I have an

user_state_changed

event listener where I want to generate a reply when the user is away for too long, however it doesn't play the reply. The logs shows it played but it doesn't.

Thanks for reaching out, Ahron. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Thanks — I’ve reviewed your code and the logs you shared. You’re specifically asking why the silence prompt’s speech shows as “playout completed without interruption” in the logs but isn’t audible to the caller. I’ll walk through the most likely causes given what you’ve posted and give focused checks you can run next. What the logs show (useful facts) • TTS and playout completed: LLM and TTS metrics and “playout completed without interruption” for

speech_3e0cbe39-6a0

. • Your silence timer continued and later triggered the room deletion, so your flow didn’t detect the user as returning after that playout. • Your handler’s addDoneCallback ran (you log “[SilenceListener] Playout finished for silence prompt.”). Given that, the system believes the audio finished playing — so this is likely an audio routing / device / timing issue rather than a TTS or pipeline failure. Try the checks and mitigations below in order. Checks to run now 1) Verify audio output is enabled for the agent/session • Confirm

audio_enabled

(or the equivalent RoomOptions/outputOptions) is set to true for the agent session that performs TTS. If output is disabled the system will generate speech but not route audio to participants. 2) Confirm audio is routed to the expected participant(s) • Ensure the generated reply is targeted at the caller’s connection and not just to an internal sink or different participant. If you have per-participant routing or multiple output tracks, verify the target is correct. 3) Reproduce with a minimal test • Trigger a single

generateReply

outside the silence listener (no timers, no room delete) and confirm the caller can hear it. If that minimal test is audible, the issue is in the interaction between the silence timer/room lifecycle and audio routing. 4) Check for concurrent audio activity or muting • Look for other audio played immediately after or during the silence playout (background prompts, hold music, or session-level mute). Another audio stream could override or mute the prompt. • Confirm the caller’s client/device isn’t muted, and no remote mute/unpublish events occur around the playout timestamps. 5) Short delay after waitForPlayout() • Even though logs show playout finished, add a small delay after

await handle.waitForPlayout()

before continuing (100–300 ms). This guards against a race where log resolution happens slightly before audio is actually audible. Example:

await handle.waitForPlayout();
  await new Promise(resolve => setTimeout(resolve, 150));

6) Extra diagnostic logging • Log the following with timestamps to correlate server logs and client behavior: ◦ When you call

generateReply

◦ When addDoneCallback runs ◦ Immediately after

waitForPlayout()

and after the optional delay ◦ Any participant mute/unpublish events and room-level output option values • If you can capture client-side logs from the caller’s device at the same timestamps, that will clarify whether audio reached the client. 7) Verify audio device / mixing setup (if applicable) • If you use virtual audio mixers, multiple interfaces, or special output devices for the room, confirm the room’s audio is actually played to the caller’s device. Some setups can generate audio server-side but not forward it correctly into the media path. If the minimal test is audible but your full silence flow still isn’t • Check whether the room deletion or other lifecycle operations happen too soon, or whether some other state change is triggered by your silence logic before audio actually reaches the caller. • Keep the room open long enough after the final playout and confirm the participant connection remains active. Quick next questions to narrow this down • Is audio output enabled for this agent/session (RoomOptions/outputOptions)? • When you run a minimal generateReply test (outside the silenceTimer flow), can the caller hear it? • Do you see any mute/unpublish or other participant state changes in the logs around 151012–151017? Tell me the answers to those and I’ll propose the exact code changes or focused diagnostics to fix this. If you want, paste the minimal test code you tried and I’ll inspect it for timing or routing issues.

audio works fine the user and agent can talk. It's only on the event handler that doesn't work. It used to work with realtime model but not in pipeline

Thanks — that helps. You confirmed audio works normally, and the silence only happens when the handler runs under a pipeline (STT→LLM→TTS) agent while it used to work with a realtime model. I’ll explain exactly why that happens and give focused, actionable fixes you can try immediately. Why this happens (short) • Realtime models synthesize and route audio directly into the WebRTC path, so calling generateReply in an event handler plays audio immediately. • Pipeline models require the TTS/output pipeline and a published output track to be active before audio actually reaches the caller. If the event fires at a time when the output pipeline or publishing isn’t fully ready, the system will generate speech (and log playout) but the audio never gets routed to the participant — hence “played” in logs but silent for the caller. What to try now (prio order) 1) Ensure the session / room output is fully connected before calling generateReply • Check the room/session connection state and only call when the output track is published/connected. • Add a small guard delay after connection if necessary (100–500 ms). 2) Await the SpeechHandle correctly for pipeline replies • Use the awaited form and wait for playout: ◦ await the handle returned from generateReply (or await the

waitForPlayout()

as you already do), then add a short delay:

await handle.waitForPlayout();
    await new Promise(r => setTimeout(r, 150));

• The short delay prevents a subtle race between the playout promise resolving and the audio actually being forwarded to the participant. 3) Confirm audio output is enabled for pipeline agents • Verify the agent session/room options have audio output enabled (RoomOptions / outputOptions / session.output.set_audio_enabled(true) equivalent). • Ensure TTS node / output pipeline is configured and not paused. 4) Check routing / target participant • Confirm the reply is routed to the caller’s connection (not to an internal sink). If you have per-participant routing, make sure the target is correct. 5) Reproduce in a minimal test inside the same event context • In the event handler, temporarily replace all other logic with only the generateReply call to confirm whether the issue is specific to the silence-timer context or to pipeline replies in that event. If the minimal test is audible, reintroduce other logic to find the interfering step. 6) Avoid lifecycle races (room deletion / state changes) • Keep the room open long enough after playout and ensure no lifecycle action (room deletion, unpublish/mute) happens while audio is finishing. Your logs show you later deleted the room — ensure deletion isn’t racing with the final bytes of audio (use the delay above or wait until you’re sure playout reached the client). 7) As a fallback for event handlers, try session.say() •

session.say()

is typically more direct for short prompts and may avoid speculatively generated/cancelled pipeline replies. Use allowInterruptions as needed. Quick checklist you can paste into your code to test: • Is room/session connection_state == connected? • Is audio output enabled? • Do:

const handle = this.session.generateReply({...});
  await handle.waitForPlayout();
  await new Promise(r => setTimeout(r, 150));

• Confirm no immediate unpublish/mute/room delete after that. Two quick questions to narrow further • In this handler, is the room or agent session ever being reinitialized or have its output track toggled around the away event? • If you run a minimal generateReply inside the same handler (no timer/room deletion), is that audible to the caller? If you tell me those two answers I’ll give the exact code adjustment to guarantee audible output in your pipeline agent flow. Sources: Realtime models overview | LiveKit Documentation | Turns overview | LiveKit Documentation | Agent speech and audio | LiveKit Documentation | Text and transcriptions | LiveKit Documentation

After more troubleshooting I see it does work, however, the agent repeats it's previous message instead of following the instructions provided in the generateReply

Thanks — I see you’ve narrowed it down: pipeline replies play, but the agent repeats its prior message instead of following the new instructions passed to

generateReply

. I’ll explain why that happens and give clear, actionable fixes you can try right away. Why the agent repeats the previous message • In the pipeline STT→LLM→TTS flow, the LLM decision is heavily influenced by the chat context.

generateReply({ instructions: ... })

uses the instructions to produce a reply but the instructions themselves are not persisted as a user message in the chat context — only the agent’s response is added. If the context hasn’t changed (no new user turn or injected user message), the model can fall back to pattern completion and repeat earlier responses. • Repeated identical triggers (e.g., same away event fired multiple times) present the same context to the model, so it often produces similar or the same wording. • Some pipeline agents may also cache recent reasoning/state, which can make them more likely to produce repeated phrases unless you explicitly change the context or give a stronger, more directive prompt. What to try (priority order) 1) Inject a synthetic user message (recommended) • Instead of solely using

instructions

, add a

user_input

so the model sees a new user turn and must respond to it: ◦ Example: session.generateReply({ user_input: "The caller has been inactive. Ask if they are still there." }) • That new user turn is appended to the chat history and anchors the LLM to produce a fresh reply. 2) Make instructions explicit and unique • If you must use

instructions

, be explicit and include unique context so the model can’t default to prior wording: ◦ "Ignore previous phrasing. Say exactly: 'Hello, are you still there? If not, I'll end this call in 30 seconds.'" • Add a small unique token (e.g., attempt number) to force different behavior: ◦ "Attempt 2: Ask the caller if they're still there and warn you'll end the call." 3) Reset or advance context before generating reply • If the conversation context is stale, consider adding a brief synthetic system or assistant message to steer the model, or clear any cached reasoning state before generating the new reply. 4) Debounce/reduce repeated triggers • Make sure you’re not invoking

generateReply()

multiple times from event flooding. Use a short debounce or a flag to ensure a single, deliberate reply per away incident. 5) Use

session.say()

for short deterministic prompts •

session.say()

is often simpler for short prompts and can be more predictable in event handlers: ◦ await session.say("Hello, are you still there?", { allowInterruptions: true }) 6) Add explicit instruction to not repeat previous content • If the model keeps echoing, add a directive: "Do not repeat the prior message. Compose a new, short prompt asking if the user is present." Debugging checks to run • Inspect the chat context right before generateReply: ◦ Print recent messages to see what the LLM will condition on. • Log how many times generateReply is called per away event and the attempt count. • Try a single minimal test that injects a

user_input

and verify the reply changes. Small code patterns (TS pseudocode) • Use user_input:

const handle = this.session.generateReply({
    user_input: "The caller has been inactive. Please ask if they're still there."
  });
  await handle.waitForPlayout();

• Force uniqueness when using instructions:

const handle = this.session.generateReply({
    instructions: `Attempt ${attemptCount}: Ask "Are you still there?" and do not repeat prior message.`,
  });
  await handle.waitForPlayout();

If you’d like, paste: • The exact

generateReply

call you now use (full options), and • A short dump of the last few messages from the agent chat context (if accessible), and I’ll suggest the precise prompt or minimal code change to guarantee the agent follows the new instructions instead of repeating itself. Sources: Agent speech and audio | LiveKit Documentation | How to detect and handle silence during calls | LiveKit

isn't user input the user's response?

Great question — you’re right that “user input” often means the actual user’s response, but in the

generateReply

API it has a specific and very useful meaning: you can inject a synthetic user turn into the conversation history so the LLM treats it like a real user message. That forces the model to respond to that new turn rather than just continuing from prior assistant context. Short comparison • `instructions`: Guidance for the model on how to produce a reply. These are used to influence the current generation but are NOT appended to the chat history as a user message. • `user_input`: Adds a new user-like message into the chat history. The LLM sees it as a user turn and generates a reply to it. This anchors the model and prevents it from just repeating earlier assistant text. Why use

user_input

here • Your pipeline agent repeats prior wording because the chat context hasn’t changed — the LLM keeps pattern-completing from earlier assistant messages. • Injecting a

user_input

creates a fresh context the LLM must respond to, so it is more likely to follow the new content you want (e.g., “The caller is inactive — ask if they are still there”). When to prefer each • Use

instructions

when you want to nudge style, tone, or behavior for a single reply and the context already contains the user turn the model should respond to. • Use

user_input

when there is no new user message and you want the LLM to behave as if a user just spoke (useful for system-initiated prompts like inactivity checks). Quick example (TypeScript-style) • Inject a synthetic user turn:

const handle = this.session.generateReply({
    user_input: "The caller has been inactive. Please ask if they are still there."
  });
  await handle.waitForPlayout();

• Use instructions (less anchored):

const handle = this.session.generateReply({
    instructions: "Ask the caller if they're still there in a polite tone."
  });
  await handle.waitForPlayout();

Practical tip • If you need the model to follow an exact phrasing, make the

user_input

or the

instructions

very explicit (or include an attempt number). For event-driven prompts (like away timeouts)

user_input

is usually the most reliable way to get a fresh, relevant reply. If you want, paste the exact

generateReply

call you’re using now and I’ll show the minimal change to switch it to

user_input

(or to combine both) so the agent responds correctly.

works!