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is session.generate_reply reflected in the @sessio...
# ask-ai
a
is session.generate_reply reflected in the @session.on("conversation_item_added") event like does it call an event ConversationItemAddedEvent when a replied is created using this method? If yes, then can I stop it?
t
Thanks for reaching out, Talha. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks for your question! The 
session.generate_reply
method does indeed trigger the 
@session.on("conversation_item_added")
event because it adds the generated reply to the chat history, which in turn emits this event. I understand you might want to stop or prevent this event from being triggered, but currently, there isn’t a documented way to do that when using 
session.generate_reply
. This event is automatically emitted as part of the internal process when the reply is added to the chat context. Sources: Events and error handling | LiveKit Docs | Agent speech and audio | LiveKit Docs | pipeline-llm/interrupt_user.py | livekit-agents/livekit/agents/voice/agent_activity.py | Agent speech and audio | LiveKit Docs
a
how can I start a new session with a first message?
t
Let's walk through how you can start a new session with a first message, depending on your agent type. I'm here to help make this as smooth as possible for you! • For voice agents: After starting the session, you can use 
session.say()
to have the agent greet the user with a spoken message. Here's a simple example:
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await session.start({
  agent: new voice.Agent({ instructions: "You are a helpful assistant." }),
  room: ctx.room,
});
session.say('Hello, how can I help you today?');
• For multimodal agents: You can add an initial message from the assistant using 
session.conversation.item.create()
, then trigger the response with 
session.response.create()
. Like this:
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session.conversation.item.create(llm.ChatMessage.create({
  role: llm.ChatRole.ASSISTANT,
  text: 'How can I help you today?',
}));
session.response.create();
• Using `generate_reply()`: Another handy option is to use 
session.generate_reply()
to prompt the agent to generate a welcoming initial response. Just a quick reminder: make sure your session is started and the agent is connected before sending that very first message. If you have any questions or need further help, feel free to ask—I’m here for you! Sources: Agent speech and audio | LiveKit Docs | examples/src/cartersia_tts.ts | src/agent.ts | Working with the MultimodalAgent class | LiveKit Docs | Agents v0.x migration guide - Node.js | LiveKit Docs
a
does say method create a text response if the customer is not connected to the voice? or should I use generate_reply for it?
t
I’m happy to help clarify how the 
session.say
method works! It creates a text response and then turns it into audio using the TTS plugin you have set up, even if the customer isn’t connected to voice. Just a quick heads-up: this method does need a TTS plugin to work properly. So, if there isn’t one configured, it won’t be able to complete the task. If you don’t have a TTS plugin or are working with a realtime model, no worries! You can use 
session.generate_reply
instead. This method simply generates a text response without depending on TTS, which is perfect for situations where you don’t need audio playback. Sources: Agent speech and audio | LiveKit Docs | examples/warm-transfer/warm_transfer.py | Realtime model integrations | LiveKit Docs
a
cann you tell me more about generate reply method's parameters?
t
Let me walk you through the 
generate_reply
method and its parameters: • 
user_input
(Optional): This is where you provide the user's input text that the agent should respond to. It gets added to the chat history as a user message. • 
instructions
(Optional): Here, you can add extra instructions to guide how the agent responds. These won’t be recorded in the chat history but will help shape the reply. • 
tool_choice
(Optional): This lets you specify if an external tool should be used during reply generation. If you don’t specify, it defaults to "none" when called within a function tool. • 
allow_interruptions
(Optional, default: 
True
): This controls whether the user can interrupt the agent while it’s speaking. Setting it to 
False
means interruptions aren’t allowed, though there are some exceptions for RealtimeModels. When you use this method, it returns a 
SpeechHandle
object. This is really handy because it lets you manage the generated speech—like waiting for it to finish playing or handling any interruptions. If you’d like, I’m happy to provide more details or examples to help you get comfortable with it! Sources: Agent speech and audio | LiveKit Docs | plugins/google/src/beta/realtime/realtime_api.ts | tests/test_agent_session.py
a
can I generate the exact response, or is that not possible?
t
I understand that getting the exact response you're hoping for can be really important. The 
generate_reply
method encourages the LLM to create a reply based on your instructions or input, but sometimes the output might vary a bit depending on how the model behaves. If you’re looking for a precise, scripted response every time, I’d suggest using 
session.say()
with pre-defined text. This approach helps ensure your replies are consistent and exactly what you want. Sources: Agent speech and audio | LiveKit Docs | Realtime model integrations | LiveKit Docs | Agent speech and audio | LiveKit Docs
a
elif ev.new_state == "away": if workspace_record["advanced"]["turn_timeout"]: create_object_livekit_bot( bots_record, workspace_record, session_id, "voice", logger ) timeout_prompt = "Respond only with and nothing else: Are you still there?" asyncio.create_task( session.generate_reply(instructions = timeout_prompt) ) This is what I used to replicate turn timeout, it happens the first time but doesn't call again. If i send a message ABC, it responds with are you still there. If I send another message, it doesn't send it again
t
It looks like the issue happens because 
session.generate_reply
cancels any ongoing generation if a timeout or interruption occurs. Once that pending generation is cleared, calling 
generate_reply
again might not work unless the session state is reset or carefully managed. To help you navigate this, here are a few steps you can try: • Make sure any pending generation future is cleared before you call 
generate_reply
again. If it’s still running, you can cancel it manually. • Consider adding retry logic to call 
generate_reply
again after a timeout, using tools like 
asyncio.sleep
or exponential backoff to space out the retries. • Use an event-driven approach by listening for timeout or error events, and then trigger a new 
generate_reply
call once you’ve confirmed the session state is ready. Here’s a simple example that might help:
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try:
    future = session.generate_reply(instructions="Are you still there?")
    await asyncio.wait_for(future, timeout=5.0)
except asyncio.TimeoutError:
    logger.warning("Generate reply timed out, reinitializing generation.")
    if session._pending_generation_fut and not session._pending_generation_fut.done():
        session._pending_generation_fut.cancel()
    # Optionally, retry generate_reply here
If anything feels unclear or you’d like more help, please don’t hesitate to ask—I’m here to support you! Sources: plugins/google/src/beta/realtime/realtime_api.ts | examples/voice_agents/langgraph_agent.py
a
@session.on("user_state_changed") def on_user_state_changed(ev: UserStateChangedEvent): try: session_id = ctx.room.name bots_record, workspace_record, _ = cache_records(ctx) if ev.new_state == "listening": update_timestamp_livekit_human( bots_record, workspace_record, session_id, logger ) elif ev.new_state == "speaking": ctx.proc.userdata["speech"] = True create_object_livekit_human_voice( bots_record, workspace_record, session_id, logger ) if ev.new_state == "away": asyncio.create_task(session.generate_reply( instructions=( "The user has been inactive. Politely check if the user is still present." ) )) except Exception as e: logger.error(f"Session: {session_id} - on_user_state_changed - {e}") Error: on_user_state_changed - a coroutine was expected, got <livekit.agents.voice.speech_handle.SpeechHandle object
t
I'm happy to help clarify this for you! The 
session.generate_reply
method actually returns a 
SpeechHandle
object, not a coroutine. This object is really handy because it lets you: • Wait for the speech playback to finish by using 
await handle
. • Check if the speech got interrupted with 
handle.interrupted
. • Add callbacks that run when the speech is done using 
handle.add_done_callback()
. To fix the error you’re seeing, you should directly 
await
the 
SpeechHandle
returned by 
session.generate_reply
, instead of wrapping it in 
asyncio.create_task
. Here’s a simple example:
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await session.generate_reply(
    instructions="The user has been inactive. Politely check if the user is still present."
)
If you have any more questions or need a hand with anything else, just let me know—I’m here to help! Sources: Agent speech and audio | LiveKit Docs
a
the function is not async, how can I get rid of the error?
t
To help you handle 
session.generate_reply
in a non-async function, here are a few friendly approaches you can try: • Use callbacks: You can attach a callback to the 
SpeechHandle
using 
add_done_callback()
. For example:
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def initiate_reply(session):
    handle = session.generate_reply(instructions="Hello, how can I help you?")
    handle.add_done_callback(lambda h: print("Speech finished"))
    return handle
• Blocking execution: Another way is to use 
asyncio.run()
to wait until the speech playback finishes:
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import asyncio

def generate_reply_blocking(session, instructions):
    handle = session.generate_reply(instructions=instructions)
    asyncio.run(handle.wait_for_playout())
    print("Blocking call complete: speech played out.")
• Event loop: If you have access to the event loop, you might find this useful, using `run_until_complete`:
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def generate_reply_with_event_loop(session, instructions):
    handle = session.generate_reply(instructions=instructions)
    loop = asyncio.get_event_loop()
    loop.run_until_complete(handle.wait_for_playout())
These options should help you manage the 
SpeechHandle
smoothly in non-async situations. If anything feels unclear or you’d like me to walk you through any part, just let me know—I’m here to help! Sources: Agent speech and audio | LiveKit Docs | Agent speech and audio | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py
a
Can i allocate more resources to my workers in livekit or they auto scale?
t
In LiveKit, managing resources depends a bit on how you’ve set things up, and I’m here to help you navigate that: • LiveKit Cloud: The great news is that resources are taken care of automatically! Workers scale up and down smoothly to meet demand, so you don’t have to worry about manually adjusting anything—the platform handles it all for you. • Self-hosted deployments: Here, you have the flexibility to allocate resources yourself and even enable autoscaling. For instance, if you’re using Kubernetes Helm charts, you can specify CPU and memory limits in the 
resources
section and set up autoscaling by defining things like minimum and maximum replicas and CPU utilization targets. If you’d like, I’d be more than happy to walk you through configuring resource allocation or autoscaling tailored to your specific setup. Just let me know! Sources: Deploy and scale agents on LiveKit Cloud | Deploy and scale agents on LiveKit Cloud | examples/server-gke-vpc-native.yaml | Deployment and scaling | LiveKit Docs
a
I have self hosted it, how can I allocate or increase limits?
t
If you'd like to allocate or increase resource limits for your self-hosted LiveKit workers, you can easily do this by updating the 
resources
section in your Helm 
values.yaml
or Kubernetes manifests. Here's a simple example to guide you:
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resources:
  requests:
    cpu: "4"
    memory: "8Gi"
  limits:
    cpu: "8"
    memory: "16Gi"
Here’s a quick walkthrough to apply your changes: 1. First, update the 
resources
section in your configuration file. 2. Next, redeploy or upgrade your Helm release by running: 
helm upgrade livekit . -f values.yaml
3. Finally, you can check the resource allocation with: 
kubectl describe pod <pod-name>
If you have any questions or need a hand with the configuration, please don’t hesitate to reach out. I’m here to help! Sources: examples/server-gke-vpc-native.yaml | Self-hosting the Ingress Service | LiveKit Docs | Self-hosting the Egress Service | LiveKit Docs | kubernetes/README.md
a
i am not using helm or kubernetes
t
Hi there! To help you allocate or increase resource limits for your self-hosted LiveKit workers without using Helm or Kubernetes, here’s a friendly step-by-step guide you can follow: 1. Set system-level resource limits: On your host machine, you can run these commands to make sure the system is ready:
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ulimit -n 65535
sudo sysctl -w fs.file-max=2097152
sudo sysctl -w net.core.somaxconn=65535
sudo sysctl -w net.core.rmem_max=25165824
sudo sysctl -w net.core.wmem_max=25165824
2. Allocate sufficient CPU and memory: It’s important that each instance has at least 4 CPUs and 4GB RAM. If you’re using Docker, you can specify this like so:
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docker run \
  --cpus=4 \
  --memory=4g \
  --cap-add=SYS_ADMIN \
  livekit/egress
3. Configure worker load thresholds: For AI Agents, you can set load thresholds directly in your code to optimize performance: 
WorkerOptions(load_threshold=0.7, load_fnc=custom_load_function)
4. Use configuration files: Don’t forget you can adjust settings such as bandwidth limits or how tracks are handled in the LiveKit server’s 
config.yaml
file. If you have any questions or need a hand with any of these steps, please feel free to reach out—I’m here to help! Sources: README.md | Self-hosting the Ingress Service | LiveKit Docs | Self-hosting the Egress Service | LiveKit Docs | Worker options | LiveKit Docs | config-sample.yaml
a
what is the defauly load threshold which is defined if I don't specify it
t
Just to share some helpful info: the default load threshold for LiveKit workers in production mode is set to 
0.7
. What this means is that a worker will pause accepting new jobs once its load—usually measured by CPU usage—goes beyond 70%. This helps keep everything running smoothly. On the other hand, when you're in development mode, this load threshold is basically turned off (set to infinity). So, workers will happily accept new jobs no matter how busy they are, which can be really handy while you're testing things out. Sources: agents/src/worker.ts | Deploying to custom environments | LiveKit Docs | livekit-agents/livekit/agents/worker.py | Worker options | LiveKit Docs
a
Sep 22 165511 0076-kci-prxmx10118 python[2273871]: {"message": "inference is slower than realtime", "level": "WARNING", "name": "livekit.plugins.silero", "delay": 0.2719513388648629, "pid": 2274250, "job_id": "AJ_XcHvBZdpzG6x", "timestamp": "2025-09-22T115511.011811+00:00"} Sep 22 165511 0076-kci-prxmx10118 python[2273871]: {"message": "inference is slower than realtime", "level": "WARNING", "name": "livekit.plugins.silero", "delay": 0.2717560931406915, "pid": 2274250, "job_id": "AJ_XcHvBZdpzG6x", "timestamp": "2025-09-22T115511.012173+00:00"} Sep 22 165511 0076-kci-prxmx10118 python[2273871]: {"message": "Error in _bounce_eou_task\nTraceback (most recent call last):\n File \"/var/www/bot-crm-dev/venv/lib/python3.10/site-packages/livekit/agents/utils/log.py\", line 16, in async_fn_logs\n return await fn(*args, **kwargs)\n File \"/var/www/bot-crm-dev/venv/lib/python3.10/site-packages/livekit/agents/voice/audio_recognition.py\", line 365, in _bounce_eou_task\n if not await turn_detector.supports_language(self._last_language)\nTypeError object bool can't be used in 'await' expression", "level": "ERROR", "name": "livekit.agents", "pid": 2274250, "job_id": "AJ_XcHvBZdpzG6x", "timestamp": "2025-09-22T115511.566908+00:00"} Sep 22 165511 0076-kci-prxmx10118 python[2273871]: {"message": "inference is slower than realtime", "level": "WARNING", "name": "livekit.plugins.silero", "delay": 0.27657619503512976, "pid": 2274250, "job_id": "AJ_XcHvBZdpzG6x", "timestamp": "2025-09-22T115511.567338+00:00"} Sep 22 165512 0076-kci-prxmx10118 python[2273871]: {"message": "Error in _bounce_eou_task\nTraceback (most recent call last):\n File \"/var/www/bot-crm-dev/venv/lib/python3.10/site-packages/livekit/agents/utils/log.py\", line 16, in async_fn_logs\n return await fn(*args, **kwargs)\n File \"/var/www/bot-crm-dev/venv/lib/python3.10/site-packages/livekit/agents/voice/audio_recognition.py\", line 365, in _bounce_eou_task\n if not await turn_detector.supports_language(self._last_language)\nTypeError object bool can't be used in 'await' expression", "level": "ERROR", "name": "livekit.agents", "pid": 2274250, "job_id": "AJ_XcHvBZdpzG6x", "timestamp": "2025-09-22T115512.047428+00:00"} what is this error?
t
It looks like you're encountering the error "`TypeError: object bool can't be used in await expression`" because the 
on_end_of_turn
method in the 
RecognitionHooks
protocol is returning a 
bool
, but it's being `await`ed in the 
_bounce_eou_task
function, which actually expects something awaitable. To help fix this, you’ll want to make sure that your 
on_end_of_turn
method is defined as an 
async
function that returns an awaitable object. Here’s a simple example:
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async def on_end_of_turn(self, info: _EndOfTurnInfo) -> bool:
    print(f"End of turn: {info.new_transcript}")
    return True
If you find yourself needing to return a boolean value synchronously, a handy trick is to wrap it in an 
asyncio.Future
or use 
asyncio.sleep(0)
to make it awaitable, like this:
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async def on_end_of_turn_wrapper(info: _EndOfTurnInfo) -> bool:
    return on_end_of_turn(info)
Please don’t hesitate to reach out if you’d like me to walk through this more or if there’s anything else you need help with—I’m here for you! Sources: livekit-agents/livekit/agents/voice/speech_handle.py
a
Nvidia Encoder is supported. Sep 22 170749 0076-kci-prxmx10118 python[2283405]: Nvidia Decoder is supported. Sep 22 170751 0076-kci-prxmx10118 python[2283405]: error: failed to fetch server settings: http status: 404 Sep 22 170751 0076-kci-prxmx10118 python[2283091]: {"message": "livekit_ffi:serverroom149livekit ffiserver:room - audio filter cannot be enabled: LiveKit Cloud is required", "level": "ERROR", "name": "live What is this?
t
Hi there! I wanted to help clarify the error "`failed to fetch server settings: http status: 404`" you’re seeing. This happens because the LiveKit client is trying to reach the 
/settings/regions
endpoint, which is available only on LiveKit Cloud. If you’re using a self-hosted setup, you can safely ignore this error since that endpoint doesn’t exist on self-hosted servers. Regarding the error "`audio filter cannot be enabled: LiveKit Cloud is required`", this means that some advanced audio features, like enhanced noise cancellation, are exclusive to LiveKit Cloud and aren’t supported in self-hosted environments. When this error appears, the system just logs it and continues without enabling the audio filter. So, no worries about the 404 error in your self-hosted setup—it’s expected and doesn’t require any action. If you’re interested in those advanced audio features, you would need to switch to LiveKit Cloud to access them. I hope this helps clear things up! If you have any more questions, feel free to ask. Sources: src/room/Room.ts | regionurlprovider.go | lib/src/core/room.dart | livekit-ffi/src/server/room.rs | Operational Guidance | livekit/livekit_composite | DeepWiki
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