diff --git a/examples/foundational/tmp-khk-sonnet-ttft.py b/examples/foundational/tmp-khk-sonnet-ttft.py
new file mode 100644
index 000000000..3c5265208
--- /dev/null
+++ b/examples/foundational/tmp-khk-sonnet-ttft.py
@@ -0,0 +1,114 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+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")
+
+
+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 = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_name=sys.argv[1] if len(sys.argv) > 1 else "British Lady"
+        )
+
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-sonnet-20240620",
+            temperature=1.0
+        )
+
+        # todo: think more about how to handle system prompts in a more general way. OpenAI,
+        # Google, and Anthropic all have slightly different approaches to providing a system
+        # prompt.
+        messages = [
+            {
+                "role": "system",
+                "content": (
+                    "You are participating in a friendly competition to invent creative "
+                    "new ice cream flavors. Say the craziest flavor you can think of "
+                    "then wait for your opponent to come up with a different crazy flavor. "
+                    "then respond with another flavor idea. Repeat forever. Say only the "
+                    "ice cream flavors and nothing else. End each ice cream flavor statement "
+                    "with an exclamation mark! Go ..."
+                )
+            },
+        ]
+
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            tma_in,              # User responses
+            llm,                 # LLM
+            tts,                 # TTS
+            transport.output(),  # Transport bot output
+            tma_out,             # Assistant spoken responses
+            tma_trim
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=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):
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))
+
+# '{"action":"app-message","data":{"metrics":{"ttfb":[{"name":"AnthropicLLMService#0","time":0.5975627899169922}]},"type":"pipecat-metrics"},"fromId":"592d3489-90ba-401d-a760-c1a863d64a4a","callFrameId":"17189290998160.035120590426112264"}'
+# [Durian and Limburger Cheese Charcoal Activated Tar Twist!]
+# [Fermented Fish Sauce and Ghost Pepper Bubblegum Cotton Candy Nightmare!]
+# [Spoiled Yogurt and Ghost Pepper Gummy Bear Blizzard!]
diff --git a/src/pipecat/services/anthropic.py b/src/pipecat/services/anthropic.py
index 688d43b14..05ae5aeca 100644
--- a/src/pipecat/services/anthropic.py
+++ b/src/pipecat/services/anthropic.py
@@ -40,14 +40,17 @@ class AnthropicLLMService(LLMService):
     """
 
     def __init__(
-            self,
-            api_key: str,
-            model: str = "claude-3-opus-20240229",
-            max_tokens: int = 1024):
+        self,
+        api_key: str,
+        model: str = "claude-3-opus-20240229",
+        max_tokens: int = 1024,
+        temperature: float = 0.0
+    ):
         super().__init__()
         self._client = AsyncAnthropic(api_key=api_key)
         self._model = model
         self._max_tokens = max_tokens
+        self._temperature = temperature
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -110,6 +113,7 @@ class AnthropicLLMService(LLMService):
                 messages=messages,
                 model=self._model,
                 max_tokens=self._max_tokens,
+                temperature=self._temperature,
                 stream=True)
 
             await self.stop_ttfb_metrics()