diff --git a/examples/fast-bot-metrics/classic-pipeline.py b/examples/fast-bot-metrics/classic-pipeline.py
new file mode 100644
index 000000000..ff5540075
--- /dev/null
+++ b/examples/fast-bot-metrics/classic-pipeline.py
@@ -0,0 +1,168 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from loguru import logger
+from runner import configure
+import asyncio
+import aiohttp
+import os
+import sys
+from typing import List
+
+
+from pipecat.vad.vad_analyzer import VADParams
+from pipecat.vad.silero import SileroVADAnalyzer
+from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
+from pipecat.services.openai import OpenAILLMService, OpenAILLMContext
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.processors.logger import FrameLogger
+from pipecat.frames.frames import LLMMessagesFrame
+
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator
+)
+
+
+from fastbothelpers import (
+    GreedyLLMAggregator,
+    ClearableDeepgramTTSService,
+    VADGate,
+    AudioVolumeTimer,
+    TranscriptionTimingLogger
+)
+
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+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=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.200)),
+                vad_audio_passthrough=True
+            )
+        )
+
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            **({'url': url} if (url := os.getenv("DEEPGRAM_STT_URL")) else {})
+
+        )
+
+        tts = ClearableDeepgramTTSService(
+            name="STT",
+            aiohttp_session=session,
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            voice="aura-asteria-en",
+            **({'base_url': url} if (url := os.getenv("DEEPGRAM_TTS_BASE_URL")) else {})
+        )
+
+        llm = OpenAILLMService(
+            name="LLM",
+            # To use OpenAI
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model=os.getenv("OPENAI_MODEL"),
+            base_url=os.getenv("OPENAI_BASE_URL")
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": """You are a helpful assistant in an audio conversation.
+
+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. Be concise in your answers to basic questions. If you are asked to elaborate or tell a story, provide a longer response.
+""",
+            },
+        ]
+
+        avt = AudioVolumeTimer()
+        tl = TranscriptionTimingLogger(avt)
+
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            avt,
+            stt,
+            tl,
+            tma_in,              # User responses
+            llm,                 # LLM
+            tts,                 # TTS
+            transport.output(),  # Transport bot output
+            tma_out,             # Assistant spoken responses
+        ])
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                report_only_initial_ttfb=True
+            ))
+
+        # When a participant joins, start transcription for that participant so the
+        # bot can "hear" and respond to them.
+        # @ transport.event_handler("on_participant_joined")
+        # async def on_participant_joined(transport, participant):
+        #    transport.capture_participant_transcription(participant["id"])
+
+        # When the first participant joins, the bot should introduce itself.
+        @ transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            messages.append(
+                {"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        # Handle "latency-ping" messages. The client will send app messages that look like
+        # this:
+        #   { "latency-ping": { ts: <client-side timestamp> }}
+        #
+        # We want to send an immediate pong back to the client from this handler function.
+        # Also, we will push a frame into the top of the pipeline and send it after the
+        #
+        @ transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            try:
+                if "latency-ping" in message:
+                    logger.debug(f"Received latency ping app message: {message}")
+                    ts = message["latency-ping"]["ts"]
+                    # Send immediately
+                    transport.output().send_message(DailyTransportMessageFrame(
+                        message={"latency-pong-msg-handler": {"ts": ts}},
+                        participant_id=sender))
+                    # And push to the pipeline for the Daily transport.output to send
+                    await tma_in.push_frame(
+                        DailyTransportMessageFrame(
+                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
+                            participant_id=sender))
+            except Exception as e:
+                logger.debug(f"message handling error: {e} - {message}")
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))
diff --git a/examples/fast-bot-metrics/tmp.py b/examples/fast-bot-metrics/vad-gated-pipeline.py
similarity index 100%
rename from examples/fast-bot-metrics/tmp.py
rename to examples/fast-bot-metrics/vad-gated-pipeline.py