diff --git a/examples/foundational/15-function-calling.py b/examples/foundational/15-function-calling.py
new file mode 100644
index 000000000..94c3d939c
--- /dev/null
+++ b/examples/foundational/15-function-calling.py
@@ -0,0 +1,195 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import json
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame, Frame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
+from pipecat.services.openai import OpenAILLMContextFrame, OpenAILLMContext
+from pipecat.processors.logger import FrameLogger
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+from openai.types.chat import (
+    ChatCompletionSystemMessageParam,
+    ChatCompletionFunctionMessageParam,
+    ChatCompletionToolParam,
+    ChatCompletionUserMessageParam,
+)
+from pipecat.frames.frames import (
+    LLMFullResponseStartFrame,
+    LLMFullResponseEndFrame,
+    LLMResponseEndFrame,
+    LLMResponseStartFrame,
+    LLMFunctionCallFrame,
+    LLMFunctionStartFrame,
+    TextFrame
+)
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class FunctionCaller(FrameProcessor):
+    def __init__(self, context):
+        self._context = context
+        super().__init__()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        # When we receive function call frames, we need to ask the LLM to run a completion
+        # again so it actually talks to the user
+        if isinstance(frame, LLMFunctionCallFrame):
+            tool_call = ChatCompletionFunctionMessageParam({
+                "role": "assistant",
+                "tool_calls": [
+                    {
+                        "id": frame.tool_call_id,
+                        "function": {
+                            "arguments": frame.arguments,
+                            "name": frame.function_name
+                        },
+                        "type": "function"
+                    }
+                ]
+
+            })
+            self._context.add_message(tool_call)
+
+            # This is where you'd actually call the function
+            weather_result = {
+                "city": "San Francisco, CA",
+                "conditions": "Sunny and beautiful",
+                "temperature": "75 degrees Fahrenheit"
+            }
+            print(f"weather_result: {weather_result}")
+            result = ChatCompletionToolParam({
+                "tool_call_id": frame.tool_call_id,
+                "role": "tool",
+                "content": json.dumps(weather_result)
+            })
+            print(f"result: {result}")
+            try:
+                self._context.add_message(result)
+            except Exception as e:
+                print(f"got exception: {e}")
+            print(f"context now includes: {self._context.messages}")
+            await self.push_frame(OpenAILLMContextFrame(self._context), FrameDirection.UPSTREAM)
+        else:
+            print(f"!!! Got a frame I'm forwarding: {frame}")
+            await self.push_frame(frame)
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer()
+            )
+        )
+
+        tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4-turbo-preview")
+
+        fl_in = FrameLogger("Inner")
+        fl_out = FrameLogger("Outer")
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": [
+                                    "celsius",
+                                    "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": [
+                            "location",
+                            "format"],
+                    },
+                })]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        tma_in = LLMUserContextAggregator(context)
+        tma_out = LLMAssistantContextAggregator(context)
+        caller = FunctionCaller(context)
+        pipeline = Pipeline([
+            fl_in,
+            transport.input(),
+            tma_in,
+            llm,
+            caller,
+            fl_out,
+            tts,
+            transport.output(),
+            tma_out
+        ])
+
+        task = PipelineTask(pipeline)
+
+        @ transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await tts.say("Hi! Ask me about the weather in San Francisco.")
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))
diff --git a/tests/integration/integration_openai_llm.py b/tests/integration/integration_openai_llm.py
index bc011c3eb..3f4a1105d 100644
--- a/tests/integration/integration_openai_llm.py
+++ b/tests/integration/integration_openai_llm.py
@@ -98,7 +98,6 @@ async def test_chat():
             content="Please tell the world hello.", name="system", role="system")
         context.add_message(message)
         frame = OpenAILLMContextFrame(context)
-        print(f"starting chattest")
         await llm.process_frame(frame, FrameDirection.DOWNSTREAM)
 
     async def run_tests():