diff --git a/dsp/modules/google.py b/dsp/modules/google.py
new file mode 100644
index 000000000..581c98414
--- /dev/null
+++ b/dsp/modules/google.py
@@ -0,0 +1,104 @@
+import math
+from typing import Any, Optional
+import backoff
+
+from dsp.modules.lm import LM
+
+try:
+    import google.generativeai as genai
+except ImportError:
+    google_api_error = Exception
+    print("Not loading Google because it is not installed.")
+
+def backoff_hdlr(details):
+    """Handler from https://pypi.org/project/backoff/"""
+    print(
+        "Backing off {wait:0.1f} seconds after {tries} tries "
+        "calling function {target} with kwargs "
+        "{kwargs}".format(**details)
+    )
+
+
+def giveup_hdlr(details):
+    """wrapper function that decides when to give up on retry"""
+    if "rate limits" in details.message:
+        return False
+    return True
+
+
+class Google(LM):
+    """Wrapper around Google's API.
+
+    Currently supported models include `gemini-pro-1.0`.
+    """
+
+    def __init__(
+        self,
+        model: str = "gemini-pro-1.0",
+        api_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Parameters
+        ----------
+        model : str
+            Which pre-trained model from Google to use?
+            Choices are [`gemini-pro-1.0`]
+        api_key : str
+            The API key for Google.
+            It can be obtained from https://cloud.google.com/generative-ai-studio
+        **kwargs: dict
+            Additional arguments to pass to the API provider.
+        """
+        super().__init__(model)
+        self.google = genai.configure(api_key=self.api_key)
+        self.provider = "google"
+        self.kwargs = {
+            "model_name": model,
+            "temperature": 0.0 if "temperature" not in kwargs else kwargs["temperature"],
+            "max_output_tokens": 2048,
+            "top_p": 1,
+            "top_k": 1,
+            **kwargs
+        }
+
+        self.history: list[dict[str, Any]] = []
+
+    def basic_request(self, prompt: str, **kwargs):
+        raw_kwargs = kwargs
+        kwargs = {
+            **self.kwargs,
+            "prompt": prompt,
+            **kwargs,
+        }
+        response = self.co.generate(**kwargs)
+
+        history = {
+            "prompt": prompt,
+            "response": response,
+            "kwargs": kwargs,
+            "raw_kwargs": raw_kwargs,
+        }
+        self.history.append(history)
+
+        return response
+
+    @backoff.on_exception(
+        backoff.expo,
+        (google_api_error),
+        max_time=1000,
+        on_backoff=backoff_hdlr,
+        giveup=giveup_hdlr,
+    )
+    def request(self, prompt: str, **kwargs):
+        """Handles retrieval of completions from Google whilst handling API errors"""
+        return self.basic_request(prompt, **kwargs)
+
+    def __call__(
+        self,
+        prompt: str,
+        only_completed: bool = True,
+        return_sorted: bool = False,
+        **kwargs
+    ):
+        return self.request(prompt, **kwargs)