Rename BaseTask → BaseWorker and reserve "task" for asyncio
Replaces every "task" identifier that referred to the BaseTask abstraction with "worker". Asyncio task plumbing (asyncio.Task, BaseTaskManager, TaskManager, create_task, cancel_task, etc.) stays untouched. Highlights: - Classes: BaseTask → BaseWorker, PipelineTask → PipelineWorker, LLMTask → LLMWorker, LLMContextTask → LLMContextWorker, TaskBus → WorkerBus, TaskRegistry → WorkerRegistry, TaskActivationArgs → WorkerActivationArgs, TaskReadyData → WorkerReadyData, TaskRegistryEntry → WorkerRegistryEntry, TaskObserver → WorkerObserver, all Bus*TaskMessage → Bus*WorkerMessage, BusAddTaskMessage.task field → worker, BusWorkerRegistryMessage.tasks field → workers. - Methods/decorators: activate_task → activate_worker, deactivate_task → deactivate_worker, add_task → add_worker, watch_task → watch_worker, @task_ready → @worker_ready, setup_pipeline_task hook → setup_pipeline_worker. - Params/fields: FrameProcessorSetup.pipeline_task and FunctionCallParams.pipeline_task → pipeline_worker. Parameter names like task_name → worker_name; spawn/run accept worker:. - Files: pipeline/base_task.py → base_worker.py, pipeline/task.py → worker.py (plus a re-export shim at pipeline/task.py), task_observer.py → worker_observer.py, task_ready_decorator.py → worker_ready_decorator.py, pipecat.tasks → pipecat.workers, llm_task.py → llm_worker.py, llm_context_task.py → llm_context_worker.py, examples/multi-task → examples/multi-worker. Back-compat: - PipelineTask kept as a deprecated subclass of PipelineWorker that warns on construction. - pipecat.pipeline.task re-exports PipelineWorker/PipelineTask/etc. so existing user imports keep working. - FrameProcessor.pipeline_task kept as a deprecated property that forwards to pipeline_worker. Local variables in examples that hold a worker (task = PipelineTask(...)) are renamed to worker = PipelineWorker(...). Asyncio-task locals (runner_task, etc.) are preserved.
This commit is contained in:
Aleix Conchillo Flaqué
179
examples/multi-worker/code-assistant/code-assistant.py
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179
examples/multi-worker/code-assistant/code-assistant.py
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#
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# Copyright (c) 2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Voice code assistant powered by Claude Agent SDK.
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Talk to your codebase hands-free. Ask questions like "what does the
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auth middleware do?" or "find all TODO comments" and get spoken answers
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based on actual file contents. The Claude Agent SDK worker navigates
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the filesystem using Read, Bash, Glob, and Grep tools.
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Architecture::
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Main worker (transport + LLM + ``ask_code`` tool)
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└── job → CodeWorker (Claude Agent SDK)
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Requirements:
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- ANTHROPIC_API_KEY
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- OPENAI_API_KEY
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- DEEPGRAM_API_KEY
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- CARTESIA_API_KEY
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- DAILY_API_KEY (for Daily transport)
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"""
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import os
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from code_worker import CodeWorker
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.worker import PipelineParams, PipelineWorker
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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PROJECT_PATH = os.getenv("PROJECT_PATH", os.getcwd())
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def ask_code(params: FunctionCallParams, question: str):
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"""Ask a question about the codebase. A Claude Code worker will
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explore the project by reading files, searching code, and running
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commands. It remembers previous questions for follow-ups.
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Args:
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question (str): The question about code, files, structure,
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dependencies, or anything in the project.
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"""
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logger.info(f"Asking code worker: '{question}'")
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async with params.pipeline_worker.job("code_worker", payload={"question": question}) as job:
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await params.llm.queue_frame(
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LLMMessagesAppendFrame(
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messages=[{"role": "developer", "content": "Give me a moment."}],
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run_llm=True,
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)
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)
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# The LLM keeps talking while the worker runs.
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await params.result_callback(job.response)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting code assistant")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
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tts = CartesiaTTSService(
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api_key=os.environ["CARTESIA_API_KEY"],
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settings=CartesiaTTSService.Settings(
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voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
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),
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)
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(
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system_instruction=(
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"You are a voice interface to a code assistant powered by Claude Code. "
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"Behind you is a worker that can read files, search code with grep and "
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"glob patterns, and run bash commands on the project. It maintains "
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"context across questions, so follow-up questions work naturally.\n\n"
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"When the user asks anything about code, project structure, files, "
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"dependencies, tests, or wants to explore the codebase, call the "
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"ask_code tool. When the worker result comes back, summarize it naturally "
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"for speaking. Keep responses concise and conversational.\n"
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),
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),
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)
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llm.register_direct_function(ask_code, cancel_on_interruption=False, timeout_secs=60)
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context = LLMContext(tools=ToolsSchema(standard_tools=[ask_code]))
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aggregators = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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aggregators.user(),
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llm,
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tts,
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transport.output(),
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aggregators.assistant(),
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]
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)
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worker = PipelineWorker(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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context.add_message(
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{
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"role": "developer",
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"content": "Greet the user and tell them you're a code assistant.",
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}
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)
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await worker.queue_frame(LLMRunFrame())
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await runner.cancel()
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await runner.add_worker(CodeWorker("code_worker", project_path=PROJECT_PATH))
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await runner.add_worker(worker)
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await runner.run()
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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