n",
+    "In this notebook we go over how to use this.\n",
+    "\n",
+    "We start this with using the FakeLLM in an agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ef97ac4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms.fake import FakeListLLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9a0a160f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b272258c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = load_tools([\"python_repl\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "94096c4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "responses=[\n",
+    "    \"Action: Python REPL\\nAction Input: print(2 + 2)\",\n",
+    "    \"Final Answer: 4\"\n",
+    "]\n",
+    "llm = FakeListLLM(responses=responses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "da226d02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "44c13426",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: Python REPL\n",
+      "Action Input: print(2 + 2)\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m4\n",
+      "\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3mFinal Answer: 4\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'4'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent.run(\"whats 2 + 2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "814c2858",
+   "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/modules/llms/generic_how_to.rst b/docs/modules/llms/generic_how_to.rst
index 90d1a837..c1146aa2 100644
--- a/docs/modules/llms/generic_how_to.rst
+++ b/docs/modules/llms/generic_how_to.rst
@@ -11,6 +11,8 @@ The examples here all address certain "how-to" guides for working with LLMs.
 
 `Token Usage Tracking <./examples/token_usage_tracking.html>`_: How to track the token usage of various chains/agents/LLM calls.
 
+`Fake LLM <./examples/fake_llm.html>`_: How to create and use a fake LLM for testing and debugging purposes.
+
 
 .. toctree::
    :maxdepth: 1
diff --git a/langchain/llms/fake.py b/langchain/llms/fake.py
new file mode 100644
index 00000000..96f766f9
--- /dev/null
+++ b/langchain/llms/fake.py
@@ -0,0 +1,28 @@
+"""Fake LLM wrapper for testing purposes."""
+from typing import Any, List, Mapping, Optional
+
+from pydantic import BaseModel
+
+from langchain.llms.base import LLM
+
+
+class FakeListLLM(LLM, BaseModel):
+    """Fake LLM wrapper for testing purposes."""
+
+    responses: List
+    i: int = 0
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "fake-list"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        """First try to lookup in queries, else return 'foo' or 'bar'."""
+        response = self.responses[self.i]
+        self.i += 1
+        return response
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {}