Refactoring the pipecat agent

examples/aws-agentcore/agents/pipecat/env.example

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+OPENAI_API_KEY=...
+DEEPGRAM_API_KEY=...
+CARTESIA_API_KEY=...
+
+PIPECAT_LOCAL_DEV=...
+TURN_USERNAME=
+TURN_CREDENTIAL=

examples/aws-agentcore/agents/pipecat/pipecat.py

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+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+import aiohttp
+from bedrock_agentcore import BedrockAgentCoreApp
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.types import DailyRunnerArguments, RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyLogLevel, DailyParams, DailyTransport
+
+app = BedrockAgentCoreApp()
+
+load_dotenv(override=True)
+
+
+async def get_public_ip():
+    """Retrieve public IP from AWS metadata service or external service."""
+    try:
+        # Fallback to external service
+        async with aiohttp.ClientSession() as session:
+            async with session.get(
+                "https://api.ipify.org", timeout=aiohttp.ClientTimeout(total=5)
+            ) as response:
+                if response.status == 200:
+                    return await response.text()
+    except Exception:
+        pass
+
+    return None
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def fetch_restaurant_recommendation(params: FunctionCallParams):
+    await params.result_callback({"name": "The Golden Dragon"})
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    public_ip = await get_public_ip()
+    if public_ip:
+        logger.info(f"Public IP address: {public_ip}")
+    else:
+        logger.warning("Could not retrieve public IP address")
+
+    yield {"status": "initializing", "ip": public_ip}
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    # You can also register a function_name of None to get all functions
+    # sent to the same callback with an additional function_name parameter.
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
+
+    @llm.event_handler("on_function_calls_started")
+    async def on_function_calls_started(service, function_calls):
+        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
+
+    weather_function = FunctionSchema(
+        name="get_current_weather",
+        description="Get the current weather",
+        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 user's location.",
+            },
+        },
+        required=["location", "format"],
+    )
+    restaurant_function = FunctionSchema(
+        name="get_restaurant_recommendation",
+        description="Get a restaurant recommendation",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
+
+    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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    task_id = app.add_async_task("voice_agent")
+
+    await runner.run(task)
+
+    app.complete_async_task(task_id)
+
+    yield {"status": "completed"}
+
+
+async def bot(runner_args: RunnerArguments):
+    """Bot entry point for running locally and on Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    async for result in run_bot(transport, runner_args):
+        pass  # Consume the stream
+
+
+@app.entrypoint
+async def agentcore_bot(payload, context):
+    """Bot entry point for running on Amazon Bedrock AgentCore Runtime."""
+    room_url = payload.get("roomUrl")
+    transport = await create_transport(
+        DailyRunnerArguments(room_url=room_url),
+        transport_params,
+    )
+    if isinstance(transport, DailyTransport):
+        transport.set_log_level(DailyLogLevel.Info)
+        turn_username = os.getenv("TURN_USERNAME")
+        turn_credential = os.getenv("TURN_CREDENTIAL")
+        transport._client._client.set_ice_config(
+            {
+                "placement": "replace",
+                "iceServers": [
+                    {
+                        "urls": [
+                            "turn:turn.cloudflare.com:80?transport=tcp",
+                            "turns:turn.cloudflare.com:443?transport=tcp",
+                        ],
+                        "username": turn_username,
+                        "credential": turn_credential,
+                    },
+                ],
+            }
+        )
+
+    async for result in run_bot(transport, RunnerArguments()):
+        yield result
+
+
+if __name__ == "__main__":
+    # NOTE: ideally we shouldn't have to branch for local dev vs AgentCore, but
+    # local AgentCore container-based dev doesn't seem to be working, or at
+    # least not for this project.
+    if os.getenv("PIPECAT_LOCAL_DEV") == "1":
+        # Running locally
+        from pipecat.runner.run import main
+
+        main()
+    else:
+        # Running on AgentCore Runtime
+        app.run()

examples/aws-agentcore/agents/pipecat/requirements.txt

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+aiohttp
+bedrock-agentcore
+pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]