Auto-end PipelineRunner.run() when all root workers finish

run() now defaults to auto_end=True: the runner ends once every root
worker has finished, so single-pipeline bots end naturally when their
pipeline does and tests no longer need an explicit runner.end() call.
Multi-worker bots whose helpers run forever still trigger shutdown via
end() / cancel() from an event handler (typically on transport
disconnect). Hosts that add and remove workers across many sessions can
pass auto_end=False to keep the runner up.

.github/workflows/coverage.yaml

@@ -41,7 +41,9 @@ jobs:
             --extra google \
             --extra langchain \
             --extra livekit \
+            --extra pgmq \
             --extra piper \
+            --extra redis \
             --extra runner \
             --extra sagemaker \
             --extra tracing \

.github/workflows/tests.yaml

@@ -45,7 +45,9 @@ jobs:
             --extra google \
             --extra langchain \
             --extra livekit \
+            --extra pgmq \
             --extra piper \
+            --extra redis \
             --extra runner \
             --extra sagemaker \
             --extra tracing \

changelog/4493.added.md

@@ -0,0 +1 @@
+- Added `pipecat.workers`, a worker-based agent framework folded in from the standalone `pipecat-subagents` package. Workers inherit from `BaseWorker`, share a `WorkerBus`, register in a `WorkerRegistry`, and exchange typed work via `@job` handlers. `LLMWorker` and `LLMContextWorker` provide ready-made LLM-driven workers. `PipelineRunner.spawn(worker)` registers fire-and-forget workers alongside the main pipeline worker.

changelog/4493.changed.2.md

@@ -0,0 +1 @@
+- ⚠️ `FrameProcessorSetup.pipeline_worker` and `FunctionCallParams.pipeline_worker` are now mandatory fields, and `FrameProcessor.pipeline_worker` raises if read before `setup()` instead of returning `None`. Real-world code (frame processors set up by `PipelineWorker`, tool handlers invoked by `LLMService`) is unaffected; only callers that construct these dataclasses by hand (typically tests) now have to supply a `pipeline_worker` reference.

changelog/4493.changed.3.md

@@ -0,0 +1 @@
+- `PipelineRunner.run()` now ends automatically once every root worker has finished, so single-pipeline bots no longer need an explicit `runner.end()` / `runner.cancel()` call. Multi-worker bots whose helpers run forever (waiting for bus messages) still trigger shutdown by calling `end()` / `cancel()` from an event handler (typically on transport disconnect). Pass `auto_end=False` to `run()` for long-lived hosts (e.g. a FastAPI server) that add and remove workers across many sessions.

changelog/4493.changed.md

@@ -0,0 +1 @@
+- `PipelineWorker` now inherits from `BaseWorker`, so every pipeline worker is also a bus participant. It accepts a new optional `bridged=()` parameter that auto-wraps the pipeline with bus edge processors, letting the worker exchange frames with other bridged workers over the shared `WorkerBus`. The bus is supplied by `PipelineRunner` via `worker.attach(registry=..., bus=...)` instead of through the constructor.

changelog/4493.deprecated.md

@@ -0,0 +1 @@
+- Passing a worker to `PipelineRunner.run()` is deprecated. Register the worker with `PipelineRunner.add_workers()` before calling `run()` instead. The `worker` argument still works but emits a `DeprecationWarning` and will be removed in a future release.

examples/audio/audio-bot-background-sound.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, MixerEnableFrame, MixerUpdateSettingsFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -105,7 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -120,27 +120,28 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Listening for background sound for a bit...")
         await asyncio.sleep(5.0)
         logger.info(f"Reducing volume...")
-        await task.queue_frame(MixerUpdateSettingsFrame({"volume": 0.5}))
+        await worker.queue_frame(MixerUpdateSettingsFrame({"volume": 0.5}))
         await asyncio.sleep(5.0)
         logger.info(f"Disabling background sound for a bit...")
-        await task.queue_frame(MixerEnableFrame(False))
+        await worker.queue_frame(MixerEnableFrame(False))
         await asyncio.sleep(5.0)
         logger.info(f"Re-enabling background sound and starting bot...")
-        await task.queue_frame(MixerEnableFrame(True))
+        await worker.queue_frame(MixerEnableFrame(True))
         # Kick off the conversation.
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/audio/audio-recording.py

@@ -54,7 +54,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -146,7 +146,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -161,12 +161,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Start recording audio
         await audiobuffer.start_recording()
         # Start conversation - empty prompt to let LLM follow system instructions
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     # Handler for merged audio
     @audiobuffer.event_handler("on_audio_data")
@@ -191,7 +191,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/audio/audio-sound-effects.py

@@ -20,7 +20,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -144,7 +144,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
@@ -153,17 +153,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frame(TTSSpeakFrame("Hi, I'm listening!"))
+        await worker.queue_frame(TTSSpeakFrame("Hi, I'm listening!"))
         await transport.send_audio(sounds["ding1.wav"])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/context-summarization/context-summarization-dedicated-llm.py

@@ -26,7 +26,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
 from pipecat.processors.aggregators.llm_response_universal import (
@@ -198,7 +198,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -214,16 +214,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/context-summarization/context-summarization-google.py

@@ -24,7 +24,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
 from pipecat.processors.aggregators.llm_response_universal import (
@@ -159,7 +159,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -175,16 +175,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/context-summarization/context-summarization-manual-openai.py

@@ -26,7 +26,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, LLMSummarizeContextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -133,7 +133,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -149,16 +149,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/context-summarization/context-summarization-openai.py

@@ -24,7 +24,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_context_summarizer import SummaryAppliedEvent
 from pipecat.processors.aggregators.llm_response_universal import (
@@ -159,7 +159,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -175,16 +175,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-add-tool-change-messages.py

@@ -56,7 +56,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import NOT_GIVEN, LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -163,7 +163,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
@@ -185,13 +185,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "=== Phase 1: weather tool REMOVED. Keep asking about the weather "
                 "to exercise hallucination scenarios. ==="
             )
-            await task.queue_frame(LLMSetToolsFrame(tools=NOT_GIVEN))
+            await worker.queue_frame(LLMSetToolsFrame(tools=NOT_GIVEN))
         elif user_turn_count == READD_AT_TURN - 1:
             logger.info(
                 "=== Phase 2: weather tool RE-ADDED. Ask for the weather again — "
                 "does the LLM call it, or keep refusing? (THIS IS THE TEST.) ==="
             )
-            await task.queue_frame(LLMSetToolsFrame(tools=weather_tools))
+            await worker.queue_frame(LLMSetToolsFrame(tools=weather_tools))
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
@@ -209,15 +209,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 ),
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-app-resources.py

@@ -4,27 +4,27 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Example demonstrating ``PipelineTask(app_resources=...)``.
+"""Example demonstrating ``PipelineWorker(app_resources=...)``.
 
 ``app_resources`` is an application-defined bag of anything your
 application code may want to share across a session: database handles,
 HTTP clients, feature flags, per-user state, observability clients,
 in-memory caches — whatever fits your app. Pipecat passes it through
-untouched and exposes it as ``task.app_resources``, so any code with a
-handle on the task can read or mutate it.
+untouched and exposes it as ``worker.app_resources``, so any code with a
+handle on the worker can read or mutate it.
 
 Two of the convenience aliases exercised below:
 
 - Tool handlers read it from ``FunctionCallParams.app_resources``.
 - Custom ``FrameProcessor`` subclasses read it from
-  ``self.pipeline_task.app_resources``.
+  ``self.pipeline_worker.app_resources``.
 
 This example uses two small loggers as stand-ins for that "shared thing":
 ``ToolCallLogger`` (written from tool handlers) and
 ``TranscriptionLogger`` (written from a custom ``FrameProcessor`` that
 sits in the pipeline). A real app might just as easily pass a Postgres
 pool, a Redis client, a Stripe SDK instance, or any combination thereof.
-The mechanics shown here — construct once, hand to the task, read it
+The mechanics shown here — construct once, hand to the worker, read it
 from each site, inspect it after the session — are the same regardless
 of what you put in.
 
@@ -50,7 +50,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, LLMRunFrame, TranscriptionFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -131,7 +131,7 @@ class AppResources:
     get autocomplete and refactor safety:
 
     - In tools: ``cast(AppResources, params.app_resources)``.
-    - In custom processors: ``cast(AppResources, self.pipeline_task.app_resources)``.
+    - In custom processors: ``cast(AppResources, self.pipeline_worker.app_resources)``.
     """
 
     tool_call_logger: ToolCallLogger
@@ -155,8 +155,8 @@ class TranscriptionLoggingProcessor(FrameProcessor):
 
     Demonstrates the second read site for ``app_resources``: any custom
     ``FrameProcessor`` can reach the same bag every tool handler sees by
-    going through ``self.pipeline_task.app_resources``. ``pipeline_task``
-    is ``None`` until the task sets the processor up, so we guard against
+    going through ``self.pipeline_worker.app_resources``. ``pipeline_worker``
+    is ``None`` until the worker sets the processor up, so we guard against
     that case.
     """
 
@@ -164,8 +164,8 @@ class TranscriptionLoggingProcessor(FrameProcessor):
         """Forward all frames; log final user transcriptions on the way through."""
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, TranscriptionFrame) and self.pipeline_task is not None:
-            resources = cast(AppResources, self.pipeline_task.app_resources)
+        if isinstance(frame, TranscriptionFrame) and self.pipeline_worker is not None:
+            resources = cast(AppResources, self.pipeline_worker.app_resources)
             resources.transcription_logger.log_transcription(frame.text)
 
         await self.push_frame(frame, direction)
@@ -282,7 +282,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         transcription_logger=transcription_logger,
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -299,16 +299,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
     # The session has ended; read whatever state the handlers built up.
     logger.info(f"Tool calls logged during session:\n{tool_call_logger.dump()}")

examples/features/features-before-and-after-events.py

@@ -14,7 +14,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import DataFrame, LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -97,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -124,16 +124,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
         # Custom frames are pushed in order so they can be used for synchronization purposes.
-        await task.queue_frames([CustomBeforeProcessFrame(), LLMRunFrame(), CustomAfterPushFrame()])
+        await worker.queue_frames(
+            [CustomBeforeProcessFrame(), LLMRunFrame(), CustomAfterPushFrame()]
+        )
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-concurrent-llm-evaluation.py

@@ -15,7 +15,7 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -130,7 +130,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -149,16 +149,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         groq_context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-concurrent-llm-rtvi-ignored-sources.py

@@ -21,7 +21,7 @@ from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -141,7 +141,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -160,16 +160,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         evaluator_context.add_message(
             {"role": "developer", "content": "Ready to evaluate user messages."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-custom-frame-processor.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -128,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -144,16 +144,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-gpu-container-local-bot.py

@@ -14,7 +14,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -95,7 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -112,7 +112,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     # Handle "latency-ping" messages. The client will send app messages that look like
     # this:
@@ -128,13 +128,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 logger.debug(f"Received latency ping app message: {message}")
                 ts = message["latency-ping"]["ts"]
                 # Send immediately
-                await task.queue_frame(
+                await worker.queue_frame(
                     DailyOutputTransportMessageUrgentFrame(
                         message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                     )
                 )
                 # And push to the pipeline for the Daily transport.output to send
-                await task.queue_frame(
+                await worker.queue_frame(
                     DailyOutputTransportMessageFrame(
                         message={"latency-pong-pipeline-delivery": {"ts": ts}},
                         participant_id=sender,
@@ -146,11 +146,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-live-translation.py

@@ -14,7 +14,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -99,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -111,7 +111,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
-        await task.queue_frames(
+        await worker.queue_frames(
             [
                 TTSSpeakFrame(
                     text="Hello, welcome to live translation. Everything you say will be automatically translated to Spanish. Let's begin!",
@@ -123,11 +123,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-pattern-pair-voice-switching.py

@@ -48,7 +48,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -216,7 +216,7 @@ Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -229,15 +229,16 @@ Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Start conversation - empty prompt to let LLM follow system instructions
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-service-switcher.py

@@ -18,7 +18,7 @@ from pipecat.pipeline.llm_switcher import LLMSwitcher
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.service_switcher import ServiceSwitcher
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -151,7 +151,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -167,25 +167,26 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
         await asyncio.sleep(15)
         print(f"Switching to {stt_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
+        await worker.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 worker.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
         await asyncio.sleep(15)
         print(f"Switching to {tts_deepgram}")
-        await task.queue_frames([ManuallySwitchServiceFrame(service=tts_deepgram)])
+        await worker.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()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-switch-languages.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import Frame, LLMRunFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -147,7 +147,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -166,16 +166,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {tts.current_language}.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-switch-voices.py

@@ -17,7 +17,7 @@ from pipecat.frames.frames import Frame, LLMRunFrame
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -157,7 +157,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -176,16 +176,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {tts.current_voice}.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-user-email-gathering.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -125,7 +125,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -138,15 +138,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Start conversation - empty prompt to let LLM follow system instructions
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-voicemail-detection.py

@@ -14,7 +14,7 @@ from pipecat.extensions.voicemail.voicemail_detector import VoicemailDetector
 from pipecat.frames.frames import TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -91,7 +91,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -107,7 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     @voicemail.event_handler("on_conversation_detected")
     async def on_conversation_detected(processor):
@@ -130,7 +130,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/features/features-wake-phrase.py

@@ -13,7 +13,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -107,7 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -123,16 +123,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-anthropic-async-stream.py

@@ -38,7 +38,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -171,7 +171,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -186,16 +186,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-anthropic-async.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -140,7 +140,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -156,16 +156,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-anthropic-video.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -168,16 +168,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-anthropic.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -125,7 +125,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -141,16 +141,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-aws-video.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -148,7 +148,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -173,16 +173,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user briefly; don't mention the camera. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-aws.py

@@ -14,7 +14,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -134,7 +134,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -150,16 +150,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-azure.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -120,7 +120,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -133,16 +133,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-cerebras.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -129,7 +129,7 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -142,16 +142,17 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-deepseek.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -130,7 +130,7 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -143,16 +143,17 @@ Start by asking me for my location. Then, use 'get_weather_current' to give me a
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-direct.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -121,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -134,16 +134,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-fireworks.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -123,7 +123,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -139,16 +139,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-google-async-stream.py

@@ -38,7 +38,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -175,7 +175,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -190,16 +190,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-google-async.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -208,7 +208,7 @@ indicate you should use the get_image tool are:
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -232,16 +232,17 @@ indicate you should use the get_image tool are:
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-google-vertex.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -121,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -140,16 +140,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": "Please introduce yourself to the user.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-google-video.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -168,16 +168,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-google.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -200,7 +200,7 @@ indicate you should use the get_image tool are:
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -224,16 +224,17 @@ indicate you should use the get_image tool are:
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-grok.py

@@ -17,7 +17,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -117,7 +117,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             ]
         )
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             params=PipelineParams(
                 enable_metrics=True,
@@ -133,16 +133,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             context.add_message(
                 {"role": "developer", "content": "Please introduce yourself to the user."}
             )
-            await task.queue_frames([LLMRunFrame()])
+            await worker.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-groq.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -118,7 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -131,16 +131,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-inception.py

@@ -162,7 +162,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(task)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-missing-handler.py

@@ -48,7 +48,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -141,7 +141,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
@@ -164,15 +164,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 ),
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info("Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-mistral.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -131,7 +131,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -144,16 +144,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-moondream-video.py

@@ -24,7 +24,7 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -185,7 +185,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
@@ -206,16 +206,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-nebius.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -135,7 +135,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -151,16 +151,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-novita.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -136,7 +136,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -149,16 +149,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-nvidia.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -122,7 +122,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -135,16 +135,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-ollama.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -136,7 +136,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -149,16 +149,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-async-stream.py

@@ -38,7 +38,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -175,7 +175,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -190,16 +190,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-async.py

@@ -19,7 +19,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -153,7 +153,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -168,16 +168,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses-async-stream.py

@@ -38,7 +38,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -175,7 +175,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -187,16 +187,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses-async.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -157,7 +157,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -173,16 +173,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses-http.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -135,7 +135,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -151,16 +151,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses-video-http.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -167,12 +167,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     @tts.event_handler("on_tts_request")
     async def on_tts_request(tts, context_id: str, text: str):
@@ -180,7 +180,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses-video.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -167,12 +167,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     @tts.event_handler("on_tts_request")
     async def on_tts_request(tts, context_id: str, text: str):
@@ -180,7 +180,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-responses.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -159,16 +159,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai-video.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -143,7 +143,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -167,12 +167,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     @tts.event_handler("on_tts_request")
     async def on_tts_request(tts, context_id: str, text: str):
@@ -180,7 +180,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openai.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -136,7 +136,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -148,16 +148,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-openrouter.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -123,7 +123,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -136,16 +136,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-perplexity.py

@@ -20,7 +20,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -92,7 +92,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -108,16 +108,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-qwen.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -121,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -134,16 +134,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-sambanova.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -120,7 +120,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -133,16 +133,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-sarvam.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -140,7 +140,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -156,16 +156,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/function-calling/function-calling-together.py

@@ -16,7 +16,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -118,7 +118,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -134,16 +134,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/01-say-one-thing.py

@@ -12,7 +12,7 @@ from loguru import logger
 from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -42,7 +42,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ),
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         Pipeline([tts, transport.output()]),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
@@ -50,11 +50,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        await task.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
+        await worker.queue_frames([TTSSpeakFrame(f"Hello there!"), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/01a-local-audio.py

@@ -14,7 +14,7 @@ from loguru import logger
 from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
@@ -36,15 +36,16 @@ async def main():
 
     pipeline = Pipeline([tts, transport.output()])
 
-    task = PipelineTask(pipeline)
+    worker = PipelineWorker(pipeline)
 
     async def say_something():
         await asyncio.sleep(1)
-        await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
+        await worker.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
 
-    await asyncio.gather(runner.run(task), say_something())
+    await runner.add_workers(worker)
+    await asyncio.gather(runner.run(), say_something())
 
 
 if __name__ == "__main__":

examples/getting-started/02-llm-say-one-thing.py

@@ -12,7 +12,7 @@ from loguru import logger
 from pipecat.frames.frames import EndFrame, LLMContextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -51,7 +51,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ),
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         Pipeline([llm, tts, transport.output()]),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
@@ -61,11 +61,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         context = LLMContext()
         context.add_message({"role": "developer", "content": "Say hello to the world."})
-        await task.queue_frames([LLMContextFrame(context), EndFrame()])
+        await worker.queue_frames([LLMContextFrame(context), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/03-still-frame.py

@@ -12,7 +12,7 @@ from loguru import logger
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.google.image import GoogleImageGenService
@@ -45,7 +45,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         api_key=os.environ["GOOGLE_API_KEY"],
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         Pipeline([imagegen, transport.output()]),
         params=PipelineParams(
             enable_metrics=True,
@@ -57,18 +57,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Register an event handler so we can play the audio when the client joins
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        await task.queue_frame(TextFrame("a cat in the style of picasso"))
-        await task.queue_frame(TextFrame("a dog in the style of picasso"))
-        await task.queue_frame(TextFrame("a fish in the style of picasso"))
+        await worker.queue_frame(TextFrame("a cat in the style of picasso"))
+        await worker.queue_frame(TextFrame("a dog in the style of picasso"))
+        await worker.queue_frame(TextFrame("a fish in the style of picasso"))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/03a-local-still-frame.py

@@ -16,7 +16,7 @@ from loguru import logger
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.services.fal.image import FalImageGenService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
@@ -46,18 +46,19 @@ async def main():
 
         pipeline = Pipeline([imagegen, transport.output()])
 
-        task = PipelineTask(pipeline)
-        await task.queue_frames([TextFrame("a cat in the style of picasso")])
+        worker = PipelineWorker(pipeline)
+        await worker.queue_frames([TextFrame("a cat in the style of picasso")])
 
         runner = PipelineRunner()
 
         async def run_tk():
-            while not task.has_finished():
+            while not worker.has_finished():
                 tk_root.update()
                 tk_root.update_idletasks()
                 await asyncio.sleep(0.1)
 
-        await asyncio.gather(runner.run(task), run_tk())
+        await runner.add_workers(worker)
+        await asyncio.gather(runner.run(), run_tk())
 
 
 if __name__ == "__main__":

examples/getting-started/04-sync-speech-and-image.py

@@ -22,7 +22,7 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.sync_parallel_pipeline import FrameOrder, SyncParallelPipeline
-from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.worker import PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -186,7 +186,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             frames.append(MonthFrame(month=month))
             frames.append(LLMContextFrame(LLMContext(messages)))
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
         )
@@ -196,16 +196,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected")
             # Start the month narration once connected
-            await task.queue_frames(frames)
+            await worker.queue_frames(frames)
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         # Run the pipeline
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/05-speaking-state.py

@@ -20,7 +20,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -136,7 +136,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -149,15 +149,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/06-voice-agent.py

@@ -13,7 +13,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -85,7 +85,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -101,16 +101,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/getting-started/06a-voice-agent-local.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -74,7 +74,7 @@ async def main():
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -83,11 +83,12 @@ async def main():
     )
 
     context.add_message({"role": "developer", "content": "Please introduce yourself to the user."})
-    await task.queue_frames([LLMRunFrame()])
+    await worker.queue_frames([LLMRunFrame()])
 
     runner = PipelineRunner()
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 if __name__ == "__main__":

examples/getting-started/07-function-calling.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 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.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -135,7 +135,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -151,16 +151,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.add_workers(worker)
+    await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/mcp/mcp-multiple-mcp.py

@@ -18,7 +18,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -130,7 +130,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             ]
         )
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             params=PipelineParams(
                 enable_metrics=True,
@@ -143,16 +143,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected: {client}")
             # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
+            await worker.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/mcp/mcp-stdio.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -108,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             ]
         )
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             params=PipelineParams(
                 enable_metrics=True,
@@ -121,16 +121,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected: {client}")
             # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
+            await worker.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/mcp/mcp-streamable-http-gemini-live.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -97,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             ]
         )
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             params=PipelineParams(
                 enable_metrics=True,
@@ -110,16 +110,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected: {client}")
             # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
+            await worker.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/mcp/mcp-streamable-http.py

@@ -15,7 +15,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -112,7 +112,7 @@ Just respond with short sentences when you are carrying out tool calls.
             ]
         )
 
-        task = PipelineTask(
+        worker = PipelineWorker(
             pipeline,
             params=PipelineParams(
                 enable_metrics=True,
@@ -125,16 +125,17 @@ Just respond with short sentences when you are carrying out tool calls.
         async def on_client_connected(transport, client):
             logger.info(f"Client connected: {client}")
             # Kick off the conversation.
-            await task.queue_frames([LLMRunFrame()])
+            await worker.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")
         async def on_client_disconnected(transport, client):
             logger.info(f"Client disconnected")
-            await task.cancel()
+            await worker.cancel()
 
         runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+        await runner.add_workers(worker)
+        await runner.run()
 
 
 async def bot(runner_args: RunnerArguments):

examples/multi-worker/README.md

@@ -0,0 +1,369 @@
+# Pipecat Multi-Worker Examples
+
+This directory contains example bots that use the multi-worker framework in `pipecat.workers`, `pipecat.pipeline.runner` (with `add_workers()`), and the `WorkerBus`. Each example shows a different cooperation pattern between workers: hand-off, parallel fan-out, remote workers, etc.
+
+## Setup
+
+From the repo root:
+
+```bash
+uv sync --all-extras
+source .venv/bin/activate
+cd examples/multi-worker
+```
+
+Copy the env template and fill in your API keys:
+
+```bash
+cp env.example .env
+```
+
+## Environment variables
+
+| Variable           | Required by                             |
+| ------------------ | --------------------------------------- |
+| `OPENAI_API_KEY`   | LLM workers                               |
+| `DEEPGRAM_API_KEY` | STT                                     |
+| `CARTESIA_API_KEY` | TTS                                     |
+| `DAILY_API_KEY`    | Optional: only with `--transport daily` |
+
+Additional, example-specific variables are listed below.
+
+## Table of contents
+
+**[Local](#local)** (single process)
+
+- [Handoff between LLM workers](#handoff-between-llm-tasks)
+- [Parallel debate](#parallel-debate)
+- [Voice code assistant with Claude Agent SDK](#voice-code-assistant)
+- [Sensor controller](#sensor-controller)
+
+**[Distributed](#distributed)** (multi-process)
+
+- [Handoff via Redis](#handoff-via-redis)
+- [Handoff via PGMQ (Postgres)](#handoff-via-pgmq-postgres)
+- [LLM worker via WebSocket proxy](#llm-task-via-websocket-proxy)
+
+# Local
+
+Examples where all workers run in the same process on an `AsyncQueueBus`.
+
+## Handoff between LLM workers
+
+Two LLM workers (greeter + support) that transfer control to each other during a voice conversation. A main worker owns the transport pipeline and bridges frames to the bus.
+
+### Running
+
+```bash
+uv run local-handoff/local-handoff-two-agents.py
+```
+
+Open <http://localhost:7860/client> in your browser to talk to your bot.
+
+To use Daily transport:
+
+```bash
+uv run local-handoff/local-handoff-two-agents.py --transport daily
+```
+
+### Overview
+
+- **[`local-handoff-two-agents.py`](local-handoff/local-handoff-two-agents.py)** — Two LLM workers (greeter + support) that hand off via `activate_worker(..., deactivate_self=True)`. The main worker owns STT, TTS, transport, and a `BusBridgeProcessor`.
+- **[`local-handoff-two-agents-tts.py`](local-handoff/local-handoff-two-agents-tts.py)** — Same shape, but each child worker ships with its own `CartesiaTTSService` in a custom pipeline. The main worker has no TTS — audio comes from whichever child is active over the bus.
+
+## Parallel debate
+
+Parallel fan-out using `worker.job_group(...)`. A voice bot takes a topic from the user, kicks off three workers in parallel (advocate, critic, analyst), waits for all three to respond, and synthesizes a balanced answer. Each worker keeps its own LLM context across rounds.
+
+### Running
+
+```bash
+uv run parallel-debate/parallel-debate.py
+```
+
+Open <http://localhost:7860/client> in your browser to talk to your bot.
+
+To use Daily transport:
+
+```bash
+uv run parallel-debate/parallel-debate.py --transport daily
+```
+
+### Architecture
+
+```
+Main worker (transport + LLM + `debate` tool)
+  └── job_group(advocate, critic, analyst)
+        └── DebateWorker (LLMContextWorker, one per role)
+```
+
+- **Main worker**: transport (STT, TTS) + LLM moderator with a `debate` direct function that fans out via `worker.job_group(...)`.
+- **Debate workers**: `LLMContextWorker`s spawned on the runner. Each keeps its own `LLMContext` across rounds and ships its completed turn back as a job response via the assistant-aggregator's `on_assistant_turn_stopped` event.
+
+## Voice code assistant
+
+Talk to your codebase hands-free. Ask questions about code, project structure, or file contents and get spoken answers based on actual files. The Claude Agent SDK worker navigates the filesystem using `Read`, `Bash`, `Glob`, and `Grep` tools.
+
+### Additional environment variables
+
+| Variable            | Required by                    |
+| ------------------- | ------------------------------ |
+| `ANTHROPIC_API_KEY` | Code worker (Claude Agent SDK) |
+| `PROJECT_PATH`      | Optional, defaults to cwd      |
+
+### Running
+
+```bash
+# Default: explores the current directory
+uv run code-assistant/code-assistant.py
+
+# Specify a project path
+PROJECT_PATH=/path/to/your/project uv run code-assistant/code-assistant.py
+```
+
+Open <http://localhost:7860/client> in your browser to talk to your bot.
+
+To use Daily transport:
+
+```bash
+uv run code-assistant/code-assistant.py --transport daily
+```
+
+### Example questions
+
+- "What does the main module do?"
+- "Find all TODO comments in the project"
+- "How is error handling implemented?"
+- "What dependencies does this project use?"
+- "Explain the test structure"
+
+### Architecture
+
+```
+Main worker (transport + LLM + `ask_code` tool)
+  └── job → CodeWorker (Claude Agent SDK)
+```
+
+- **`code-assistant.py`** — Main worker: STT, LLM (with system prompt + `ask_code` direct function), TTS, and transport. The `ask_code` tool dispatches a job to the worker via `worker.job("code_worker", payload=...)`.
+- **`code_worker.py`** — `CodeWorker`: a bus-only `BaseWorker` spawned on the runner. It accepts `@job`-style requests through the bus and runs them sequentially through a persistent Claude SDK session so follow-up questions share context.
+
+## Sensor controller
+
+Two `PipelineWorker`s side by side, communicating only over job RPC. A voice agent has a single `ask_controller(question)` tool that forwards every temperature-related request to a worker; the worker owns a simulated thermometer and its own tool-calling LLM that decides how to answer (read the current value, inspect rolling stats, change the target, change the response rate). The worker is a plain `PipelineWorker` — it does not subclass `LLMWorker` and is not bridged.
+
+### Running
+
+```bash
+uv run sensor-controller/sensor-controller.py
+```
+
+Open <http://localhost:7860/client> in your browser to talk to your bot.
+
+To use Daily transport:
+
+```bash
+uv run sensor-controller/sensor-controller.py --transport daily
+```
+
+### Example questions
+
+- "What's the temperature?"
+- "Make it warmer."
+- "Is it stable yet?"
+- "Why is it slow?" / "Speed up the response."
+- "What was the highest reading?"
+
+### Architecture
+
+```
+Voice agent (transport + STT + LLM + TTS, tool: ask_controller)
+  └── job → Controller (PipelineWorker)
+              └── SensorReader -> SensorStats -> user_agg -> llm -> assistant_agg
+```
+
+- **[`sensor-controller.py`](sensor-controller/sensor-controller.py)** — `build_sensor_controller()` returns a plain `PipelineWorker`. Jobs arrive via `@worker.event_handler("on_job_request")`, the question is queued onto the worker LLM, and the LLM's reply is paired back to the job via the assistant aggregator's `on_assistant_turn_stopped` event.
+- **[`sensor.py`](sensor-controller/sensor.py)** — Two custom `FrameProcessor` subclasses: `SensorReader` runs an autonomous tick loop that emits a `SensorReadingFrame` each second (first-order lag toward target plus Gaussian noise; mutable target and response rate); `SensorStats` maintains rolling min/max/avg/trend.
+
+# Distributed
+
+Examples where workers run across separate processes or machines.
+
+## Handoff via Redis
+
+Same two-worker handoff as the local example, but each worker runs as a separate process connected via Redis pub/sub. Requires `pip install pipecat-ai[redis]`.
+
+### Quick start (single machine, local Redis)
+
+_Terminal 1_: start Redis
+
+```bash
+docker run --rm -p 6379:6379 redis:7
+```
+
+_Terminal 2_: start the greeter worker
+
+```bash
+uv run distributed-handoff/redis-handoff/llm.py greeter
+```
+
+_Terminal 3_: start the support worker
+
+```bash
+uv run distributed-handoff/redis-handoff/llm.py support
+```
+
+_Terminal 4_: start the main transport worker
+
+```bash
+uv run distributed-handoff/redis-handoff/main.py
+```
+
+All processes connect to `redis://localhost:6379` by default.
+
+### Running across machines
+
+Point each process at the same Redis instance:
+
+_Machine A_
+
+```bash
+uv run distributed-handoff/redis-handoff/main.py --redis-url redis://your-redis-host:6379
+```
+
+_Machine B_
+
+```bash
+uv run distributed-handoff/redis-handoff/llm.py greeter --redis-url redis://your-redis-host:6379
+```
+
+_Machine C_
+
+```bash
+uv run distributed-handoff/redis-handoff/llm.py support --redis-url redis://your-redis-host:6379
+```
+
+### Architecture
+
+```
+Machine A                    Redis                    Machine B
++------------+          +-------------+          +-------------+
+| main.py    |  <---->  | pub/sub     |  <---->  |    llm.py   |
+| (transport,|          | channel:    |          |  (greeter)  |
+|  STT, TTS) |          | pipecat:acme|          +-------------+
++------------+          +-------------+          +-------------+
+                               ^                 |    llm.py   |
+                               +-------------->  |  (support)  |
+                                                 +-------------+
+```
+
+- **[main.py](distributed-handoff/redis-handoff/main.py)** — Transport worker: Daily/WebRTC, Deepgram STT, Cartesia TTS, and a `BusBridgeProcessor` over a `RedisBus`.
+- **[llm.py](distributed-handoff/redis-handoff/llm.py)** — LLM worker: runs either `greeter` or `support` with OpenAI behind a bridged `LLMWorker`.
+
+## Handoff via PGMQ (Postgres)
+
+Same shape as the Redis handoff, but the bus is backed by [PGMQ](https://github.com/tembo-io/pgmq) on a shared Postgres database (e.g. Supabase). Requires `pip install pipecat-ai[pgmq]`.
+
+### Additional environment variables
+
+| Variable       | Required by                                                          |
+| -------------- | -------------------------------------------------------------------- |
+| `DATABASE_URL` | PostgreSQL DSN (e.g. Supabase pooled connection string)              |
+| `PGMQ_CHANNEL` | Optional, channel prefix for queue names. Defaults to `pipecat_acme` |
+
+### Quick start
+
+_Terminal 1_: start the greeter worker
+
+```bash
+uv run distributed-handoff/pgmq-handoff/llm.py greeter --database-url $DATABASE_URL
+```
+
+_Terminal 2_: start the support worker
+
+```bash
+uv run distributed-handoff/pgmq-handoff/llm.py support --database-url $DATABASE_URL
+```
+
+_Terminal 3_: start the main transport worker
+
+```bash
+uv run distributed-handoff/pgmq-handoff/main.py --database-url $DATABASE_URL
+```
+
+You can also set `DATABASE_URL` in `.env` and omit the `--database-url` flag.
+
+### Architecture
+
+Same as the Redis handoff above; the `RedisBus` is replaced by a `PgmqBus`, and the "pub/sub channel" is a set of PGMQ queues on the shared Postgres instance.
+
+## LLM worker via WebSocket proxy
+
+Runs an LLM worker on a remote server, connected to the main transport worker via a WebSocket proxy. No shared bus required — the proxy workers forward bus messages point-to-point over the WebSocket.
+
+### Quick start (single machine)
+
+_Terminal 1_: start the remote assistant server
+
+```bash
+uv run remote-proxy-assistant/assistant.py
+```
+
+_Terminal 2_: start the main transport worker
+
+```bash
+uv run remote-proxy-assistant/main.py --remote-url ws://localhost:8765/ws
+```
+
+Open <http://localhost:7860/client> in your browser to talk to the bot.
+
+### Running across machines
+
+_Server machine_: start the assistant
+
+```bash
+uv run remote-proxy-assistant/assistant.py --host 0.0.0.0 --port 8765
+```
+
+_Client machine_: point at the server
+
+```bash
+uv run remote-proxy-assistant/main.py --remote-url ws://server-host:8765/ws
+```
+
+### Architecture
+
+```
+    +-------------+    +-------------+           +-------------+     +-----------------+
+    |             |    |             |           |             |     |                 |
+    |  Main worker  |    | Proxy worker  |  <~~~~~>  | Proxy worker  |     | Assistant worker  |
+    |             |    |  (client)   |           |  (server)   |     |                 |
+    +-------------+    +-------------+           +-------------+     +-----------------+
+        messages           messages                  messages             messages
+            │                 │                         │                    │
+  ══════════╧═════════════════╧════════         ════════╧════════════════════╧═══════════
+                Task Bus                                       Task Bus
+  ═════════════════════════════════════         ═════════════════════════════════════════
+```
+
+- **[main.py](remote-proxy-assistant/main.py)** — Transport worker with STT, TTS, and a `BusBridge`. Spawns a `WebSocketProxyClientTask` that connects to the remote server and forwards `BusFrameMessage`s.
+- **[assistant.py](remote-proxy-assistant/assistant.py)** — FastAPI server. Each WebSocket connection spawns a `WebSocketProxyServerTask` plus a bridged `AcmeAssistant` LLM worker on a per-session `PipelineRunner`.
+
+### Security
+
+The proxy workers filter messages by worker name:
+
+- Only messages targeted at the remote worker cross the WebSocket
+- Only messages targeted at the local worker are accepted from the WebSocket
+- Broadcast messages never cross the WebSocket
+
+Pass HTTP headers for authentication:
+
+```python
+proxy = WebSocketProxyClientTask(
+    "proxy",
+    url="wss://server-host:8765/ws",
+    remote_worker_name="assistant",
+    local_worker_name="acme",
+    headers={"Authorization": "Bearer <token>"},
+)
+```

examples/multi-worker/code-assistant/code-assistant.py

@@ -0,0 +1,178 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Voice code assistant powered by Claude Agent SDK.
+
+Talk to your codebase hands-free. Ask questions like "what does the
+auth middleware do?" or "find all TODO comments" and get spoken answers
+based on actual file contents. The Claude Agent SDK worker navigates
+the filesystem using Read, Bash, Glob, and Grep tools.
+
+Architecture::
+
+    Main worker (transport + LLM + ``ask_code`` tool)
+      └── job → CodeWorker (Claude Agent SDK)
+
+Requirements:
+
+- ANTHROPIC_API_KEY
+- OPENAI_API_KEY
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DAILY_API_KEY (for Daily transport)
+"""
+
+import os
+
+from code_worker import CodeWorker
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import 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 DailyParams
+
+load_dotenv(override=True)
+
+PROJECT_PATH = os.getenv("PROJECT_PATH", os.getcwd())
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def ask_code(params: FunctionCallParams, question: str):
+    """Ask a question about the codebase. A Claude Code worker will
+    explore the project by reading files, searching code, and running
+    commands. It remembers previous questions for follow-ups.
+
+    Args:
+        question (str): The question about code, files, structure,
+            dependencies, or anything in the project.
+    """
+    logger.info(f"Asking code worker: '{question}'")
+    async with params.pipeline_worker.job("code_worker", payload={"question": question}) as job:
+        await params.llm.queue_frame(
+            LLMMessagesAppendFrame(
+                messages=[{"role": "developer", "content": "Give me a moment."}],
+                run_llm=True,
+            )
+        )
+        # The LLM keeps talking while the worker runs.
+    await params.result_callback(job.response)
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting code assistant")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a voice interface to a code assistant powered by Claude Code. "
+                "Behind you is a worker that can read files, search code with grep and "
+                "glob patterns, and run bash commands on the project. It maintains "
+                "context across questions, so follow-up questions work naturally.\n\n"
+                "When the user asks anything about code, project structure, files, "
+                "dependencies, tests, or wants to explore the codebase, call the "
+                "ask_code tool. When the worker result comes back, summarize it naturally "
+                "for speaking. Keep responses concise and conversational.\n"
+            ),
+        ),
+    )
+
+    llm.register_direct_function(ask_code, cancel_on_interruption=False, timeout_secs=60)
+
+    context = LLMContext(tools=ToolsSchema(standard_tools=[ask_code]))
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            llm,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        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("Client connected")
+        context.add_message(
+            {
+                "role": "developer",
+                "content": "Greet the user and tell them you're a code assistant.",
+            }
+        )
+        await worker.queue_frame(LLMRunFrame())
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(CodeWorker("code_worker", project_path=PROJECT_PATH), worker)
+
+    await runner.run()
+
+
+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/multi-worker/code-assistant/code_worker.py

@@ -0,0 +1,118 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Code worker that explores a codebase using Claude Agent SDK."""
+
+import asyncio
+
+from loguru import logger
+
+from pipecat.bus import BusJobRequestMessage
+from pipecat.pipeline.base_worker import BaseWorker
+from pipecat.pipeline.job_context import JobStatus
+
+try:
+    from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error("In order to use CodeWorker, you need to `pip install claude-agent-sdk`.")
+    raise Exception(f"Missing module: {e}")
+
+
+class CodeWorker(BaseWorker):
+    """Bus-only worker that answers code questions using Claude Agent SDK.
+
+    Maintains a persistent Claude SDK session so follow-up questions
+    share context. Questions are queued and processed sequentially. The
+    worker has no Pipecat pipeline — it consumes job requests from the
+    bus and replies with job responses.
+    """
+
+    def __init__(self, name: str, *, project_path: str):
+        """Initialize the CodeWorker.
+
+        Args:
+            name: Unique worker name.
+            project_path: Filesystem path the Claude SDK should explore.
+        """
+        super().__init__(name)
+
+        self._project_path = project_path
+        self._queue: asyncio.Queue = asyncio.Queue()
+        self._worker_task: asyncio.Task | None = None
+
+        self._claude_options = ClaudeAgentOptions(
+            permission_mode="bypassPermissions",
+            system_prompt=(
+                f"You are a code assistant. The project is at: {self._project_path}\n\n"
+                "Answer the user's question by exploring the codebase. Use Read to "
+                "view files, Glob to find files by pattern, and Bash to run commands "
+                "like grep or find. Be thorough but concise in your answer. "
+                "Focus on what the user asked. Respond with a clear, spoken-friendly "
+                "summary (no markdown, no bullet points, no code blocks)."
+            ),
+            allowed_tools=["Read", "Bash", "Glob", "Grep"],
+            model="sonnet",
+            max_turns=10,
+        )
+
+    async def start(self) -> None:
+        """Launch the Claude SDK worker loop alongside the standard worker start."""
+        await super().start()
+        self._worker_task = self.create_task(self._worker_loop(), "worker")
+
+    async def stop(self) -> None:
+        """Cancel the worker loop before tearing down the worker."""
+        if self._worker_task:
+            await self.cancel_task(self._worker_task)
+            self._worker_task = None
+        await super().stop()
+
+    async def on_job_request(self, message: BusJobRequestMessage) -> None:
+        """Enqueue an incoming job for the worker loop."""
+        await super().on_job_request(message)
+        logger.info(f"Worker '{self.name}': queued '{message.payload['question']}'")
+        self._queue.put_nowait(message)
+
+    async def _worker_loop(self):
+        client = ClaudeSDKClient(options=self._claude_options)
+        try:
+            await client.connect()
+        except Exception as e:
+            logger.error(f"Worker '{self.name}': failed to start Claude SDK: {e}")
+            return
+
+        try:
+            while True:
+                message = await self._queue.get()
+                question = message.payload["question"]
+                logger.info(f"Worker '{self.name}': researching '{question}'")
+
+                try:
+                    answer = ""
+                    await client.query(prompt=question)
+                    async for msg in client.receive_response():
+                        if type(msg).__name__ == "AssistantMessage":
+                            for block in msg.content:
+                                if type(block).__name__ == "TextBlock":
+                                    answer += block.text
+
+                    logger.info(f"Worker '{self.name}': completed ({len(answer)} chars)")
+                    await self.send_job_response(message.job_id, {"answer": answer})
+
+                except Exception as e:
+                    logger.error(f"Worker '{self.name}': error: {e}")
+                    await self.send_job_response(
+                        message.job_id, {"error": str(e)}, status=JobStatus.ERROR
+                    )
+        finally:
+            # Bypass `async with ClaudeSDKClient` and call disconnect()
+            # ourselves: __aexit__ → Query.close() → _read_task.wait() uses
+            # `with suppress(asyncio.CancelledError)`, which would swallow the
+            # outer task's cancellation. By the time this finally runs, our
+            # CancelledError has already been raised once, so _must_cancel is
+            # cleared and disconnect()'s awaits proceed normally.
+            await client.disconnect()

examples/multi-worker/distributed-handoff/pgmq-handoff/llm.py

@@ -0,0 +1,183 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""LLM worker — run on Machine B (or locally alongside ``main.py``).
+
+A standalone process that runs one LLM worker (greeter or support)
+attached to the same PGMQ-backed `WorkerBus` as the main worker.
+Multiple instances can run on different machines as long as they
+share a Postgres database with the PGMQ extension enabled.
+
+Usage::
+
+    python llm.py greeter --database-url postgresql://...
+    python llm.py support --database-url postgresql://...
+
+Requirements:
+
+- OPENAI_API_KEY
+- DATABASE_URL (or ``--database-url``)
+"""
+
+import argparse
+import asyncio
+import os
+from urllib.parse import unquote, urlparse
+
+from dotenv import load_dotenv
+from loguru import logger
+from pgmq.async_queue import PGMQueue
+
+from pipecat.bus.network.pgmq import PgmqBus
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
+
+load_dotenv(override=True)
+
+WORKER_CONFIG = {
+    "greeter": {
+        "system_instruction": (
+            "You are a friendly greeter for Acme Corp. The available products "
+            "are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
+            "Tornado Kit. Ask which one they'd like to learn more about. "
+            "When the user picks a product or asks a question about one, "
+            "immediately call the transfer_to_agent tool with agent 'support'. "
+            "Do not answer product questions yourself. If the user says goodbye, "
+            "call the end_conversation tool. Do not mention transferring — just do it "
+            "seamlessly. Keep responses brief — this is a voice conversation."
+        ),
+        "watch": ["support"],
+    },
+    "support": {
+        "system_instruction": (
+            "You are a support agent for Acme Corp. You know about three "
+            "products: Acme Rocket Boots (jet-powered boots, $299, run up "
+            "to 60 mph), Acme Invisible Paint (makes anything invisible for "
+            "24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
+            "generator, $199, batteries included). Answer the user's questions "
+            "about these products. If the user wants to browse other products "
+            "or start over, call the transfer_to_agent tool with agent "
+            "'greeter'. If the user says goodbye, call the end_conversation "
+            "tool. Do not mention transferring — just do it seamlessly. "
+            "Keep responses brief — this is a voice conversation."
+        ),
+        "watch": ["greeter"],
+    },
+}
+
+
+def pgmq_from_url(database_url: str, *, pool_size: int = 4) -> PGMQueue:
+    """Build a `PGMQueue` from a Postgres DSN string."""
+    parsed = urlparse(database_url)
+    if parsed.scheme not in ("postgres", "postgresql"):
+        raise ValueError(f"Unsupported scheme '{parsed.scheme}' for database URL")
+    return PGMQueue(
+        host=parsed.hostname or "localhost",
+        port=str(parsed.port or 5432),
+        database=(parsed.path or "/postgres").lstrip("/") or "postgres",
+        username=unquote(parsed.username or "postgres"),
+        password=unquote(parsed.password or ""),
+        pool_size=pool_size,
+    )
+
+
+class AcmeLLMTask(LLMWorker):
+    """LLM worker for Acme Corp with transfer and end tools."""
+
+    def __init__(self, name: str, *, system_instruction: str, watch: list[str]):
+        """Initialize the AcmeLLMTask.
+
+        Args:
+            name: Unique worker name (``"greeter"`` or ``"support"``).
+            system_instruction: System prompt for this LLM role.
+            watch: Sibling worker names this worker will watch via the
+                registry so it knows when they become available for
+                handoff.
+        """
+        llm = OpenAILLMService(
+            name=f"{name}::OpenAILLMService",
+            api_key=os.environ["OPENAI_API_KEY"],
+            settings=OpenAILLMService.Settings(system_instruction=system_instruction),
+        )
+        super().__init__(name, llm=llm, bridged=())
+        self._watch = watch
+
+    async def start(self) -> None:
+        """Register watches for sibling workers once ready."""
+        await super().start()
+        for worker_name in self._watch:
+            await self.watch_worker(worker_name)
+
+    @tool(cancel_on_interruption=False)
+    async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
+        """Transfer the user to another agent.
+
+        Args:
+            agent (str): The agent to transfer to (e.g. 'greeter', 'support').
+            reason (str): Why the user is being transferred.
+        """
+        logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
+        await self.activate_worker(
+            agent,
+            args=LLMWorkerActivationArgs(messages=[{"role": "developer", "content": reason}]),
+            deactivate_self=True,
+            result_callback=params.result_callback,
+        )
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the conversation when the user says goodbye.
+
+        Args:
+            reason (str): Why the conversation is ending.
+        """
+        logger.info(f"Task '{self.name}': ending conversation ({reason})")
+        await self.end(
+            reason=reason,
+            messages=[{"role": "developer", "content": reason}],
+            result_callback=params.result_callback,
+        )
+
+
+async def main_async() -> None:
+    parser = argparse.ArgumentParser(description="LLM worker (greeter or support)")
+    parser.add_argument("worker", choices=list(WORKER_CONFIG), help="Which worker to run")
+    parser.add_argument(
+        "--database-url",
+        default=os.getenv("DATABASE_URL"),
+        help="PostgreSQL DSN (or set DATABASE_URL env var)",
+    )
+    parser.add_argument(
+        "--channel",
+        default=os.getenv("PGMQ_CHANNEL", "pipecat_acme"),
+        help="PGMQ channel prefix",
+    )
+    args = parser.parse_args()
+
+    if not args.database_url:
+        parser.error("--database-url is required (or set DATABASE_URL env var)")
+
+    pgmq = pgmq_from_url(args.database_url)
+    await pgmq.init()
+    bus = PgmqBus(pgmq=pgmq, channel=args.channel)
+
+    config = WORKER_CONFIG[args.worker]
+    worker = AcmeLLMTask(
+        args.worker,
+        system_instruction=config["system_instruction"],
+        watch=config["watch"],
+    )
+
+    runner = PipelineRunner(bus=bus, handle_sigint=True)
+    logger.info(f"Starting {args.worker} worker, waiting for activation...")
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main_async())

examples/multi-worker/distributed-handoff/pgmq-handoff/main.py

@@ -0,0 +1,197 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Main transport worker — run on Machine A.
+
+Handles audio I/O (STT, TTS) and bridges frames to the bus. The LLM
+workers run as separate processes (possibly on different
+machines) connected via PGMQ on a shared Postgres database
+(e.g. Supabase).
+
+Usage::
+
+    python main.py --database-url postgresql://...
+
+Requirements:
+
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DATABASE_URL (or ``--database-url``)
+"""
+
+import argparse
+import os
+from urllib.parse import unquote, urlparse
+
+from dotenv import load_dotenv
+from loguru import logger
+from pgmq.async_queue import PGMQueue
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor
+from pipecat.bus.network.pgmq import PgmqBus
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.registry.types import WorkerReadyData
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.workers.llm import LLMWorkerActivationArgs
+
+load_dotenv(override=True)
+
+MAIN_NAME = "acme"
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+def pgmq_from_url(database_url: str, *, pool_size: int = 4) -> PGMQueue:
+    """Build a `PGMQueue` from a Postgres DSN string."""
+    parsed = urlparse(database_url)
+    if parsed.scheme not in ("postgres", "postgresql"):
+        raise ValueError(f"Unsupported scheme '{parsed.scheme}' for database URL")
+    return PGMQueue(
+        host=parsed.hostname or "localhost",
+        port=str(parsed.port or 5432),
+        database=(parsed.path or "/postgres").lstrip("/") or "postgres",
+        username=unquote(parsed.username or "postgres"),
+        password=unquote(parsed.password or ""),
+        pool_size=pool_size,
+    )
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    pgmq = pgmq_from_url(runner_args.cli_args.database_url)
+    await pgmq.init()
+    bus = PgmqBus(pgmq=pgmq, channel=runner_args.cli_args.channel)
+    runner = PipelineRunner(bus=bus, handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    # The remote LLM workers may take a moment to register on the bus.
+    # We only activate ``greeter`` once *both* the client is connected
+    # and the worker has been observed via the registry.
+    state = {"client_connected": False, "greeter_ready": False}
+
+    async def maybe_activate():
+        if not (state["client_connected"] and state["greeter_ready"]):
+            return
+        await worker.activate_worker(
+            "greeter",
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, mention the available "
+                            "products and ask how you can help."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    async def on_greeter_ready(_data: WorkerReadyData) -> None:
+        state["greeter_ready"] = True
+        await maybe_activate()
+
+    await runner.registry.watch("greeter", on_greeter_ready)
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        state["client_connected"] = True
+        await maybe_activate()
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await worker.cancel()
+
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+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
+
+    parser = argparse.ArgumentParser(description="Main transport worker (PGMQ bus)")
+    parser.add_argument(
+        "--database-url",
+        default=os.getenv("DATABASE_URL"),
+        help="PostgreSQL DSN (or set DATABASE_URL env var)",
+    )
+    parser.add_argument(
+        "--channel",
+        default=os.getenv("PGMQ_CHANNEL", "pipecat_acme"),
+        help="PGMQ channel prefix",
+    )
+
+    main(parser)

examples/multi-worker/distributed-handoff/redis-handoff/llm.py

@@ -0,0 +1,153 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""LLM worker — run on Machine B (or locally alongside ``main.py``).
+
+A standalone process that runs one LLM worker (greeter or support)
+attached to the same Redis-backed `WorkerBus` as the main worker.
+Multiple instances can run on different machines.
+
+Usage::
+
+    python llm.py greeter --redis-url redis://localhost:6379
+    python llm.py support --redis-url redis://localhost:6379
+
+Requirements:
+
+- OPENAI_API_KEY
+"""
+
+import argparse
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from redis.asyncio import Redis
+
+from pipecat.bus.network.redis import RedisBus
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
+
+load_dotenv(override=True)
+
+WORKER_CONFIG = {
+    "greeter": {
+        "system_instruction": (
+            "You are a friendly greeter for Acme Corp. The available products "
+            "are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
+            "Tornado Kit. Ask which one they'd like to learn more about. "
+            "When the user picks a product or asks a question about one, "
+            "immediately call the transfer_to_agent tool with agent 'support'. "
+            "Do not answer product questions yourself. If the user says goodbye, "
+            "call the end_conversation tool. Do not mention transferring — just do it "
+            "seamlessly. Keep responses brief — this is a voice conversation."
+        ),
+        "watch": ["support"],
+    },
+    "support": {
+        "system_instruction": (
+            "You are a support agent for Acme Corp. You know about three "
+            "products: Acme Rocket Boots (jet-powered boots, $299, run up "
+            "to 60 mph), Acme Invisible Paint (makes anything invisible for "
+            "24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
+            "generator, $199, batteries included). Answer the user's questions "
+            "about these products. If the user wants to browse other products "
+            "or start over, call the transfer_to_agent tool with agent "
+            "'greeter'. If the user says goodbye, call the end_conversation "
+            "tool. Do not mention transferring — just do it seamlessly. "
+            "Keep responses brief — this is a voice conversation."
+        ),
+        "watch": ["greeter"],
+    },
+}
+
+
+class AcmeLLMTask(LLMWorker):
+    """LLM worker for Acme Corp with transfer and end tools."""
+
+    def __init__(self, name: str, *, system_instruction: str, watch: list[str]):
+        """Initialize the AcmeLLMTask.
+
+        Args:
+            name: Unique worker name (``"greeter"`` or ``"support"``).
+            system_instruction: System prompt for this LLM role.
+            watch: Sibling worker names this worker will watch via the
+                registry so it knows when they become available for
+                handoff.
+        """
+        llm = OpenAILLMService(
+            name=f"{name}::OpenAILLMService",
+            api_key=os.environ["OPENAI_API_KEY"],
+            settings=OpenAILLMService.Settings(system_instruction=system_instruction),
+        )
+        super().__init__(name, llm=llm, bridged=())
+        self._watch = watch
+
+    async def start(self) -> None:
+        """Register watches for sibling workers once ready."""
+        await super().start()
+        for worker_name in self._watch:
+            await self.watch_worker(worker_name)
+
+    @tool(cancel_on_interruption=False)
+    async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
+        """Transfer the user to another agent.
+
+        Args:
+            agent (str): The agent to transfer to (e.g. 'greeter', 'support').
+            reason (str): Why the user is being transferred.
+        """
+        logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
+        await self.activate_worker(
+            agent,
+            args=LLMWorkerActivationArgs(messages=[{"role": "developer", "content": reason}]),
+            deactivate_self=True,
+            result_callback=params.result_callback,
+        )
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the conversation when the user says goodbye.
+
+        Args:
+            reason (str): Why the conversation is ending.
+        """
+        logger.info(f"Task '{self.name}': ending conversation ({reason})")
+        await self.end(
+            reason=reason,
+            messages=[{"role": "developer", "content": reason}],
+            result_callback=params.result_callback,
+        )
+
+
+async def main_async() -> None:
+    parser = argparse.ArgumentParser(description="LLM worker (greeter or support)")
+    parser.add_argument("worker", choices=list(WORKER_CONFIG), help="Which worker to run")
+    parser.add_argument("--redis-url", default="redis://localhost:6379", help="Redis URL")
+    parser.add_argument("--channel", default="pipecat:acme", help="Redis pub/sub channel")
+    args = parser.parse_args()
+
+    redis = Redis.from_url(args.redis_url)
+    bus = RedisBus(redis=redis, channel=args.channel)
+
+    config = WORKER_CONFIG[args.worker]
+    worker = AcmeLLMTask(
+        args.worker,
+        system_instruction=config["system_instruction"],
+        watch=config["watch"],
+    )
+
+    runner = PipelineRunner(bus=bus, handle_sigint=True)
+    logger.info(f"Starting {args.worker} worker, waiting for activation...")
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main_async())

examples/multi-worker/distributed-handoff/redis-handoff/main.py

@@ -0,0 +1,170 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Main transport worker — run on Machine A.
+
+Handles audio I/O (STT, TTS) and bridges frames to the bus. The LLM
+workers run as separate processes (possibly on different
+machines) and connect to the same Redis-backed `WorkerBus`.
+
+Usage::
+
+    python main.py --redis-url redis://localhost:6379
+
+Requirements:
+
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+"""
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from redis.asyncio import Redis
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor
+from pipecat.bus.network.redis import RedisBus
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.registry.types import WorkerReadyData
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.workers.llm import LLMWorkerActivationArgs
+
+load_dotenv(override=True)
+
+MAIN_NAME = "acme"
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    redis = Redis.from_url(runner_args.cli_args.redis_url)
+    bus = RedisBus(redis=redis, channel=runner_args.cli_args.channel)
+    runner = PipelineRunner(bus=bus, handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    # The remote LLM workers may take a moment to register on the bus.
+    # We only activate ``greeter`` once *both* the client is connected
+    # and the worker has been observed via the registry.
+    state = {"client_connected": False, "greeter_ready": False}
+
+    async def maybe_activate():
+        if not (state["client_connected"] and state["greeter_ready"]):
+            return
+        await worker.activate_worker(
+            "greeter",
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, mention the available "
+                            "products and ask how you can help."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    async def on_greeter_ready(_data: WorkerReadyData) -> None:
+        state["greeter_ready"] = True
+        await maybe_activate()
+
+    await runner.registry.watch("greeter", on_greeter_ready)
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        state["client_connected"] = True
+        await maybe_activate()
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await worker.cancel()
+
+    await runner.add_workers(worker)
+    await runner.run()
+
+
+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
+
+    parser = argparse.ArgumentParser(description="Main transport worker (Redis bus)")
+    parser.add_argument("--redis-url", default="redis://localhost:6379", help="Redis URL")
+    parser.add_argument("--channel", default="pipecat:acme", help="Redis pub/sub channel")
+
+    main(parser)

examples/multi-worker/env.example

@@ -0,0 +1,22 @@
+# Audio services
+DEEPGRAM_API_KEY=
+CARTESIA_API_KEY=
+
+# LLM
+OPENAI_API_KEY=
+
+# Voice code assistant (Claude Agent SDK)
+ANTHROPIC_API_KEY=
+
+# Transport (optional — only needed with --transport daily)
+DAILY_API_KEY=
+
+# Distributed handoff via PGMQ
+# Get from: Supabase dashboard > Project Settings > Database > Connection string
+# The session-mode pooler (port 5432) is preferred. The transaction-mode
+# pooler (port 6543) also works but logs benign "resetting connection"
+# warnings.
+# Format (session pooler):
+#   postgresql://postgres.<project-ref>:<password>@aws-0-<region>.pooler.supabase.com:5432/postgres
+DATABASE_URL=
+PGMQ_CHANNEL=pipecat_acme

examples/multi-worker/local-handoff/local-handoff-two-agents-tts.py

@@ -0,0 +1,268 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Two LLM workers with per-worker TTS voices.
+
+Same shape as ``local-handoff-two-agents.py``, but each child worker
+runs its own TTS with a distinct voice. The main worker has no TTS —
+audio comes from the child workers via the bus and is played by the
+main worker's transport. Tasks announce the transfer ("let me connect
+you with...") before handing off.
+
+Architecture::
+
+    Main worker (no TTS):
+      transport.in → STT → user_agg → BusBridge → transport.out → assistant_agg
+
+    Child worker (with TTS):
+      bridge_in → LLM → TTS → bridge_out
+
+Requirements:
+
+- OPENAI_API_KEY
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DAILY_API_KEY (for Daily transport)
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import 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 DailyParams
+from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
+
+load_dotenv(override=True)
+
+MAIN_NAME = "acme"
+
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+class AcmeTTSTask(LLMWorker):
+    """Child worker with its own LLM + TTS, bridged to the main worker.
+
+    Each child wraps the standard ``Pipeline([llm])`` with an extra
+    TTS processor so audio is produced locally by each child and
+    shipped to the main worker over the bus.
+    """
+
+    def __init__(self, name: str, *, llm: OpenAILLMService, voice_id: str):
+        """Initialize the child worker.
+
+        Args:
+            name: Unique worker name.
+            llm: The LLM service for this child.
+            voice_id: Cartesia voice id for this child's TTS.
+        """
+        tts = CartesiaTTSService(
+            api_key=os.environ["CARTESIA_API_KEY"],
+            settings=CartesiaTTSService.Settings(voice=voice_id),
+        )
+        super().__init__(
+            name,
+            llm=llm,
+            pipeline=Pipeline([llm, tts]),
+            bridged=(),
+        )
+
+    @tool(cancel_on_interruption=False)
+    async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
+        """Transfer the user to another agent.
+
+        Args:
+            agent (str): The agent to transfer to (e.g. 'greeter', 'support').
+            reason (str): Why the user is being transferred.
+        """
+        logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
+        await self.activate_worker(
+            agent,
+            messages=[
+                {
+                    "role": "developer",
+                    "content": f"Tell the user about the transfer ({reason}).",
+                }
+            ],
+            args=LLMWorkerActivationArgs(
+                messages=[{"role": "developer", "content": reason}],
+            ),
+            deactivate_self=True,
+            result_callback=params.result_callback,
+        )
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the conversation when the user says goodbye.
+
+        Args:
+            reason (str): Why the conversation is ending.
+        """
+        logger.info(f"Task '{self.name}': ending conversation ({reason})")
+        await self.end(
+            reason=reason,
+            messages=[{"role": "developer", "content": reason}],
+            result_callback=params.result_callback,
+        )
+
+
+def build_greeter() -> AcmeTTSTask:
+    """Greeter: routes the user to support when they pick a product."""
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a friendly greeter for Acme Corp. The available products "
+                "are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
+                "Tornado Kit. Ask which one they'd like to learn more about. "
+                "When the user picks a product or asks a question about one, "
+                "call the transfer_to_agent tool with agent 'support'. "
+                "Do not answer product questions yourself. If the user says goodbye, "
+                "call the end_conversation tool. Keep responses brief — this is a "
+                "voice conversation."
+            ),
+        ),
+    )
+    return AcmeTTSTask(
+        "greeter",
+        llm=llm,
+        voice_id="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+    )
+
+
+def build_support() -> AcmeTTSTask:
+    """Support: answers product questions, can hand back to the greeter."""
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a support agent for Acme Corp. You know about three "
+                "products: Acme Rocket Boots (jet-powered boots, $299, run up "
+                "to 60 mph), Acme Invisible Paint (makes anything invisible for "
+                "24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
+                "generator, $199, batteries included). Answer the user's questions "
+                "about these products. If the user wants to browse other products "
+                "or start over, call the transfer_to_agent tool with agent "
+                "'greeter'. If the user says goodbye, call the end_conversation "
+                "tool. Keep responses brief — this is a voice conversation."
+            ),
+        ),
+    )
+    return AcmeTTSTask(
+        "support",
+        llm=llm,
+        voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",  # Blake
+    )
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting two-agents-with-tts bot")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    # The main worker has no TTS. Audio comes from the children over
+    # the bus; the main bridge tees user context out and pushes
+    # incoming audio/text frames back into the local pipeline.
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        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("Client connected")
+        await worker.activate_worker(
+            "greeter",
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, mention the available products "
+                            "and ask how you can help."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(build_greeter(), build_support(), worker)
+
+    await runner.run()
+
+
+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/multi-worker/local-handoff/local-handoff-two-agents.py

@@ -0,0 +1,241 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Two LLM workers with a main worker bridging transport to the bus.
+
+Demonstrates multi-worker coordination: a main worker handles transport I/O
+(STT, TTS) and bridges frames to the bus. Two LLM workers — a greeter and
+a support worker — each run their own LLM pipeline and hand off control
+between each other.
+
+The user talks to one worker at a time. Hand-offs are seamless — the LLM
+decides when to transfer based on its tools.
+
+Requirements:
+
+- OPENAI_API_KEY
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DAILY_API_KEY (for Daily transport)
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import 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 DailyParams
+from pipecat.workers.llm import LLMWorker, LLMWorkerActivationArgs, tool
+
+load_dotenv(override=True)
+
+MAIN_NAME = "acme"
+
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+class AcmeLLMTask(LLMWorker):
+    """LLM-only child worker with transfer/end tools.
+
+    Receives user context from the main worker via the bus, runs its LLM,
+    and ships generated text frames back. The main worker's TTS turns the
+    text into audio.
+
+    Passing ``bridged=()`` tells :class:`PipelineWorker` to wrap the LLM
+    pipeline with bus edge processors so frames flow between this worker
+    and the main worker automatically.
+    """
+
+    @tool(cancel_on_interruption=False)
+    async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
+        """Transfer the user to another agent.
+
+        Args:
+            agent (str): The agent to transfer to (e.g. 'greeter', 'support').
+            reason (str): Why the user is being transferred.
+        """
+        logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
+        await self.activate_worker(
+            agent,
+            args=LLMWorkerActivationArgs(
+                messages=[{"role": "developer", "content": reason}],
+            ),
+            deactivate_self=True,
+            result_callback=params.result_callback,
+        )
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the conversation when the user says goodbye.
+
+        Args:
+            reason (str): Why the conversation is ending.
+        """
+        logger.info(f"Task '{self.name}': ending conversation ({reason})")
+        await self.end(
+            reason=reason,
+            messages=[{"role": "developer", "content": reason}],
+            result_callback=params.result_callback,
+        )
+
+
+def build_greeter() -> AcmeLLMTask:
+    """Greeter: routes the user to support when they pick a product."""
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a friendly greeter for Acme Corp. The available products "
+                "are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
+                "Tornado Kit. Ask which one they'd like to learn more about. "
+                "When the user picks a product or asks a question about one, "
+                "immediately call the transfer_to_agent tool with agent 'support'. "
+                "Do not answer product questions yourself. If the user says goodbye, "
+                "call the end_conversation tool. Do not mention transferring — just do it "
+                "seamlessly. Keep responses brief — this is a voice conversation."
+            ),
+        ),
+    )
+    return AcmeLLMTask("greeter", llm=llm, bridged=())
+
+
+def build_support() -> AcmeLLMTask:
+    """Support: answers product questions, can hand back to the greeter."""
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a support agent for Acme Corp. You know about three "
+                "products: Acme Rocket Boots (jet-powered boots, $299, run up "
+                "to 60 mph), Acme Invisible Paint (makes anything invisible for "
+                "24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
+                "generator, $199, batteries included). Answer the user's questions "
+                "about these products. If the user wants to browse other products "
+                "or start over, call the transfer_to_agent tool with agent "
+                "'greeter'. If the user says goodbye, call the end_conversation "
+                "tool. Do not mention transferring — just do it seamlessly. "
+                "Keep responses brief — this is a voice conversation."
+            ),
+        ),
+    )
+    return AcmeLLMTask("support", llm=llm, bridged=())
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting two-agent bot")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    # The main bridge sends user-side context downstream to the children
+    # via the bus, and the children's generated text comes back here so
+    # the TTS can speak it.
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        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("Client connected")
+        await worker.activate_worker(
+            "greeter",
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, mention the available products "
+                            "and ask how you can help."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(build_greeter(), build_support(), worker)
+
+    await runner.run()
+
+
+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/multi-worker/parallel-debate/parallel-debate.py

@@ -0,0 +1,239 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Parallel debate using job groups.
+
+A voice bot receives a topic from the user and fans out to three
+workers in parallel via ``worker.job_group(...)``. Each worker
+runs its own LLM context, so it remembers previous topics across
+debate rounds. The bot collects all three perspectives and the
+main-worker LLM synthesizes a balanced answer.
+
+Architecture::
+
+    Main worker (transport + LLM + ``debate`` tool)
+      └── job_group(advocate, critic, analyst)
+            └── DebateWorker (LLMContextWorker, one per role)
+
+Requirements:
+
+- OPENAI_API_KEY
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DAILY_API_KEY (for Daily transport)
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusJobRequestMessage
+from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import 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 DailyParams
+from pipecat.workers.llm import LLMContextWorker
+
+load_dotenv(override=True)
+
+ROLE_PROMPTS = {
+    "advocate": (
+        "You argue IN FAVOR of the topic. Present the strongest case for why "
+        "this is a good idea, with concrete benefits. Be persuasive but honest. "
+        "Be concise, just 2-3 sentences."
+    ),
+    "critic": (
+        "You argue AGAINST the topic. Present the strongest concerns, risks, "
+        "and downsides. Be critical but fair. Be concise, just 2-3 sentences."
+    ),
+    "analyst": (
+        "You provide a BALANCED, NEUTRAL analysis. Weigh both sides objectively "
+        "and highlight the key trade-offs. Be concise, just 2-3 sentences."
+    ),
+}
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+class DebateWorker(LLMContextWorker):
+    """Worker that generates a perspective using its own LLM context.
+
+    Each worker keeps its own ``LLMContext`` so it remembers previous
+    topics across multiple debate rounds. Job requests append the new
+    topic and trigger the LLM; the assistant-aggregator captures the
+    full reply and sends it back as the job response.
+    """
+
+    def __init__(self, role: str):
+        """Initialize the DebateWorker.
+
+        Args:
+            role: One of ``"advocate"``, ``"critic"``, ``"analyst"`` —
+                used as the worker name and selects the system prompt.
+        """
+        llm = OpenAILLMService(
+            api_key=os.environ["OPENAI_API_KEY"],
+            settings=OpenAILLMService.Settings(system_instruction=ROLE_PROMPTS[role]),
+        )
+        super().__init__(role, llm=llm)
+        self._role = role
+        self._current_job_id: str | None = None
+
+        @self.assistant_aggregator.event_handler("on_assistant_turn_stopped")
+        async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
+            text = message.content
+            logger.info(f"Worker '{self.name}': completed ({len(text)} chars)")
+            if self._current_job_id:
+                job_id = self._current_job_id
+                self._current_job_id = None
+                await self.send_job_response(job_id, {"role": self._role, "text": text})
+
+    async def on_job_request(self, message: BusJobRequestMessage) -> None:
+        """Inject the topic and run the LLM."""
+        await super().on_job_request(message)
+        self._current_job_id = message.job_id
+        await self.queue_frame(
+            LLMMessagesAppendFrame(
+                messages=[{"role": "developer", "content": f"Topic: {message.payload['topic']}"}],
+                run_llm=True,
+            )
+        )
+
+
+async def debate(params: FunctionCallParams, topic: str):
+    """Analyze a topic from multiple perspectives (advocate, critic, analyst).
+
+    Args:
+        topic (str): The topic or question to debate.
+    """
+    logger.info(f"Starting debate on '{topic}'")
+    async with params.pipeline_worker.job_group(
+        *ROLE_PROMPTS, payload={"topic": topic}, timeout=30
+    ) as tg:
+        pass
+    result = "\n\n".join(f"{r['role'].upper()}: {r['text']}" for r in tg.responses.values())
+    logger.info("Debate complete, synthesizing")
+    await params.result_callback(result)
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting parallel-debate bot")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a debate moderator in a voice conversation. When the user "
+                "gives you a topic, call the debate tool to gather perspectives from "
+                "three viewpoints (advocate, critic, analyst). Then synthesize the "
+                "results into a clear, balanced summary for the user. Keep your "
+                "responses concise and natural for speaking."
+            ),
+        ),
+    )
+    llm.register_direct_function(debate, cancel_on_interruption=False, timeout_secs=60)
+
+    context = LLMContext(tools=ToolsSchema(standard_tools=[debate]))
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            llm,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        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("Client connected")
+        context.add_message(
+            {
+                "role": "developer",
+                "content": (
+                    "Greet the user and tell them you can moderate a debate on any "
+                    "topic. Ask what they'd like to explore."
+                ),
+            }
+        )
+        await worker.queue_frame(LLMRunFrame())
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(
+        DebateWorker("advocate"),
+        DebateWorker("critic"),
+        DebateWorker("analyst"),
+        worker,
+    )
+
+    await runner.run()
+
+
+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/multi-worker/remote-proxy-assistant/assistant.py

@@ -0,0 +1,119 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Remote assistant LLM server.
+
+Runs a FastAPI server that accepts WebSocket connections from a
+``main.py``-style client. Each connection spins up a
+`WebSocketProxyServerTask` bridging the socket to a local
+`PipelineRunner` and an `LLMWorker` that handles the conversation.
+
+Usage::
+
+    python assistant.py
+    python assistant.py --port 9000
+
+Requirements:
+
+- OPENAI_API_KEY
+"""
+
+import argparse
+import os
+
+import uvicorn
+from dotenv import load_dotenv
+from fastapi import FastAPI, WebSocket
+from loguru import logger
+
+from pipecat.bus import BusFrameMessage
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.workers.llm import LLMWorker, tool
+from pipecat.workers.proxy.websocket import WebSocketProxyServerTask
+
+load_dotenv(override=True)
+
+app = FastAPI()
+
+
+class AcmeAssistant(LLMWorker):
+    """Handles greetings, product questions, and conversation end."""
+
+    def __init__(self):
+        """Initialize the AcmeAssistant LLM worker."""
+        llm = OpenAILLMService(
+            api_key=os.environ["OPENAI_API_KEY"],
+            settings=OpenAILLMService.Settings(
+                system_instruction=(
+                    "You are a friendly assistant for Acme Corp. You know about three "
+                    "products: Acme Rocket Boots (jet-powered boots, $299, run up to "
+                    "60 mph), Acme Invisible Paint (makes anything invisible for 24 hours, "
+                    "$49 per can), and Acme Tornado Kit (portable tornado generator, $199, "
+                    "batteries included). Greet the user, help them with product questions, "
+                    "and call end_conversation when the user says goodbye. "
+                    "Keep responses brief, this is a voice conversation."
+                ),
+            ),
+        )
+        super().__init__("assistant", llm=llm, bridged=())
+
+    @tool
+    async def end_conversation(self, params: FunctionCallParams, reason: str):
+        """End the conversation when the user says goodbye.
+
+        Args:
+            reason (str): Why the conversation is ending.
+        """
+        logger.info(f"Task '{self.name}': ending conversation ({reason})")
+        await self.end(
+            reason=reason,
+            messages=[{"role": "developer", "content": reason}],
+            result_callback=params.result_callback,
+        )
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    """Handle a WebSocket connection from the main bot's proxy."""
+    await websocket.accept()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    proxy = WebSocketProxyServerTask(
+        "gateway",
+        websocket=websocket,
+        worker_name="assistant",
+        remote_worker_name="acme",
+        forward_messages=(BusFrameMessage,),
+    )
+
+    @proxy.event_handler("on_client_connected")
+    async def on_client_connected(proxy, client):
+        logger.info("WebSocket client connected")
+
+    @proxy.event_handler("on_client_disconnected")
+    async def on_client_disconnected(proxy, client):
+        logger.info("WebSocket client disconnected")
+        await runner.cancel()
+
+    assistant = AcmeAssistant()
+
+    await runner.add_workers(proxy, assistant)
+
+    logger.info("Assistant server ready, waiting for activation")
+    await runner.run()
+    logger.info("Assistant server session ended")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Remote assistant LLM server")
+    parser.add_argument("--host", default="0.0.0.0", help="Host to bind to")
+    parser.add_argument("--port", type=int, default=8765, help="Port to listen on")
+    args = parser.parse_args()
+
+    uvicorn.run(app, host=args.host, port=args.port)

examples/multi-worker/remote-proxy-assistant/main.py

@@ -0,0 +1,170 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Main transport worker with a WebSocket proxy to a remote LLM server.
+
+Handles audio I/O (STT, TTS) and bridges frames to the bus. A
+`WebSocketProxyClientTask` forwards bus messages to a remote LLM
+server (see ``assistant.py``) over WebSocket.
+
+Usage::
+
+    python main.py --remote-url ws://localhost:8765/ws
+
+Requirements:
+
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+"""
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusBridgeProcessor, BusFrameMessage
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.registry.types import WorkerReadyData
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.workers.llm import LLMWorkerActivationArgs
+from pipecat.workers.proxy.websocket import WebSocketProxyClientTask
+
+load_dotenv(override=True)
+
+MAIN_NAME = "acme"
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+
+    context = LLMContext()
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    bridge = BusBridgeProcessor(
+        bus=runner.bus,
+        worker_name=MAIN_NAME,
+        name=f"{MAIN_NAME}::BusBridge",
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            bridge,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        name=MAIN_NAME,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    # Forward bus frame messages over the WebSocket so the remote
+    # assistant sees user-side context and can ship back its replies.
+    proxy = WebSocketProxyClientTask(
+        "proxy",
+        url=runner_args.cli_args.remote_url,
+        local_worker_name=MAIN_NAME,
+        remote_worker_name="assistant",
+        forward_messages=(BusFrameMessage,),
+    )
+
+    async def on_assistant_ready(_data: WorkerReadyData) -> None:
+        logger.info("Remote assistant ready, activating")
+        await worker.activate_worker(
+            "assistant",
+            args=LLMWorkerActivationArgs(
+                messages=[
+                    {
+                        "role": "developer",
+                        "content": (
+                            "Welcome the user to Acme Corp, mention the available "
+                            "products and ask how you can help."
+                        ),
+                    },
+                ],
+            ),
+        )
+
+    await runner.registry.watch("assistant", on_assistant_ready)
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected, activating proxy")
+        await worker.activate_worker("proxy")
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(proxy, worker)
+
+    await runner.run()
+
+
+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
+
+    parser = argparse.ArgumentParser(description="Main transport worker with WebSocket proxy")
+    parser.add_argument(
+        "--remote-url",
+        default="ws://localhost:8765/ws",
+        help="WebSocket URL of the remote LLM server",
+    )
+
+    main(parser)

examples/multi-worker/sensor-controller/sensor-controller.py

@@ -0,0 +1,333 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Voice agent + sensor-controller worker, both as plain PipelineTasks.
+
+Two ``PipelineWorker`` instances run side by side:
+
+- The **voice agent** is built inline in ``run_bot`` — a standard
+  transport + STT + LLM + TTS pipeline. Its LLM has a single tool,
+  ``ask_controller(question)``, which forwards the user's request to
+  the controller over the bus and speaks back the response.
+- The **sensor controller** (``build_sensor_controller``) is a
+  ``PipelineWorker`` whose pipeline runs a simulated temperature sensor
+  (see ``sensor.py``) alongside its own LLM. The worker LLM has tool
+  access to read the current reading, inspect rolling stats, and
+  mutate the simulated sensor's target temperature and response rate.
+
+The worker does **not** subclass ``LLMWorker`` and is **not** bridged.
+The voice agent and the controller communicate exclusively through
+``BusJobRequestMessage`` / ``BusJobResponseMessage``. The controller
+collects responses by listening to the assistant aggregator's
+``on_assistant_turn_stopped`` event and pairing each LLM completion
+with the in-flight job id.
+
+Requirements:
+
+- OPENAI_API_KEY
+- DEEPGRAM_API_KEY
+- CARTESIA_API_KEY
+- DAILY_API_KEY (for Daily transport)
+
+Example voice exchange::
+
+    User: What's the temperature?
+    Controller: 22.1°C, holding steady.
+
+    User: Make it warmer.
+    Controller: I set the target to 26°C. Give it about 20 seconds.
+
+    User: Is it stable yet?
+    Controller: It's at 25.4°C and still climbing — almost there.
+
+    User: Why is it slow?
+    Controller: The response rate is 5%. I sped it up to 20%; it'll settle faster now.
+"""
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from sensor import SensorReader, SensorStats
+
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.bus import BusJobRequestMessage
+from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    AssistantTurnStoppedMessage,
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.types import 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 DailyParams
+
+load_dotenv(override=True)
+
+
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+    ),
+}
+
+
+def build_sensor_controller() -> PipelineWorker:
+    """Build the controller worker as a plain :class:`PipelineWorker`.
+
+    The pipeline shape is::
+
+        SensorReader -> SensorStats -> user_agg -> llm -> assistant_agg
+
+    ``SensorReader`` runs an autonomous tick loop that emits a
+    :class:`SensorReadingFrame` every second; ``SensorStats`` consumes
+    those readings and exposes rolling statistics. The LLM has four
+    direct tools that read or mutate the sensor.
+
+    Jobs arrive via the ``on_job_request`` event handler. The handler
+    stores the active ``job_id``, then queues an
+    :class:`LLMMessagesAppendFrame` with the user's question and runs
+    the LLM. When the assistant turn finishes (signalled by the
+    assistant aggregator's ``on_assistant_turn_stopped`` event), the
+    handler sends a :class:`BusJobResponseMessage` carrying the LLM's
+    answer back to the voice agent.
+    """
+    sensor = SensorReader()
+    stats = SensorStats()
+
+    async def get_current_reading(params: FunctionCallParams):
+        """Read the sensor's current temperature in degrees Celsius."""
+        await params.result_callback({"temperature": round(sensor.current, 2)})
+
+    async def get_stats(params: FunctionCallParams):
+        """Rolling minimum, maximum, average, and trend of the temperature."""
+        await params.result_callback(
+            {
+                "min": round(stats.min, 2),
+                "max": round(stats.max, 2),
+                "avg": round(stats.avg, 2),
+                "trend": stats.trend,
+            }
+        )
+
+    async def set_target_temperature(params: FunctionCallParams, target_celsius: float):
+        """Adjust the target temperature; the sensor will drift toward it.
+
+        Args:
+            target_celsius (float): The new target temperature in degrees Celsius.
+        """
+        sensor.set_target(target_celsius)
+        await params.result_callback({"ok": True, "new_target": target_celsius})
+
+    async def set_response_rate(params: FunctionCallParams, rate: float):
+        """Set how aggressively the sensor approaches the target.
+
+        Args:
+            rate (float): Response rate between 0.01 (slow) and 0.3 (fast).
+        """
+        sensor.set_response_rate(rate)
+        await params.result_callback({"ok": True, "new_rate": rate})
+
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a temperature sensor controller. You manage a single "
+                "thermometer and answer the user's questions about it. Use the "
+                "provided tools to read the current temperature, inspect rolling "
+                "statistics, change the target temperature, or change how fast "
+                "the sensor responds. When the user asks for a vague change "
+                "('make it warmer', 'cooler'), pick a sensible target and call "
+                "set_target_temperature. Always answer in one or two short "
+                "sentences — your reply is spoken aloud."
+            ),
+        ),
+    )
+    llm.register_direct_function(get_current_reading)
+    llm.register_direct_function(get_stats)
+    llm.register_direct_function(set_target_temperature)
+    llm.register_direct_function(set_response_rate)
+
+    context = LLMContext(
+        tools=ToolsSchema(
+            standard_tools=[
+                get_current_reading,
+                get_stats,
+                set_target_temperature,
+                set_response_rate,
+            ]
+        )
+    )
+    aggregators = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            sensor,
+            stats,
+            aggregators.user(),
+            llm,
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(pipeline, name="controller")
+
+    # The controller handles one job at a time (the LLM pipeline can only
+    # run one turn at a time). ``state["job_id"]`` pairs the in-flight
+    # job with the next ``on_assistant_turn_stopped`` event.
+    state: dict[str, str | None] = {"job_id": None}
+
+    @worker.event_handler("on_job_request")
+    async def on_request(_task, message: BusJobRequestMessage):
+        question = message.payload["question"]
+        logger.info(f"Controller: received question '{question}'")
+        state["job_id"] = message.job_id
+        await worker.queue_frame(
+            LLMMessagesAppendFrame(
+                messages=[{"role": "user", "content": question}],
+                run_llm=True,
+            )
+        )
+
+    @aggregators.assistant().event_handler("on_assistant_turn_stopped")
+    async def on_assistant_turn_stopped(_aggregator, message: AssistantTurnStoppedMessage):
+        # The aggregator fires this event on every ``LLMFullResponseEndFrame``,
+        # including the tool-call round that precedes the tool result and has
+        # no spoken text. Skip those so we only forward the LLM's final
+        # response to the voice agent.
+        if not message.content:
+            return
+        if state["job_id"] is None:
+            return
+        job_id, state["job_id"] = state["job_id"], None
+        logger.info(f"Controller: answering job {job_id[:8]}")
+        await worker.send_job_response(job_id, response={"answer": message.content})
+
+    return worker
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info("Starting sensor-controller bot")
+
+    # Voice agent: standard transport + STT + LLM + TTS pipeline. The
+    # only tool the voice LLM has is ``ask_controller`` — it does not
+    # know anything about temperatures, trends, or response rates.
+    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
+    tts = CartesiaTTSService(
+        api_key=os.environ["CARTESIA_API_KEY"],
+        settings=CartesiaTTSService.Settings(
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",  # Jacqueline
+        ),
+    )
+
+    async def ask_controller(params: FunctionCallParams, question: str):
+        """Ask the temperature sensor controller anything about the sensor.
+
+        Forward the user's request verbatim and speak back the answer.
+
+        Args:
+            question (str): The user's question or instruction to forward to the controller.
+        """
+        logger.info(f"Voice agent: forwarding to controller: '{question}'")
+        async with params.pipeline_worker.job(
+            "controller", payload={"question": question}, timeout=30
+        ) as t:
+            pass
+        await params.result_callback(t.response["answer"])
+
+    llm = OpenAILLMService(
+        api_key=os.environ["OPENAI_API_KEY"],
+        settings=OpenAILLMService.Settings(
+            system_instruction=(
+                "You are a friendly voice assistant with access to a temperature "
+                "sensor controller. For ANY request about the temperature — "
+                "reading it, adjusting it, checking trends, changing how fast it "
+                "responds — call the ask_controller tool. Forward the user's "
+                "request verbatim. Then speak the controller's answer back. "
+                "Keep responses brief; do not add extra commentary."
+            ),
+        ),
+    )
+    llm.register_direct_function(ask_controller, timeout_secs=60)
+
+    context = LLMContext(tools=ToolsSchema(standard_tools=[ask_controller]))
+    aggregators = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            aggregators.user(),
+            llm,
+            tts,
+            transport.output(),
+            aggregators.assistant(),
+        ]
+    )
+
+    worker = PipelineWorker(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info("Client connected")
+        context.add_message(
+            {
+                "role": "developer",
+                "content": (
+                    "Greet the user and let them know you can read or adjust a "
+                    "temperature sensor on their behalf."
+                ),
+            }
+        )
+        await worker.queue_frame(LLMRunFrame())
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info("Client disconnected")
+        await runner.cancel()
+
+    await runner.add_workers(build_sensor_controller(), worker)
+
+    await runner.run()
+
+
+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/multi-worker/sensor-controller/sensor.py

@@ -0,0 +1,186 @@
+#
+# Copyright (c) 2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Temperature sensor processors for the sensor-controller example.
+
+Two custom :class:`FrameProcessor` subclasses that give the worker
+pipeline real autonomous frame flow:
+
+- :class:`SensorReader` simulates a thermometer. It runs an async tick
+  loop that advances ``current`` toward ``target`` with a first-order
+  lag plus Gaussian noise, and pushes a :class:`SensorReadingFrame` on
+  every tick. ``target`` and ``response_rate`` are mutable so the
+  worker's LLM can adjust them via tool calls.
+- :class:`SensorStats` consumes the readings, maintains a rolling
+  window, and exposes ``current`` / ``min`` / ``max`` / ``avg`` /
+  ``trend`` as properties. The worker LLM reads these directly when
+  answering the user.
+"""
+
+import random
+import time
+from collections import deque
+from dataclasses import dataclass
+
+from pipecat.frames.frames import DataFrame, Frame, StartFrame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+@dataclass
+class SensorReadingFrame(DataFrame):
+    """A single temperature reading emitted by :class:`SensorReader`.
+
+    Parameters:
+        temperature: The reading in degrees Celsius.
+        timestamp: Unix timestamp when the reading was taken.
+    """
+
+    temperature: float = 0.0
+    timestamp: float = 0.0
+
+
+class SensorReader(FrameProcessor):
+    """Simulated temperature sensor with adjustable target and response rate.
+
+    Each tick, ``current`` is updated as::
+
+        current += (target - current) * response_rate + gauss(0, noise_sigma)
+
+    This is a first-order lag toward ``target``. With ``response_rate=0.05``
+    and a 1s tick, the current reading reaches ~halfway to target in ~14s;
+    with ``response_rate=0.2`` it converges in ~5–10s.
+    """
+
+    def __init__(
+        self,
+        *,
+        start_temp: float = 22.0,
+        sample_period_s: float = 1.0,
+        response_rate: float = 0.05,
+        noise_sigma: float = 0.1,
+    ):
+        """Initialize the sensor.
+
+        Args:
+            start_temp: Initial temperature and initial target (°C).
+            sample_period_s: Seconds between successive readings.
+            response_rate: Fraction of the gap toward target closed each tick
+                (clamped to ``[0.0, 1.0]``).
+            noise_sigma: Standard deviation of the Gaussian noise added to
+                each reading.
+        """
+        super().__init__()
+        self._current = start_temp
+        self._target = start_temp
+        self._response_rate = max(0.0, min(1.0, response_rate))
+        self._noise_sigma = noise_sigma
+        self._sample_period_s = sample_period_s
+        self._tick_task = None
+
+    @property
+    def current(self) -> float:
+        """The most recent temperature reading (°C)."""
+        return self._current
+
+    @property
+    def target(self) -> float:
+        """The temperature the sensor is drifting toward (°C)."""
+        return self._target
+
+    @property
+    def response_rate(self) -> float:
+        """Fraction of the target-current gap closed per tick."""
+        return self._response_rate
+
+    def set_target(self, value: float) -> None:
+        """Set a new target temperature (°C)."""
+        self._target = value
+
+    def set_response_rate(self, rate: float) -> None:
+        """Set how aggressively the sensor approaches the target.
+
+        Args:
+            rate: Fraction in ``[0.0, 1.0]``. Clamped to that range.
+        """
+        self._response_rate = max(0.0, min(1.0, rate))
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if isinstance(frame, StartFrame) and self._tick_task is None:
+            self._tick_task = self.create_task(self._tick_loop(), "ticker")
+        await self.push_frame(frame, direction)
+
+    async def cleanup(self) -> None:
+        if self._tick_task is not None:
+            await self.cancel_task(self._tick_task)
+            self._tick_task = None
+        await super().cleanup()
+
+    async def _tick_loop(self) -> None:
+        import asyncio
+
+        while True:
+            await asyncio.sleep(self._sample_period_s)
+            gap = self._target - self._current
+            self._current += gap * self._response_rate + random.gauss(0, self._noise_sigma)
+            await self.push_frame(
+                SensorReadingFrame(temperature=self._current, timestamp=time.time()),
+                FrameDirection.DOWNSTREAM,
+            )
+
+
+class SensorStats(FrameProcessor):
+    """Rolling-window statistics over :class:`SensorReadingFrame`s.
+
+    Consumes readings as they flow downstream and exposes rolling
+    ``min`` / ``max`` / ``avg`` / ``trend`` as properties — the worker
+    LLM reads them directly when responding to the user.
+    """
+
+    def __init__(self, window: int = 30):
+        """Initialize the stats aggregator.
+
+        Args:
+            window: Number of recent readings to retain.
+        """
+        super().__init__()
+        self._readings: deque[float] = deque(maxlen=window)
+
+    @property
+    def current(self) -> float:
+        """The most recent reading, or 0.0 if none have been seen."""
+        return self._readings[-1] if self._readings else 0.0
+
+    @property
+    def min(self) -> float:
+        return min(self._readings) if self._readings else 0.0
+
+    @property
+    def max(self) -> float:
+        return max(self._readings) if self._readings else 0.0
+
+    @property
+    def avg(self) -> float:
+        return sum(self._readings) / len(self._readings) if self._readings else 0.0
+
+    @property
+    def trend(self) -> str:
+        """``"rising"`` / ``"falling"`` / ``"stable"`` based on first vs. last half of the window."""
+        if len(self._readings) < 4:
+            return "stable"
+        half = len(self._readings) // 2
+        old_avg = sum(list(self._readings)[:half]) / half
+        new_avg = sum(list(self._readings)[half:]) / (len(self._readings) - half)
+        diff = new_avg - old_avg
+        if abs(diff) < 0.25:
+            return "stable"
+        return "rising" if diff > 0 else "falling"
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if isinstance(frame, SensorReadingFrame):
+            self._readings.append(frame.temperature)
+        await self.push_frame(frame, direction)