can i lunch a worker thread inside LiveKit agent to run a parallel task?

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is in Python and agent runs in LiveKit cloud

Yes — running parallel work inside a Python agent that runs in LiveKit Cloud is supported and recommended. Use asynchronous tasks for I/O-bound work and offload CPU-bound work to threads or processes. Recommended approaches • I/O-bound / cooperative concurrency (preferred): ◦ Use

asyncio.create_task()

to start concurrent coroutines (e.g., pushing audio/video frames, STT, VAD). ◦ Use

asyncio.TaskGroup

to manage groups of related tasks and handle cancellation/exception aggregation. • CPU-bound work: ◦ Use

concurrent.futures.ThreadPoolExecutor

or

ProcessPoolExecutor

and run via

asyncio.to_thread()

(or

loop.run_in_executor

) to avoid blocking the event loop. Practical patterns 1. Push audio and video concurrently:

asyncio.create_task(self._av_sync.push(audio_frame, ...))
asyncio.create_task(self._av_sync.push(video_frame, ...))

1. Run background player while main loop handles speech:

player_task = asyncio.create_task(play_background_audio())
async with asyncio.TaskGroup() as tg:
    tg.create_task(self.main_agent_loop())
    tg.create_task(player_task)

1. Offload CPU-heavy inference:

result = await asyncio.to_thread(cpu_heavy_inference, input_data)

Questions to tailor an example • Is the parallel work primarily I/O-bound (network, file, streaming) or CPU-bound (model inference)? • Do you want a small code sample showing TaskGroup + to_thread for your specific task?

i'll have a queue that i'll read and flush via http calls

Regarding your queue + HTTP flush inside a Python agent running in LiveKit Cloud — yes, use asyncio for the queue loop and aiohttp for nonblocking HTTP calls; offload any CPU work to threads if needed. Recommended pattern (concise): 1. Run a background consumer coroutine that reads from an

asyncio.Queue

and batches/flushes via HTTP. 2. Start it with

asyncio.create_task()

or inside an

asyncio.TaskGroup

so it runs alongside the agent main loop. 3. Use

aiohttp

for HTTP I/O (nonblocking). If HTTP calls are short-lived you can also use

asyncio.Semaphore

to limit concurrency. 4. Handle retries, backoff, and graceful shutdown (cancel task, flush remaining queue). Minimal example:

import asyncio
import aiohttp

async def http_flush(session, batch):
    async with <http://session.post|session.post>("<https://example.com/flush>", json={"items": batch}) as resp:
        resp.raise_for_status()
        return await resp.text()

async def queue_consumer(queue: asyncio.Queue, shutdown_event: asyncio.Event):
    session_timeout = aiohttp.ClientTimeout(total=30)
    async with aiohttp.ClientSession(timeout=session_timeout) as session:
        while not (shutdown_event.is_set() and queue.empty()):
            batch = []
            try:
                # wait for at least one item, with timeout to check shutdown
                item = await asyncio.wait_for(queue.get(), timeout=1.0)
                batch.append(item)
                # collect additional items without waiting
                while len(batch) < 50:
                    try:
                        batch.append(queue.get_nowait())
                    except asyncio.QueueEmpty:
                        break
                # send batch, with basic retry
                for attempt in range(3):
                    try:
                        await http_flush(session, batch)
                        break
                    except Exception as e:
                        await asyncio.sleep(2 ** attempt)
                queue.task_done()
            except asyncio.TimeoutError:
                continue

Starting and shutdown (inside your agent):

queue = asyncio.Queue()
shutdown_event = asyncio.Event()

consumer_task = asyncio.create_task(queue_consumer(queue, shutdown_event))

# run agent main loop...
# to enqueue:
await queue.put(item)

# on shutdown:
shutdown_event.set()
await consumer_task  # ensures remaining items flushed

Notes and best practices • Use

asyncio.to_thread()

/

ThreadPoolExecutor

if HTTP client or processing is blocking. • Limit concurrent HTTP requests with

asyncio.Semaphore

to avoid overloading the endpoint. • Tune batch size, timeouts, and retry/backoff for your throughput and endpoint behavior. • Ensure graceful shutdown so queued items are flushed before termination. Do you want a version that: • uses

asyncio.TaskGroup

, or • adds a bounded concurrency semaphore, or • includes exponential backoff with jitter?

do you suggest TaskGroup instead of Threads? it will be a long running task as long as agent is connected

Responding to whether to use

asyncio.TaskGroup

or threads for a long-running queue+HTTP flush inside your Python LiveKit agent. Recommendation • Use asyncio (TaskGroup or a single background

asyncio.create_task

) for a long-running, I/O-bound consumer. TaskGroup is preferred when you want structured concurrency (start/stop multiple related tasks together and propagate cancellations/exceptions cleanly). • Use threads/processes only for CPU-bound or blocking libraries that cannot be made async. Why asyncio/TaskGroup is better here • Your workload (reading an asyncio.Queue + making HTTP calls with aiohttp) is I/O-bound and fits the async model. • TaskGroup gives structured lifecycle management so the consumer cancels and joins cleanly when the agent shuts down. • Async avoids OS thread overhead and context switching and keeps the event loop responsive. • You can limit concurrency of outbound HTTP requests with an

asyncio.Semaphore

without leaving the event loop. When to use threads/processes • If your HTTP client or any processing is blocking (no async client available) or you run heavy CPU-bound inference, offload that part with

asyncio.to_thread()

or a

ProcessPoolExecutor

. Minimal pattern with TaskGroup (high level) 1. Create an

asyncio.Queue

and a shutdown

Event

. 2. Start consumer(s) inside an

asyncio.TaskGroup

. 3. Consumer reads, batches, calls

aiohttp

to flush, uses retries/backoff, and honors shutdown by finishing the queue before exit. 4. On agent shutdown, cancel the TaskGroup or set shutdown event so tasks finish/flush and exit deterministically. If you’d like, I can provide a concise TaskGroup-based code sample that includes batching, semaphore-limited concurrency, and graceful shutdown. Which features do you want included: batching behavior, concurrency limit, retry/backoff with jitter, or graceful shutdown only?