is there a way to play a twiml message from inside a livekit worker?

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

yea just a twiml <Say> message, or better yet if we could generate twiml that could Dial and fallback to Say.

Thanks — this is helpful. I’m responding to your follow-up asking about playing a TwiML

<Say>

(or a

Dial

that falls back to

Say

) from inside a LiveKit worker. Short answer: you cannot directly “execute” TwiML inside a LiveKit worker. Instead, you need to render the TwiML into audio (speech) and then publish that audio into the LiveKit room from the worker. Below are practical ways to do that and what to consider for a Dial+fallback flow. Options to deliver a

<Say>

into a LiveKit room • Use Twilio to synthesize the speech (TTS) and fetch the audio: a. Generate TwiML for

<Say>

on your server. b. Use Twilio’s TTS/output URL (for example have Twilio return or host a WAV/MP3) or use Twilio’s REST API to synthesize speech into an audio file/URL. c. From your LiveKit worker, fetch that audio file (stream or download) and publish it into the LiveKit room as an audio track (using the LiveKit SDK or server-side publisher approach). • Use an external TTS engine (Google, AWS Polly, etc.): a. Convert the text you would put in

<Say>

to audio using an external TTS API. b. Publish the resulting audio into the LiveKit room from the worker the same way. How to publish the audio from a LiveKit worker • Create or use a publisher in your worker that can push an audio track into the room. Stream the TTS audio (live stream or short file) as the track so room participants hear it. • If you need low-latency streaming, stream chunks of PCM/Opus to the publisher rather than sending a file end-to-end. Dial with fallback to Say • A Dial flow (where you attempt a PSTN or SIP call, then fallback to a message if the call fails) is run by Twilio via TwiML/Call flows. That dial-and-fallback logic cannot be “run” inside LiveKit directly. • Two ways to combine Dial+LiveKit: ◦ Run the Dial+fallback on Twilio. If the Dial fails and Twilio executes

<Say>

, have Twilio produce an audio file or a publicly accessible media URL for the spoken fallback. Your LiveKit worker can then fetch that audio and publish it into the room. ◦ Or implement the Dial attempt separately (for example, call out via Twilio Programmable Voice or SIP) and have your backend detect failure. If it fails, generate the fallback text and synthesize it to audio (Twilio TTS or other), then publish into LiveKit. Questions that will help me give exact steps • How do you want the audio published into the room? (heard by everyone, a specific participant, or recorded only) • Do you already have a LiveKit publisher in your worker that can send audio tracks, or do you want step-by-step code for publishing audio from a worker? • Are you using Twilio to host/serve the synthesized audio, or would you prefer an external TTS provider? If you tell me which publishing approach you prefer (file download -> publish, or streaming TTS -> publish) and whether you want sample code for a specific language/runtime, I’ll provide concise, step-by-step instructions and example snippets to implement it. , with interactive elements

in a livekit worker, whats like the easiest approach to synthesize one message in speech with something like cartesia, before an agent session is active but after a room is connected

Thanks — I’m responding to your question about the easiest way to synthesize a single speech message (e.g., Cartesia) in a LiveKit worker after the room is connected but before an AgentSession starts. Short answer • The simplest approach is: use a TTS plugin (Cartesia) from inside the worker to synthesize audio, create a local audio track, and publish that track to the room. This does not require starting an AgentSession — only a connected room and a publisher track. Why this works • A LiveKit worker can publish audio tracks just like any participant. If you synthesize the speech to PCM/Opus frames (or stream them), you can push those frames into a LocalAudioTrack and participants will hear the message. Recommended implementation (concept + Python example) 1. Connect the worker to the room (you already have the room connected). 2. Create a Cartesia TTS instance (plugin). 3. Create an audio source and a LocalAudioTrack. 4. Publish the track from the worker’s local participant. 5. Push synthesized audio frames into the audio source (streaming or full file). 6. Unpublish or stop the track after playback. Example (Python-style pseudocode — adapt to your runtime):

# 1. assume ctx.connect() or equivalent already called and room is connected

# 2. create TTS (cartesia plugin)
tts = cartesia.TTS(model="sonic-3")

# 3. create a streaming interface and audio source / local track
stream = tts.stream()               # streaming TTS input/output
audio_source = rtc.AudioSource(44100, 1)
track = rtc.LocalAudioTrack.create_audio_track("tts_intro", audio_source)
await ctx.room.local_participant.publish_track(track)

# 4. push text and stream audio frames into the audio source
text = "Hello — an agent will be with you shortly."
async def play():
    async for audio_chunk in stream:      # stream yields audio frames/blocks
        await audio_source.capture_frame(audio_chunk.frame)

task = asyncio.create_task(play())
stream.push_text(text)
stream.end_input()
await task

# 5. cleanup when done
await ctx.room.local_participant.unpublish_track(track)
track.stop()

Practical tips • Use streaming TTS so you can begin playback quickly without waiting for a full file. Streaming also keeps latency low. • Ensure sample rate and channel config match the LiveKit audio source (commonly 48kHz or 44.1kHz mono). Convert if needed. • If you prefer not to stream frames, synthesize to a short WAV/MP3 file and stream/pipe that file into the audio source. • If you need everyone to hear it, publish from the room’s local participant (as shown). To target a single participant, you’d need per-participant routing or play only on a bot connection that only the intended listener subscribes to. • After playback, unpublish/stop the track to free resources. If you want, I can: • Provide a concrete, copy-paste example for your language/runtime (Node, Python, Go). • Show how to synthesize to a file instead of streaming. • Show how to ensure correct sample-rate conversion (if your TTS output differs from LiveKit’s expected rate). Which language/runtime would you like the concrete example in? Sources: Cartesia TTS | LiveKit Documentation | Text-to-speech (TTS) models overview | LiveKit Documentation, with interactive elements

what if you just called client.calls.update on twilio? wouldnt that end the livekit leg

Thanks — I’m following up on your question: “if you call Twilio’s

client.calls.update

, wouldn’t that end the LiveKit leg?” I’ll clarify how the two sides interact and what to expect. Short answer • Calling

client.calls.update

only affects the Twilio call resource. It will not automatically control or tear down a separate LiveKit connection unless your integration explicitly links the Twilio call state to LiveKit actions. In other words, updating a Twilio Call does not magically end a LiveKit audio track or room participant unless you have code that does that when the Twilio call changes. Why (what actually happens) • Twilio’s Call API controls Twilio-side behavior: e.g., changing the call’s TwiML URL, redirecting the call, hanging up the call (

status: "completed"

), etc. • LiveKit is a separate media system. If you bridged Twilio and LiveKit (for example, a media gateway or a bot participant that forwards audio between Twilio and LiveKit), there are two different “legs”: ◦ The Twilio PSTN/SIP leg (controlled by Twilio Call) ◦ The LiveKit leg (controlled by your LiveKit participant/worker) • If you only update the Twilio call (redirect, hang up), the Twilio leg changes. The LiveKit leg will remain active until you explicitly stop/unpublish the LiveKit participant’s track or disconnect that participant. Common integration patterns and where action is needed • Simple redirect/hangup on Twilio only: ◦ Twilio call ends/redirects. If your gateway doesn’t react, LiveKit still has the bot/bridge connected and publishing audio. ◦ To fully tear down the session, your backend should detect Twilio call updates (webhook or your update response) and then unpublish/stop or disconnect the LiveKit participant. • Twilio TwiML redirect to play fallback: ◦ Twilio will execute new TwiML (e.g.,

<Say>

). If you want that audio inside LiveKit, you either let Twilio host the audio (and fetch it into LiveKit) or synthesize the fallback inside your worker and publish to LiveKit. Again, Twilio’s update does not alter LiveKit unless your code does it. • If you have a bridge bot that proxies audio between Twilio and LiveKit: ◦ When Twilio call ends, the bridge should clean up the corresponding LiveKit track/participant. ◦ When you call

client.calls.update

to redirect Twilio to new TwiML that ends the bridge media, your bridge-side logic should respond by stopping its LiveKit publishing. What I recommend (practical) • Treat Twilio and LiveKit as two independent legs and explicitly coordinate them: ◦ Use Twilio call status webhooks (or handle the response from

client.calls.update

) to trigger LiveKit cleanup (unpublish track / disconnect participant) when the Twilio call ends. ◦ If you want Twilio-driven audio (TwiML

<Say>

or fallback) to be heard in LiveKit, have your worker fetch or synthesize that audio and publish it into the room when Twilio indicates fallback is active. • If your goal is "a single API call to end both legs": implement a small backend endpoint that: 1) Calls

client.calls.update

(to finish the Twilio call), and 2) Calls your LiveKit management code to unpublish/stop or disconnect the LiveKit participant/worker. Quick question to tailor next steps • How are you bridging Twilio and LiveKit today? (a bridge bot publishing into LiveKit, or two independent callers) • Do you want an example flow or code snippet that does: call Twilio update -> then unpublish a LiveKit track? If so, which language/runtime do you prefer? I can provide a compact code example and exact cleanup steps once you tell me how your current bridge is implemented. , with interactive elements

the problem that im trynig solve is the following: the call gets to the livekit worker and in the livekit worker, we decide that the call needs to end and want to say an automated message. i was hoping that i could update the twilio leg which maybe could tear down the livekit leg and the the twilio leg would use <Say>

Thanks — I hear you: you want the LiveKit worker to decide the call should end, have the caller hear an automated TwiML

<Say>

fallback, and ideally have that Twilio action also tear down the LiveKit leg. I’ll clarify what’s possible and give two straightforward, reliable patterns you can implement so the behavior is predictable. Short answer • Calling Twilio’s

client.calls.update

will only change the Twilio call resource. It will not automatically tear down the LiveKit leg unless your integration explicitly does that. To get the caller to hear a TwiML

<Say>

and also cleanly remove the LiveKit participant, you need to coordinate actions between Twilio and your LiveKit worker. Two practical patterns you can use 1) Twilio-driven fallback (Twilio plays

<Say>

) + explicit LiveKit cleanup • Flow: a. LiveKit worker decides to end the call. b. Worker calls Twilio

client.calls.update(callSid, {url: "<url-that-returns-TwiML>"})

or sets

status: "completed"

as appropriate. The new TwiML URL returns a

<Say>

(or a Dial that falls back to

<Say>

). c. Twilio executes the TwiML and plays the

<Say>

to the PSTN/SIP side. d. Worker (or another backend) then unpublishes/stops the LiveKit participant track or disconnects that worker from the room. • Notes: ◦ You must explicitly unpublish/stop the LiveKit leg (or detect Twilio's state via webhook) — Twilio won’t automatically remove the LiveKit participant. ◦ Use Twilio call status webhooks to be notified when Twilio completes the fallback if you want to wait for playback to finish before cleaning up LiveKit. ◦ If you want the

<Say>

to be heard inside LiveKit as well, have Twilio host an audio file or return a publicly accessible audio URL and have the LiveKit worker fetch and play it, or synthesize the same text in LiveKit (pattern 2). 2) LiveKit-driven TTS fallback (worker synthesizes and plays the message), then ends Twilio leg • Flow: a. Worker decides to end the call. b. Worker synthesizes the fallback message locally (Cartesia, Polly, etc.) and publishes that audio into the LiveKit room so the caller hears the message immediately (if the bridge is forwarding audio to Twilio). c. After playback completes, worker calls

client.calls.update(callSid, {status: "completed"})

to hang up the Twilio leg and then unpublishes/stops the LiveKit participant. • Notes: ◦ This keeps control in your LiveKit worker and avoids relying on Twilio redirect timing. ◦ Works well if you already have a bridge/bot that forwards audio between LiveKit and Twilio. ◦ If you need Twilio to show call recordings or TwiML logs, you can still call

client.calls.update

afterwards. Which to pick • Use pattern 1 if you prefer Twilio to be authoritative for call playback (Twilio plays the

<Say>

natively). • Use pattern 2 if you want lowest latency control and you already can publish audio from the LiveKit worker into the Twilio leg (i.e., you have a media bridge). Implementation tips and safety • Always coordinate cleanup explicitly: when LiveKit decides to end, call your LiveKit unpublish/leave logic immediately (or after Twilio confirms playback if you prefer). • If using pattern 1 and you depend on Twilio to play the

<Say>

, have Twilio call status webhooks (or poll) so you can know when Twilio finished playback and then unpublish/cleanup LiveKit. • If you want a single API to end both legs reliably, implement a backend endpoint that: 1) triggers Twilio update/redirect (if needed), and 2) triggers LiveKit unpublish/stop/disconnect. This avoids race conditions where one leg lingers. Quick example (pseudo steps) • Pattern 1 (Twilio plays

<Say>

, then cleanup): a. Worker: POST /update-call -> Twilio

client.calls.update(callSid, {url: "https://example.com/fallback-twiml"})

b. Fallback TwiML at /fallback-twiml returns: ResponseSayGoodbye.../Say/Response c. Twilio plays

<Say>

. d. Either: • Twilio status webhook notifies your backend when call is completed -> backend unpublishes LiveKit track, or • Worker unpublishes immediately if you want to drop LiveKit before Twilio finishes playback (not recommended if you need callers to hear the message from the same media path). • Pattern 2 (worker plays TTS, then hang up Twilio): a. Worker synthesizes TTS and publishes it into LiveKit (so it reaches the call). b. Worker waits for playback end. c. Worker calls

client.calls.update(callSid, {status: "completed"})

to end Twilio leg. d. Worker unpublishes/stops LiveKit participant. Follow-up questions so I can give exact code • How are you bridging audio between LiveKit and Twilio today? (Is your worker publishing into LiveKit and also forwarding audio to Twilio, or is Twilio connected to LiveKit via a separate gateway?) • Do you want the

<Say>

to be audible only to the PSTN caller, or also to LiveKit room participants? • Which language/runtime would you like example code in for the Twilio update + LiveKit cleanup flow? Tell me those details and I’ll give a concise example you can paste into your worker (Node or Python). , with interactive elements

could you then use <Dial> to cold transfer?

Good question — yes, you can use Twilio’s Dial to perform a cold transfer, but you need to coordinate it with your LiveKit worker so both media legs behave as you expect. Below I explain how Dial cold transfers fit into the two‑leg model (Twilio leg vs LiveKit leg), the practical flows, and recommendations so the caller hears the transferred party and your LiveKit leg is cleaned up correctly. How Dial cold transfer works in this integration • Twilio’s Dial instructs Twilio to connect the caller to another endpoint (PSTN number, SIP URI, Twilio Client, etc.). That transfer happens on the Twilio side (the Twilio leg). • The LiveKit leg is separate. Calling Dial on Twilio does not automatically stop/unpublish the LiveKit participant. You must explicitly stop your LiveKit leg when appropriate. Two common patterns to implement a cold transfer initiated by a LiveKit worker 1) Twilio-native cold transfer (Twilio does the transfer) • Flow: a. LiveKit worker decides to transfer the call. b. Worker calls Twilio’s Calls API (or returns redirect TwiML) to move the Twilio call into a TwiML document that contains a Dial (target number or SIP). • Example TwiML: ResponseDial+15551234567/Dial/Response a. Twilio connects the caller to the target directly (cold transfer). b. Worker then unpublishes/stops the LiveKit participant or disconnects the worker. • Notes & best practices: ◦ Use Twilio call status webhooks to know when Twilio has successfully connected the new party or when the original call has ended — use that to time LiveKit cleanup. ◦ If you immediately unpublish the LiveKit leg before Twilio finishes connecting, you risk losing any final audio you want the caller to hear from LiveKit. Coordinate timing as needed. ◦ This is the simplest way to perform a true cold transfer where the caller is moved off your bridge and Twilio manages the new media path. 2) Bridge-managed transfer (LiveKit broker/performs handoff) • Flow: a. Worker creates a new outbound Twilio call to the target (programmatically via Twilio API). b. Worker bridges audio between the original caller and the new outbound call (this is an active, warm handoff/bridging step). c. Once the new call is answered and audio is stable, the worker can either: • Drop itself and let Twilio and the caller remain connected directly (if Twilio supports transferring endpoints accordingly), or • Keep bridging if you want supervision or to play pre-transfer prompts. • Notes & best practices: ◦ This is closer to a warm transfer: you ensure the target answers before breaking the media bridging. ◦ It gives you more control (play pre‑transfer prompts, verify destination availability). ◦ It’s more complex because you must handle RTP/media bridging and eventual teardown of your LiveKit leg. Things to watch for (gotchas) • Race conditions: If you hang up the LiveKit participant immediately after issuing the Twilio Dial, you might cut off any final prompts or coordination audio. Use webhooks or explicit state transitions to avoid races. • Media path: If you want the caller to hear a prompt that originates in LiveKit (for example “Transferring now”), make sure that prompt is played before the Dial redirect or that Twilio plays its own TwiML prompt. • Recording and billing: When you cold transfer to another PSTN endpoint, call recording/leg accounting may change; ensure Twilio’s settings meet your requirements. • Failure fallback: If the Dial fails (busy/no-answer), provide a fallback TwiML URL that contains Say or Redirect so the caller gets a graceful message — and then trigger LiveKit cleanup. Recommended implementation approach • If you want a true cold transfer and to minimize complexity, use Twilio’s Dial and coordinate cleanup: a. Worker calls Twilio to update the call’s TwiML to a URL that returns Dial to target. b. Use Twilio status webhooks to detect the transfer result (answered, completed, failed). c. On appropriate webhook events, unpublish/stop the LiveKit participant and perform any post-transfer cleanup. • If you need to verify the target before removing the LiveKit leg (warm transfer), place an intermediate outbound call from your worker, bridge audio, then release the LiveKit leg when safe. Do you want a concise code example? • I can provide a short example that: ◦ Calls Twilio’s REST API to redirect the call to TwiML with Dial, and ◦ Shows the LiveKit cleanup call to unpublish/disconnect when Twilio reports the transfer state. • Which language/runtime would be most useful (Node or Python)?, with interactive elements

ok well then what if i want to use livekit to synthesize speech without starting an agent session

Thanks — I’m responding to your question about synthesizing speech in LiveKit without starting an AgentSession. Short answer • Yes — you can synthesize speech from a LiveKit worker without creating an AgentSession. The simplest pattern is to use a TTS plugin (e.g., Cartesia or another TTS provider) directly in the worker, create a LocalAudioTrack or audio source, and publish or push the synthesized audio into the room. No AgentSession is required. How it works (concept) • Connect your worker to the LiveKit room as a participant (or use an existing worker connection). • Use a TTS API or plugin to synthesize the text you want (streaming preferred for low latency). • Create an audio source / LocalAudioTrack in the worker and push audio frames from the TTS output into that track. • Publish the track from the worker’s local participant so room participants (or the bridged Twilio leg) hear it. • After playback, unpublish and stop the track to clean up. Minimal example flow (Python-style pseudocode) 1. Connect to the room (assume ctx.connect() already done). 2. Create Cartesia (or other) TTS instance and a streaming interface. 3. Create an audio source and LocalAudioTrack, publish it. 4. Stream synthesized frames into the audio source until done. 5. Unpublish/stop the track.

# (pseudocode — adapt to your runtime)
tts = cartesia.TTS(model="sonic-3")
stream = tts.stream()

audio_source = rtc.AudioSource(sample_rate=48000, channels=1)
track = rtc.LocalAudioTrack.create_audio_track("tts_intro", audio_source)
await ctx.room.local_participant.publish_track(track)

async def play():
    async for chunk in stream:               # chunk.frame contains PCM frames
        await audio_source.capture_frame(chunk.frame)

task = asyncio.create_task(play())
stream.push_text("Hello — an agent will be with you shortly.")
stream.end_input()
await task

await ctx.room.local_participant.unpublish_track(track)
track.stop()

Practical tips & considerations • Streaming vs file: Streaming TTS starts playback faster and lowers latency. If your TTS provider only returns files, download and stream the file into the audio source. • Sample rate & format: Ensure TTS output sample rate and channel count match the audio source expected by LiveKit (convert if needed). LiveKit commonly uses 48 kHz; convert if your TTS uses a different rate. • Targeting who hears it: Publish from the worker’s local participant to make the message audible to everyone subscribed. If you only want the PSTN/Twilio caller to hear it, publish from the bridge participant that forwards audio into Twilio. • Cleanup: Unpublish and stop the track when done to free resources. • No AgentSession needed: This approach avoids AgentSession overhead and gives you direct control for single messages or prompts. If you want, I can: • Provide a copy-paste example in Node.js (or Python) tailored to your runtime. • Show how to stream a downloaded WAV/MP3 into a LiveKit audio source if your TTS returns files. • Show sample rate conversion steps if your TTS uses 44.1kHz and LiveKit expects 48kHz. Which language/runtime would you like the concrete example in? , with interactive elements