added JSON serializtion and additional testing utilities

Gemini function calling fixes

CHANGELOG.md

@@ -5,6 +5,24 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Changed
+
+- `FunctionFilter` now has a `filter_system_frames` arg, which controls whether
+  or not SystemFrames are filtered.
+
+- Upgraded `aws_sdk_bedrock_runtime` to v0.1.1 to resolve potential CPU issues
+  when running `AWSNovaSonicLLMService`.
+
+### Fixed
+
+- Fixed an issue in the runner where starting a DailyTransport room via
+  `/start` didn't support using the `DAILY_SAMPLE_ROOM_URL` env var.
+
+- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
+  all STT services producing `TranscriptionFrame`s.
+
 ## [0.0.91] - 2025-10-21
 
 ### Added

examples/foundational/48-service-switcher.py

@@ -0,0 +1,153 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+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, ManuallySwitchServiceFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.service_switcher import ServiceSwitcher, ServiceSwitcherStrategyManual
+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 RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.stt import CartesiaSTTService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+# 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()),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        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")
+
+    stt_cartesia = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt_deepgram = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt_switcher = ServiceSwitcher(
+        services=[stt_cartesia, stt_deepgram], strategy_type=ServiceSwitcherStrategyManual
+    )
+
+    tts_cartesia = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
+    )
+    tts_deepgram = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    tts_switcher = ServiceSwitcher(
+        services=[tts_cartesia, tts_deepgram], strategy_type=ServiceSwitcherStrategyManual
+    )
+
+    llm_openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+    llm_google = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+    llm_switcher = ServiceSwitcher(
+        services=[llm_openai, llm_google], strategy_type=ServiceSwitcherStrategyManual
+    )
+
+    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 = LLMContext(messages)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt_switcher,
+            context_aggregator.user(),  # User responses
+            llm_switcher,  # LLM
+            tts_switcher,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    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.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+        await asyncio.sleep(15)
+        print(f"Switching to {stt_deepgram}")
+        await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
+        await asyncio.sleep(15)
+        print(f"Switching to {llm_google}")
+        await task.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
+        await asyncio.sleep(15)
+        print(f"Switching to {tts_deepgram}")
+        await task.queue_frames([ManuallySwitchServiceFrame(service=tts_deepgram)])
+
+    @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)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

examples/quickstart/README.md

@@ -73,13 +73,13 @@ Transform your local bot into a production-ready service. Pipecat Cloud handles
 
 1. [Sign up for Pipecat Cloud](https://pipecat.daily.co/sign-up).
 
-2. Install the Pipecat Cloud CLI:
+2. Install the Pipecat CLI:
 
    ```bash
-   uv add pipecatcloud
+   uv tool install pipecat-ai-cli
    ```
 
-> 💡 Tip: You can run the `pipecatcloud` CLI using the `pcc` alias.
+> 💡 Tip: You can run the `pipecat` CLI using the `pc` alias.
 
 3. Set up Docker for building your bot image:
 
@@ -113,12 +113,22 @@ secret_set = "quickstart-secrets"
 
 > 💡 Tip: [Set up `image_credentials`](https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets) in your TOML file for authenticated image pulls
 
+### Log in to Pipecat Cloud
+
+To start using the CLI, authenticate to Pipecat Cloud:
+
+```bash
+pipecat cloud auth login
+```
+
+You'll be presented with a link that you can click to authenticate your client.
+
 ### Configure secrets
 
 Upload your API keys to Pipecat Cloud's secure storage:
 
 ```bash
-uv run pcc secrets set quickstart-secrets --file .env
+pipecat cloud secrets set quickstart-secrets --file .env
 ```
 
 This creates a secret set called `quickstart-secrets` (matching your TOML file) and uploads all your API keys from `.env`.
@@ -128,13 +138,13 @@ This creates a secret set called `quickstart-secrets` (matching your TOML file)
 Build your Docker image and push to Docker Hub:
 
 ```bash
-uv run pcc docker build-push
+pipecat cloud docker build-push
 ```
 
 Deploy to Pipecat Cloud:
 
 ```bash
-uv run pcc deploy
+pipecat cloud deploy
 ```
 
 ### Connect to your agent

examples/quickstart/pcc-deploy.toml

@@ -1,6 +1,11 @@
 agent_name = "quickstart"
 image = "your_username/quickstart:0.1"
 secret_set = "quickstart-secrets"
+agent_profile = "agent-1x"
+
+# RECOMMENDED: Set an image pull secret:
+# https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets
+# image_credentials = "your_image_pull_secret"
 
 [scaling]
 	min_agents = 1

examples/quickstart/pyproject.toml

@@ -4,13 +4,14 @@ version = "0.1.0"
 description = "Quickstart example for building voice AI bots with Pipecat"
 requires-python = ">=3.10"
 dependencies = [
-    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]>=0.0.86",
-    "pipecatcloud>=0.2.4"
+    "pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]",
+    "pipecat-ai-cli"
 ]
 
 [dependency-groups]
 dev = [
-    "ruff~=0.12.1",
+    "pyright>=1.1.404,<2",
+    "ruff>=0.12.11,<1",
 ]
 
 [tool.ruff]

pyproject.toml

@@ -50,7 +50,7 @@ anthropic = [ "anthropic~=0.49.0" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
 aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
-aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.0; python_version>='3.12'" ]
+aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.1; python_version>='3.12'" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
 cerebras = []

src/pipecat/adapters/services/anthropic_adapter.py

@@ -110,7 +110,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         system = NOT_GIVEN
         messages = []
 
-        # first, map messages using self._from_universal_context_message(m)
+        # First, map messages using self._from_universal_context_message(m)
         try:
             messages = [self._from_universal_context_message(m) for m in universal_context_messages]
         except Exception as e:

src/pipecat/adapters/services/bedrock_adapter.py

@@ -107,7 +107,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
         system = None
         messages = []
 
-        # first, map messages using self._from_universal_context_message(m)
+        # First, map messages using self._from_universal_context_message(m)
         try:
             messages = [self._from_universal_context_message(m) for m in universal_context_messages]
         except Exception as e:

src/pipecat/adapters/services/gemini_adapter.py

@@ -8,8 +8,8 @@
 
 import base64
 import json
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, TypedDict
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple, TypedDict
 
 from loguru import logger
 from openai import NotGiven
@@ -133,6 +133,28 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         messages: List[Content]
         system_instruction: Optional[str] = None
 
+    @dataclass
+    class MessageConversionResult:
+        """Result of converting a single universal context message to Google format.
+
+        Either content (a Google Content object) or a system instruction string
+        is guaranteed to be set.
+
+        Also returns a tool call ID to name mapping for any tool calls
+        discovered in the message.
+        """
+
+        content: Optional[Content] = None
+        system_instruction: Optional[str] = None
+        tool_call_id_to_name_mapping: Dict[str, str] = field(default_factory=dict)
+
+    @dataclass
+    class MessageConversionParams:
+        """Parameters for converting a single universal context message to Google format."""
+
+        already_have_system_instruction: bool
+        tool_call_id_to_name_mapping: Dict[str, str]
+
     def _from_universal_context_messages(
         self, universal_context_messages: List[LLMContextMessage]
     ) -> ConvertedMessages:
@@ -156,24 +178,26 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         """
         system_instruction = None
         messages = []
+        tool_call_id_to_name_mapping = {}
 
         # Process each message, preserving Google-formatted messages and converting others
         for message in universal_context_messages:
-            if isinstance(message, LLMSpecificMessage):
-                # Assume that LLMSpecificMessage wraps a message in Google format
-                messages.append(message.message)
-                continue
-
-            # Convert standard format to Google format
-            converted = self._from_standard_message(
-                message, already_have_system_instruction=bool(system_instruction)
+            result = self._from_universal_context_message(
+                message,
+                params=self.MessageConversionParams(
+                    already_have_system_instruction=bool(system_instruction),
+                    tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
+                ),
             )
-            if isinstance(converted, Content):
-                # Regular (non-system) message
-                messages.append(converted)
-            else:
-                # System instruction
-                system_instruction = converted
+            # Each result is either a Content or a system instruction
+            if result.content:
+                messages.append(result.content)
+            elif result.system_instruction:
+                system_instruction = result.system_instruction
+
+            # Merge tool call ID to name mapping
+            if result.tool_call_id_to_name_mapping:
+                tool_call_id_to_name_mapping.update(result.tool_call_id_to_name_mapping)
 
         # Check if we only have function-related messages (no regular text)
         has_regular_messages = any(
@@ -193,9 +217,16 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
 
         return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
 
+    def _from_universal_context_message(
+        self, message: LLMContextMessage, *, params: MessageConversionParams
+    ) -> MessageConversionResult:
+        if isinstance(message, LLMSpecificMessage):
+            return self.MessageConversionResult(content=message.message)
+        return self._from_standard_message(message, params=params)
+
     def _from_standard_message(
-        self, message: LLMStandardMessage, already_have_system_instruction: bool
-    ) -> Content | str:
+        self, message: LLMStandardMessage, *, params: MessageConversionParams
+    ) -> MessageConversionResult:
         """Convert standard universal context message to Google Content object.
 
         Handles conversion of text, images, and function calls to Google's
@@ -205,10 +236,11 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         Args:
             message: Message in standard universal context format.
             already_have_system_instruction: Whether we already have a system instruction
+            params: Parameters for conversion.
 
         Returns:
-            Content object with role and parts, or a plain string for system
-            messages.
+            MessageConversionResult containing either a Content object or a
+            system instruction string.
 
         Examples:
             Standard text message::
@@ -242,38 +274,48 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
             Converts to Google Content with::
 
                 Content(
-                    role="model",
+                    role="user",
                     parts=[Part(function_call=FunctionCall(name="search", args={"query": "test"}))]
                 )
         """
         role = message["role"]
         content = message.get("content", [])
+
         if role == "system":
-            if already_have_system_instruction:
+            if params.already_have_system_instruction:
                 role = "user"  # Convert system message to user role if we already have a system instruction
             else:
-                # System instructions are returned as plain text
+                system_instruction: str = None
                 if isinstance(content, str):
-                    return content
+                    system_instruction = content
                 elif isinstance(content, list):
                     # If content is a list, we assume it's a list of text parts, per the standard
-                    return " ".join(part["text"] for part in content if part.get("type") == "text")
+                    system_instruction = " ".join(
+                        part["text"] for part in content if part.get("type") == "text"
+                    )
+                if system_instruction:
+                    return self.MessageConversionResult(system_instruction=system_instruction)
         elif role == "assistant":
             role = "model"
 
         parts = []
+        tool_call_id_to_name_mapping = {}
+
         if message.get("tool_calls"):
             for tc in message["tool_calls"]:
+                id = tc["id"]
+                name = tc["function"]["name"]
+                tool_call_id_to_name_mapping[id] = name
                 parts.append(
                     Part(
                         function_call=FunctionCall(
-                            name=tc["function"]["name"],
+                            name=name,
                             args=json.loads(tc["function"]["arguments"]),
                         )
                     )
                 )
         elif role == "tool":
-            role = "model"
+            role = "user"
             try:
                 response = json.loads(message["content"])
                 if isinstance(response, dict):
@@ -284,12 +326,17 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                 # Response might not be JSON-deserializable.
                 # This occurs with a UserImageFrame, for example, where we get a plain "COMPLETED" string.
                 response_dict = {"value": message["content"]}
+
+            # Get function name from mapping using tool_call_id, or fallback
+            tool_call_id = message.get("tool_call_id")
+            function_name = "tool_call_result"  # Default fallback
+            if tool_call_id and tool_call_id in params.tool_call_id_to_name_mapping:
+                function_name = params.tool_call_id_to_name_mapping[tool_call_id]
+
             parts.append(
-                Part(
-                    function_response=FunctionResponse(
-                        name="tool_call_result",  # seems to work to hard-code the same name every time
-                        response=response_dict,
-                    )
+                Part.from_function_response(
+                    name=function_name,
+                    response=response_dict,
                 )
             )
         elif isinstance(content, str):
@@ -312,4 +359,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                     audio_bytes = base64.b64decode(input_audio["data"])
                     parts.append(Part(inline_data=Blob(mime_type="audio/wav", data=audio_bytes)))
 
-        return Content(role=role, parts=parts)
+        return self.MessageConversionResult(
+            content=Content(role=role, parts=parts),
+            tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
+        )

src/pipecat/pipeline/llm_switcher.py

@@ -14,20 +14,41 @@ from pipecat.services.llm_service import LLMService
 
 
 class LLMSwitcher(ServiceSwitcher[StrategyType]):
-    """A pipeline that switches between different LLMs at runtime."""
+    """A pipeline that switches between different LLMs at runtime.
+
+    Example::
+
+        llm_switcher = LLMSwitcher(
+            llms=[openai_llm, anthropic_llm],
+            strategy_type=ServiceSwitcherStrategyManual
+        )
+    """
 
     def __init__(self, llms: List[LLMService], strategy_type: Type[StrategyType]):
-        """Initialize the service switcher with a list of LLMs and a switching strategy."""
+        """Initialize the service switcher with a list of LLMs and a switching strategy.
+
+        Args:
+            llms: List of LLM services to switch between.
+            strategy_type: The strategy class to use for switching between LLMs.
+        """
         super().__init__(llms, strategy_type)
 
     @property
     def llms(self) -> List[LLMService]:
-        """Get the list of LLMs managed by this switcher."""
+        """Get the list of LLMs managed by this switcher.
+
+        Returns:
+            List of LLM services managed by this switcher.
+        """
         return self.services
 
     @property
     def active_llm(self) -> Optional[LLMService]:
-        """Get the currently active LLM, if any."""
+        """Get the currently active LLM.
+
+        Returns:
+            The currently active LLM service, or None if no LLM is active.
+        """
         return self.strategy.active_service
 
     async def run_inference(self, context: LLMContext) -> Optional[str]:

src/pipecat/pipeline/service_switcher.py

@@ -21,10 +21,22 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class ServiceSwitcherStrategy:
-    """Base class for service switching strategies."""
+    """Base class for service switching strategies.
+
+    Note:
+        Strategy classes are instantiated internally by ServiceSwitcher.
+        Developers should pass the strategy class (not an instance) to ServiceSwitcher.
+    """
 
     def __init__(self, services: List[FrameProcessor]):
-        """Initialize the service switcher strategy with a list of services."""
+        """Initialize the service switcher strategy with a list of services.
+
+        Note:
+            This is called internally by ServiceSwitcher. Do not instantiate directly.
+
+        Args:
+            services: List of frame processors to switch between.
+        """
         self.services = services
         self.active_service: Optional[FrameProcessor] = None
 
@@ -46,10 +58,24 @@ class ServiceSwitcherStrategyManual(ServiceSwitcherStrategy):
 
     This strategy allows the user to manually select which service is active.
     The initial active service is the first one in the list.
+
+    Example::
+
+        stt_switcher = ServiceSwitcher(
+            services=[stt_1, stt_2],
+            strategy_type=ServiceSwitcherStrategyManual
+        )
     """
 
     def __init__(self, services: List[FrameProcessor]):
-        """Initialize the manual service switcher strategy with a list of services."""
+        """Initialize the manual service switcher strategy with a list of services.
+
+        Note:
+            This is called internally by ServiceSwitcher. Do not instantiate directly.
+
+        Args:
+            services: List of frame processors to switch between.
+        """
         super().__init__(services)
         self.active_service = services[0] if services else None
 
@@ -85,7 +111,12 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
     """A pipeline that switches between different services at runtime."""
 
     def __init__(self, services: List[FrameProcessor], strategy_type: Type[StrategyType]):
-        """Initialize the service switcher with a list of services and a switching strategy."""
+        """Initialize the service switcher with a list of services and a switching strategy.
+
+        Args:
+            services: List of frame processors to switch between.
+            strategy_type: The strategy class to use for switching between services.
+        """
         strategy = strategy_type(services)
         super().__init__(*self._make_pipeline_definitions(services, strategy))
         self.services = services
@@ -100,14 +131,20 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
             active_service: FrameProcessor,
             direction: FrameDirection,
         ):
-            """Initialize the service switcher filter with a strategy and direction."""
+            """Initialize the service switcher filter with a strategy and direction.
+
+            Args:
+                wrapped_service: The service that this filter wraps.
+                active_service: The currently active service.
+                direction: The direction of frame flow to filter.
+            """
+            self._wrapped_service = wrapped_service
+            self._active_service = active_service
 
             async def filter(_: Frame) -> bool:
                 return self._wrapped_service == self._active_service
 
-            super().__init__(filter, direction)
-            self._wrapped_service = wrapped_service
-            self._active_service = active_service
+            super().__init__(filter, direction, filter_system_frames=True)
 
         async def process_frame(self, frame, direction):
             """Process a frame through the filter, handling special internal filter-updating frames."""

src/pipecat/pipeline/task_observer.py

@@ -189,7 +189,7 @@ class TaskObserver(BaseObserver):
             if isinstance(data, FramePushed):
                 if on_push_frame_deprecated:
                     await observer.on_push_frame(
-                        data.src, data.dst, data.frame, data.direction, data.timestamp
+                        data.source, data.destination, data.frame, data.direction, data.timestamp
                     )
                 else:
                     await observer.on_push_frame(data)

src/pipecat/processors/filters/function_filter.py

@@ -12,7 +12,7 @@ allowing for flexible frame filtering logic in processing pipelines.
 
 from typing import Awaitable, Callable
 
-from pipecat.frames.frames import EndFrame, Frame, SystemFrame
+from pipecat.frames.frames import CancelFrame, EndFrame, Frame, StartFrame, SystemFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
@@ -28,6 +28,7 @@ class FunctionFilter(FrameProcessor):
         self,
         filter: Callable[[Frame], Awaitable[bool]],
         direction: FrameDirection = FrameDirection.DOWNSTREAM,
+        filter_system_frames: bool = False,
     ):
         """Initialize the function filter.
 
@@ -36,22 +37,32 @@ class FunctionFilter(FrameProcessor):
                 frame should pass through, False otherwise.
             direction: The direction to apply filtering. Only frames moving in
                 this direction will be filtered. Defaults to DOWNSTREAM.
+            filter_system_frames: Whether to filter system frames. Defaults to False.
         """
         super().__init__()
         self._filter = filter
         self._direction = direction
+        self._filter_system_frames = filter_system_frames
 
     #
     # Frame processor
     #
 
-    # Ignore system frames, end frames and frames that are not following the
-    # direction of this gate
     def _should_passthrough_frame(self, frame, direction):
         """Check if a frame should pass through without filtering."""
-        # Ignore system frames, end frames and frames that are not following the
-        # direction of this gate
-        return isinstance(frame, (SystemFrame, EndFrame)) or direction != self._direction
+        # Always passthrough frames in the wrong direction
+        if direction != self._direction:
+            return True
+
+        # Always passthrough lifecycle frames
+        if isinstance(frame, (StartFrame, EndFrame, CancelFrame)):
+            return True
+
+        # If not filtering system frames, passthrough all other system frames
+        if not self._filter_system_frames and isinstance(frame, SystemFrame):
+            return True
+
+        return False
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process a frame through the filter.

src/pipecat/runner/run.py

@@ -573,8 +573,14 @@ def _setup_daily_routes(app: FastAPI):
 
         bot_module = _get_bot_module()
 
+        existing_room_url = os.getenv("DAILY_SAMPLE_ROOM_URL")
+
         result = None
-        if create_daily_room:
+
+        # Configure room if:
+        # 1. Explicitly requested via createDailyRoom in payload
+        # 2. Using pre-configured room from DAILY_SAMPLE_ROOM_URL env var
+        if create_daily_room or existing_room_url:
             import aiohttp
 
             from pipecat.runner.daily import configure

src/pipecat/services/google/llm.py

@@ -1034,6 +1034,23 @@ class GoogleLLMService(LLMService):
         if context:
             await self._process_context(context)
 
+    async def stop(self, frame):
+        """Override stop to gracefully close the client."""
+        await super().stop(frame)
+        await self._close_client()
+
+    async def cancel(self, frame):
+        """Override cancel to gracefully close the client."""
+        await super().cancel(frame)
+        await self._close_client()
+
+    async def _close_client(self):
+        try:
+            await self._client.aio.aclose()
+        except Exception:
+            # Do nothing - we're shutting down anyway
+            pass
+
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,

src/pipecat/tests/__init__.py

@@ -0,0 +1,12 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Public testing API for Pipecat frame processors."""
+
+from .serialization import dict_to_frame, frame_to_dict, load_frames_from_json
+from .test_runner import run_test_from_file
+
+__all__ = ["dict_to_frame", "frame_to_dict", "load_frames_from_json", "run_test_from_file"]

src/pipecat/tests/serialization.py

@@ -0,0 +1,150 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Frame serialization and deserialization for testing."""
+
+import base64
+import inspect
+import json
+from pathlib import Path
+from typing import Any, Dict, List
+
+from pipecat.frames import frames
+
+
+def _get_frame_class(frame_type: str):
+    """Get a frame class by name from the frames module.
+    
+    Args:
+        frame_type: The name of the frame class (e.g., "TextFrame")
+        
+    Returns:
+        The frame class object
+        
+    Raises:
+        ValueError: If the frame type is not found
+    """
+    if not hasattr(frames, frame_type):
+        raise ValueError(f"Unknown frame type: {frame_type}")
+    
+    cls = getattr(frames, frame_type)
+    if not inspect.isclass(cls) or not issubclass(cls, frames.Frame):
+        raise ValueError(f"{frame_type} is not a valid Frame class")
+    
+    return cls
+
+
+def dict_to_frame(data: Dict[str, Any]) -> frames.Frame:
+    """Convert a dictionary to a Frame object.
+    
+    Args:
+        data: Dictionary containing frame data with a "type" key
+        
+    Returns:
+        A Frame instance
+        
+    Raises:
+        ValueError: If frame type is missing or invalid
+        
+    Example:
+        >>> dict_to_frame({"type": "TextFrame", "text": "hello"})
+        TextFrame(text="hello")
+    """
+    if "type" not in data:
+        raise ValueError("Frame dictionary must contain a 'type' field")
+    
+    frame_type = data["type"]
+    frame_cls = _get_frame_class(frame_type)
+    
+    # Build kwargs from data, excluding 'type'
+    kwargs = {k: v for k, v in data.items() if k != "type"}
+    
+    # Special handling for audio frames with base64 encoded audio
+    if "audio" in kwargs and isinstance(kwargs["audio"], str):
+        kwargs["audio"] = base64.b64decode(kwargs["audio"])
+    
+    # Special handling for image frames with base64 encoded images
+    if "image" in kwargs and isinstance(kwargs["image"], str):
+        kwargs["image"] = base64.b64decode(kwargs["image"])
+    
+    try:
+        return frame_cls(**kwargs)
+    except TypeError as e:
+        raise ValueError(f"Failed to create {frame_type}: {e}")
+
+
+def load_frames_from_json(filepath: str) -> List[frames.Frame]:
+    """Load frames from a JSON file.
+    
+    Args:
+        filepath: Path to JSON file containing frame data
+        
+    Returns:
+        List of Frame objects
+        
+    Raises:
+        FileNotFoundError: If the file doesn't exist
+        ValueError: If JSON is invalid or frames cannot be deserialized
+        
+    Example JSON format:
+        {
+          "input_frames": [
+            {"type": "TextFrame", "text": "hello"},
+            {"type": "EndFrame"}
+          ]
+        }
+    """
+    path = Path(filepath)
+    if not path.exists():
+        raise FileNotFoundError(f"Frame file not found: {filepath}")
+    
+    with open(path, "r") as f:
+        data = json.load(f)
+    
+    if not isinstance(data, dict):
+        raise ValueError("JSON must contain a dictionary")
+    
+    if "input_frames" not in data:
+        raise ValueError("JSON must contain an 'input_frames' key")
+    
+    frame_dicts = data["input_frames"]
+    if not isinstance(frame_dicts, list):
+        raise ValueError("'input_frames' must be a list")
+    
+    return [dict_to_frame(frame_dict) for frame_dict in frame_dicts]
+
+
+def frame_to_dict(frame: frames.Frame) -> Dict[str, Any]:
+    """Convert a Frame object to a dictionary.
+    
+    Args:
+        frame: Frame object to serialize
+        
+    Returns:
+        Dictionary representation of the frame
+        
+    Example:
+        >>> frame_to_dict(TextFrame(text="hello"))
+        {"type": "TextFrame", "text": "hello"}
+    """
+    result = {"type": frame.__class__.__name__}
+    
+    # Get all fields from the dataclass
+    if hasattr(frame, "__dataclass_fields__"):
+        for field_name in frame.__dataclass_fields__:
+            # Skip internal fields from base Frame class
+            if field_name in ("id", "name", "pts", "metadata", "transport_source", "transport_destination"):
+                continue
+            
+            value = getattr(frame, field_name, None)
+            if value is not None:
+                # Special handling for bytes (audio/image data)
+                if isinstance(value, bytes):
+                    result[field_name] = base64.b64encode(value).decode("utf-8")
+                else:
+                    result[field_name] = value
+    
+    return result

src/pipecat/tests/test_runner.py

@@ -0,0 +1,169 @@
+#
+# Copyright (c) 2024-2025 Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Test runner for frame processors from JSON test files."""
+
+import json
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from pipecat.frames.frames import Frame
+from pipecat.processors.frame_processor import FrameProcessor
+
+from .serialization import dict_to_frame, frame_to_dict, load_frames_from_json
+
+
+async def run_test_from_file(
+    processor: FrameProcessor,
+    test_file: str,
+) -> Tuple[List[Frame], Optional[List[Dict[str, Any]]], bool]:
+    """Run a processor test from a JSON test file.
+
+    Args:
+        processor: The frame processor to test
+        test_file: Path to JSON test file
+
+    Returns:
+        Tuple of (output_frames, expected_output, passed)
+        - output_frames: List of Frame objects that were output
+        - expected_output: List of expected frame dicts (None if not specified)
+        - passed: True if test passed, False if failed, None if no validation
+
+    Raises:
+        FileNotFoundError: If test file doesn't exist
+        ValueError: If test file is invalid
+
+    Example test file format:
+        {
+          "input_frames": [
+            {"type": "TextFrame", "text": "hello"}
+          ],
+          "expected_output": [
+            {"type": "TextFrame"},
+            {"type": "EndFrame"}
+          ]
+        }
+    """
+    path = Path(test_file)
+    if not path.exists():
+        raise FileNotFoundError(f"Test file not found: {test_file}")
+
+    with open(path, "r") as f:
+        test_data = json.load(f)
+
+    # Load input frames
+    if "input_frames" not in test_data:
+        raise ValueError("Test file must contain 'input_frames'")
+
+    input_frames = [dict_to_frame(frame_dict) for frame_dict in test_data["input_frames"]]
+
+    # Load expected output (optional)
+    expected_output = test_data.get("expected_output", None)
+
+    # Run the test
+    # Note: run_test() only collects frames if expected_down_frames is provided,
+    # so we need to manually collect from the pipeline ourselves
+    import asyncio
+    from pipecat.frames.frames import EndFrame
+    from pipecat.processors.frame_processor import FrameDirection
+    from pipecat.tests.utils import QueuedFrameProcessor
+    from pipecat.pipeline.pipeline import Pipeline
+    from pipecat.pipeline.task import PipelineTask, PipelineParams
+    from pipecat.pipeline.runner import PipelineRunner
+
+    # Set up the test pipeline manually
+    received_down = asyncio.Queue()
+    received_up = asyncio.Queue()
+    source = QueuedFrameProcessor(
+        queue=received_up,
+        queue_direction=FrameDirection.UPSTREAM,
+        ignore_start=True,
+    )
+    sink = QueuedFrameProcessor(
+        queue=received_down,
+        queue_direction=FrameDirection.DOWNSTREAM,
+        ignore_start=True,
+    )
+
+    pipeline = Pipeline([source, processor, sink])
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(),
+        observers=[],
+        cancel_on_idle_timeout=False,
+    )
+
+    async def push_frames():
+        await asyncio.sleep(0.01)
+        for frame in input_frames:
+            await task.queue_frame(frame)
+        await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner()
+    await asyncio.gather(runner.run(task), push_frames())
+
+    # Collect all frames from the downstream queue
+    downstream_frames = []
+    while not received_down.empty():
+        frame = await received_down.get()
+        if not isinstance(frame, EndFrame):
+            downstream_frames.append(frame)
+
+    # Validate if expected_output is provided
+    passed = None
+    if expected_output is not None:
+        passed = _validate_output(downstream_frames, expected_output)
+
+    return downstream_frames, expected_output, passed
+
+
+def _validate_output(actual_frames: List[Frame], expected_output: List[Dict[str, Any]]) -> bool:
+    """Validate actual output frames against expected output.
+
+    Args:
+        actual_frames: List of frames that were actually output
+        expected_output: List of expected frame specifications
+
+    Returns:
+        True if validation passed, False otherwise
+    """
+    if len(actual_frames) != len(expected_output):
+        return False
+
+    for actual, expected in zip(actual_frames, expected_output):
+        # Check frame type
+        if "type" not in expected:
+            return False
+
+        expected_type = expected["type"]
+        if actual.__class__.__name__ != expected_type:
+            return False
+
+        # Check specific fields if provided
+        for field_name, expected_value in expected.items():
+            if field_name == "type":
+                continue
+
+            if not hasattr(actual, field_name):
+                return False
+
+            actual_value = getattr(actual, field_name)
+
+            # Special handling for different types
+            if isinstance(expected_value, str) and isinstance(actual_value, str):
+                # For string fields, support partial matching with "contains"
+                if field_name.endswith("_contains"):
+                    base_field = field_name.replace("_contains", "")
+                    if hasattr(actual, base_field):
+                        actual_text = getattr(actual, base_field)
+                        if expected_value not in actual_text:
+                            return False
+                elif actual_value != expected_value:
+                    return False
+            elif actual_value != expected_value:
+                return False
+
+    return True

tests/test_service_switcher.py

@@ -7,10 +7,12 @@
 """Unit tests for ServiceSwitcher and related components."""
 
 import unittest
+from dataclasses import dataclass
 
 from pipecat.frames.frames import (
     Frame,
     ManuallySwitchServiceFrame,
+    SystemFrame,
     TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
@@ -52,6 +54,13 @@ class MockFrameProcessor(FrameProcessor):
         self.frame_count = 0
 
 
+@dataclass
+class DummySystemFrame(SystemFrame):
+    """A dummy system frame for testing purposes."""
+
+    text: str = ""
+
+
 class TestServiceSwitcherStrategyManual(unittest.IsolatedAsyncioTestCase):
     """Test cases for ServiceSwitcherStrategyManual."""
 
@@ -140,14 +149,22 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         # Send some test frames
         frames_to_send = [
             TextFrame(text="Hello 1"),
+            DummySystemFrame(text="System Message 1"),
             TextFrame(text="Hello 2"),
+            DummySystemFrame(text="System Message 2"),
             TextFrame(text="Hello 3"),
         ]
 
         await run_test(
             switcher,
             frames_to_send=frames_to_send,
-            expected_down_frames=[TextFrame, TextFrame, TextFrame],
+            expected_down_frames=[
+                DummySystemFrame,
+                DummySystemFrame,
+                TextFrame,
+                TextFrame,
+                TextFrame,
+            ],
             expected_up_frames=[],  # Expect no error frames
         )
 
@@ -156,7 +173,13 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         text_frames = [f for f in self.service1.processed_frames if isinstance(f, TextFrame)]
         self.assertEqual(len(text_frames), 3)
 
-        # Check that other services don't receive text frames (they might get StartFrame/EndFrame)
+        # Only service1 should have processed the system frames
+        system_frames = [
+            f for f in self.service1.processed_frames if isinstance(f, DummySystemFrame)
+        ]
+        self.assertEqual(len(system_frames), 2)
+
+        # Check that other services don't receive text frames (they still get StartFrame/EndFrame)
         service2_text_frames = [
             f for f in self.service2.processed_frames if isinstance(f, TextFrame)
         ]
@@ -166,10 +189,24 @@ class TestServiceSwitcher(unittest.IsolatedAsyncioTestCase):
         self.assertEqual(len(service2_text_frames), 0)
         self.assertEqual(len(service3_text_frames), 0)
 
+        # Check that other services don't receive dummy system frames (they still get StartFrame/EndFrame)
+        service2_system_frames = [
+            f for f in self.service2.processed_frames if isinstance(f, DummySystemFrame)
+        ]
+        service3_system_frames = [
+            f for f in self.service3.processed_frames if isinstance(f, DummySystemFrame)
+        ]
+        self.assertEqual(len(service2_system_frames), 0)
+        self.assertEqual(len(service3_system_frames), 0)
+
         # Verify the actual text frames processed
         for i, frame in enumerate(text_frames):
             self.assertEqual(frame.text, f"Hello {i + 1}")
 
+        # Verify the actual system frames processed
+        for i, frame in enumerate(system_frames):
+            self.assertEqual(frame.text, f"System Message {i + 1}")
+
     async def test_service_switching(self):
         """Test that after service switching using ManuallySwitchServiceFrame, the new active service receives frames while others don't."""
         switcher = ServiceSwitcher(self.services, ServiceSwitcherStrategyManual)

uv.lock

@@ -410,16 +410,16 @@ wheels = [
 
 [[package]]
 name = "aws-sdk-bedrock-runtime"
-version = "0.1.0"
+version = "0.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "smithy-aws-core", extra = ["eventstream", "json"], marker = "python_full_version >= '3.12'" },
     { name = "smithy-core", marker = "python_full_version >= '3.12'" },
     { name = "smithy-http", extra = ["awscrt"], marker = "python_full_version >= '3.12'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/84/e1/39971b907c83a7525bab112c9b395e1bb6d4bc23bc1712d6d7a050662217/aws_sdk_bedrock_runtime-0.1.0.tar.gz", hash = "sha256:bd062de5a48404f64e1dfe6fb8841fbbf68e8f1798c357d14eb427274cb96a2b", size = 85419, upload-time = "2025-09-29T19:40:01.855Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/78/48574454b3cac869df67665e4a403ebfc3abfcfba2c2ff01ccfd67d55f8f/aws_sdk_bedrock_runtime-0.1.1.tar.gz", hash = "sha256:c896f99e675c3a1ab600633a07b785f3dc9fe8ab94f640b1f992b63da2dfc784", size = 82446, upload-time = "2025-10-21T20:25:25.845Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3d/e1/5b36bffe85010cdcd44730d1c2d5244653d57c002f440141d7fc3b9f1347/aws_sdk_bedrock_runtime-0.1.0-py3-none-any.whl", hash = "sha256:aac6ff47069d456ca5e23083d96a01e3e0cbc215414e6753c289d7d9efef3335", size = 78853, upload-time = "2025-09-29T19:40:00.341Z" },
+    { url = "https://files.pythonhosted.org/packages/83/07/62c0b70223d178c138f29124ac2f7973a6ba803abc7735b6a01a85217f3d/aws_sdk_bedrock_runtime-0.1.1-py3-none-any.whl", hash = "sha256:c0336b377b2112cf88197d3d44302fbeb3efb1101989fa49ae55e78f49cfe345", size = 74954, upload-time = "2025-10-21T20:25:24.973Z" },
 ]
 
 [[package]]
@@ -433,31 +433,31 @@ wheels = [
 
 [[package]]
 name = "awscrt"
-version = "0.28.1"
+version = "0.28.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a0/1c/5c9e6a7375c2a1355aadeb2d06c96c95934ec37ff29ebaab2919f59c3ff1/awscrt-0.28.1.tar.gz", hash = "sha256:70a28fd6ff3e0abb7854ea8a9133bc9e5de681a0d9bdbd8a599a23d13a448685", size = 37956730, upload-time = "2025-09-19T00:58:31.564Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d4/1b/a885a699217967c3ff0e1c49ac5b1e2a050d1a8b87d1e85e958a56e3d3f5/awscrt-0.28.2.tar.gz", hash = "sha256:9715a888f2042e710dc8aeb355963a29b77e7a4cc25a14659cebd21a5fa476c1", size = 37894849, upload-time = "2025-10-14T19:06:16.867Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2d/75/dd62276f2907a9ffcf9f8f780c08ce9938bd0550a15c887db198b47f24d3/awscrt-0.28.1-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:47f885104065918d311102e2b08b943966717c0f3b0c5de5908d2fd08de32198", size = 3376838, upload-time = "2025-09-19T00:57:32.988Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/93/562709cdf13a7606548426ecc31326ba3f6839f91e98a1e9230208308afb/awscrt-0.28.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3df2316e77ad88c456b7eb2c9928007d379ed892154c1969d35b98653617e576", size = 3821522, upload-time = "2025-09-19T00:57:35.456Z" },
-    { url = "https://files.pythonhosted.org/packages/43/f0/6c6ff81f5a4c6d085eb450854149087bf9240c37c467c747521f47901b32/awscrt-0.28.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3a060d930939f142345f46a344e19ffc0dada657b04d02216b8adffba550c0a0", size = 4087344, upload-time = "2025-09-19T00:57:36.62Z" },
-    { url = "https://files.pythonhosted.org/packages/37/0a/71c097505add4ceea4ac05153311715acb7489cd82ec69db4570130f4698/awscrt-0.28.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:43f81ca6bfe85c38ad9765605aaaa646a1ed6fd7210dbedf67c113dd245f425e", size = 3745148, upload-time = "2025-09-19T00:57:38Z" },
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