diff --git a/litellm/tests/test_function_calling.py b/litellm/tests/test_function_calling.py
new file mode 100644
index 0000000000..9c6e7ca79e
--- /dev/null
+++ b/litellm/tests/test_function_calling.py
@@ -0,0 +1,97 @@
+import sys, os
+import traceback
+from dotenv import load_dotenv
+
+load_dotenv()
+import os, io
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+import litellm
+from litellm import embedding, completion, completion_cost, Timeout
+from litellm import RateLimitError
+litellm.num_retries = 3
+litellm.cache = None
+litellm.set_verbose=True
+import json
+
+
+# Example dummy function hard coded to return the same weather
+# In production, this could be your backend API or an external API
+def get_current_weather(location, unit="fahrenheit"):
+    """Get the current weather in a given location"""
+    if "tokyo" in location.lower():
+        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
+    elif "san francisco" in location.lower():
+        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
+    elif "paris" in location.lower():
+        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+def run_conversation():
+    # Step 1: send the conversation and available functions to the model
+    messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        },
+                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            },
+        }
+    ]
+    response = litellm.completion(
+        model="gpt-3.5-turbo-1106",
+        messages=messages,
+        tools=tools,
+        tool_choice="auto",  # auto is default, but we'll be explicit
+    )
+    print("Response\n", response)
+    response_message = response.choices[0].message
+    tool_calls = response_message.tool_calls
+    # Step 2: check if the model wanted to call a function
+    if tool_calls:
+        # Step 3: call the function
+        # Note: the JSON response may not always be valid; be sure to handle errors
+        available_functions = {
+            "get_current_weather": get_current_weather,
+        }  # only one function in this example, but you can have multiple
+        messages.append(response_message)  # extend conversation with assistant's reply
+        print("Response message\n", response_message)
+        # Step 4: send the info for each function call and function response to the model
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+            function_to_call = available_functions[function_name]
+            function_args = json.loads(tool_call.function.arguments)
+            function_response = function_to_call(
+                location=function_args.get("location"),
+                unit=function_args.get("unit"),
+            )
+            messages.append(
+                {
+                    "tool_call_id": tool_call.id,
+                    "role": "tool",
+                    "name": function_name,
+                    "content": function_response,
+                }
+            )  # extend conversation with function response
+        second_response = litellm.completion(
+            model="gpt-3.5-turbo-1106",
+            messages=messages,
+        )  # get a new response from the model where it can see the function response
+        return second_response
+print(run_conversation())
\ No newline at end of file