I want to discard any user messages that were said before the first assitant msg is delivered by the agent. Even though the user speaks anything before the first asssitant msg is delivered, I don't want llm call to happen and also that message must not be present in chat_ctx. What is the best way to deal with this?

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

I don't want to use the

set_audio_enabled

method because it's cause some other issues. is there an alternative?

Thanks — that helps. I hear that you want to ignore any user speech before the agent’s first assistant message, but you can’t use

set_audio_enabled

because it creates other problems in your setup. Below are safe alternatives you can use that do not toggle the audio track, plus a few targeted questions so I can recommend the cleanest approach for your codebase. Short summary of alternatives • Programmatic transcript filtering (recommended): Let STT/VAD continue running, but drop any

user_input_transcribed

events until the first assistant reply completes. This prevents LLM calls and keeps chat_ctx clean without changing audio state. • Reject early user turns at a higher event level: Intercept

user_turn_completed

(or your framework’s equivalent) and return early for turns that happen before the assistant’s initial reply. • Mark-and-sweep after transcript: Accept transcripts but keep them in a temporary buffer. After the assistant’s first reply, move buffered transcripts into chat_ctx; otherwise delete them. Use this if you need transcripts for analytics but don’t want them in LLM context. • Gate LLM calls with an explicit flag: Always route transcripts through a function that checks

first_assistant_reply_sent

. If false, don’t call the LLM or add to chat_ctx. Example patterns 1) Transcript filter (simple, low-impact)

first_reply_made = False

@session.on("user_input_transcribed")
def on_transcript(transcript):
    global first_reply_made
    if not first_reply_made:
        # Discard early transcript — do not add to chat_ctx or call LLM
        return
    # Normal processing: add to chat_ctx and call LLM

After generating the agent’s first reply:

await session.generate_reply(instructions="Greet the user.")
first_reply_made = True

2) Reject early turns (higher-level)

@session.on("user_turn_completed")
async def on_user_turn(ctx, user_msg):
    if not session_state.first_reply_sent:
        # Drop this turn — do not call inference or update chat_ctx
        return
    # proceed with normal handling

3) Temporary buffer (if you need transcripts but not in chat_ctx)

prebuffer = []

@session.on("user_input_transcribed")
def on_transcript(transcript):
    if not session_state.first_reply_sent:
        prebuffer.append(transcript)
        return
    process_transcript(transcript)

# After first assistant reply:
if should_keep_prebuffered: 
    for t in prebuffer:
        process_transcript(t)
prebuffer.clear()

When to pick which approach • Use transcript filtering if you want the simplest, most deterministic behavior and don’t need the early speech for anything. • Use a higher-level turn handler if you want to avoid partial-turn corner cases (e.g., VAD segments that span the assistant reply). • Use buffering if you need a record of early speech (for metrics or debugging) but still want to avoid LLM calls or chat_ctx contamination. Potential pitfalls to watch for • VAD/STT will still run in all these approaches, so there may be CPU/network usage even when you discard transcripts. If that is a problem, we can discuss lightweight debounce logic to reduce load. • Make sure any shared flags (like

first_reply_made

) are thread-safe if your event callbacks run concurrently. • If you use buffering, be careful about memory growth for long pre-reply periods — enforce a size/time limit. A couple quick clarifying questions so I can give code tailored to your stack 1. Are you using voice-only (STT) or also accepting text input? Do you need the same behavior for text messages? 2. Which SDK / language and event model are you using (Python server session, Node.js, client-side)? That helps me give an exact snippet matching your API. 3. What problems did

set_audio_enabled

cause in your environment (connection/state issues, platform bugs, side-effects with other integrations)? Answering those will let me give a concise code example that fits your environment and any concurrency needs. Sources: How to implement call greeting retries in voice agents | LiveKit | Agent session | LiveKit Documentation | Agent speech and audio | LiveKit Documentation | Text and transcriptions | LiveKit Documentation

I feel the

set_audio_enabled

is not threadsafe and it's causing issues when there are multiple calls happening

Thanks — I hear that

set_audio_enabled

is causing thread-safety problems in your environment and you need a safe alternative that prevents any user speech from triggering LLM calls or being added to chat_ctx before the agent’s first assistant message. Below are clear, thread-safe alternatives and concrete patterns you can adopt without toggling audio state. Short summary (recommended approach) • Stop toggling audio. Instead, keep STT/VAD running but filter or gate transcripts/turns until the agent’s first reply is complete. • Use a single, thread-safe flag or manual-turn control so concurrent events don’t race over session state. Options (pick one based on your needs) • Transcript filter with a single atomic flag (simple, robust) ◦ Keep STT running but drop any

user_input_transcribed

events while

first_reply_sent == False

. ◦ Use a thread-safe boolean (e.g., an asyncio.Lock or atomic primitive) so multiple callbacks cannot race writing/reading it. ◦ This prevents any LLM calls and ensures chat_ctx is not updated. Example sketch (Python-style):

import asyncio

  first_reply_sent = False
  flag_lock = asyncio.Lock()

  async def set_first_reply_sent():
      async with flag_lock:
          first_reply_sent = True

  @session.on("user_input_transcribed")
  async def on_transcript(transcript):
      async with flag_lock:
          if not first_reply_sent:
              return  # discard safely
      # process transcript: add to chat_ctx and call LLM

• Manual turn control (turn_detection="manual") + commit logic (best when your platform supports it) ◦ Disable automatic turn processing so the session never auto-triggers LLM. ◦ After producing and finishing the first assistant message, manually accept or commit future user turns. ◦ This prevents partial-turn corner cases and avoids toggling audio state entirely. Pattern: 1. Start session with

turn_detection="manual"

. 2. Generate the first assistant reply. 3. On playback_finished (or equivalent), set

can_accept_input = True

. 4. Only call

commit_user_turn()

or process a turn when

can_accept_input

is True. • Buffering pre-first-reply transcripts (if you must keep early speech for analytics) ◦ Instead of discarding, append early transcripts to a bounded buffer. ◦ On first assistant reply completion, either drop the buffer or flush it into analytics (but do not add to chat_ctx or call LLM). ◦ This avoids losing data but keeps LLM context clean. Sketch:

prebuffer = []
  @session.on("user_input_transcribed")
  def on_transcript(t):
      if not first_reply_sent:
          prebuffer.append(t)
          return
      process_transcript(t)

  # After first assistant reply:
  prebuffer.clear()  # or archive elsewhere

• STT pipeline interception (advanced, safest for concurrency) ◦ Intercept at the STT node or RoomIO layer and drop early transcripts there. ◦ Handles all concurrency inside a single pipeline, avoiding shared session state mutation. ◦ Set a flag like

first_speech_complete

from your playback_finished/agent_speech_started event. Conceptual sketch: • In your STT processing coroutine, while

not first_speech_complete

, discard STT events. After flag flips, resume normal processing. Why these avoid your thread-safety problem • You no longer call

set_audio_enabled()

repeatedly from multiple threads/callbacks. • All gating is a single-flag check or done inside a single pipeline, so you can control concurrency with standard locks or by running filtering in one event loop. • No re-configuration of audio tracks, avoiding stream lifecycle races. Potential pitfalls and mitigation • STT/VAD resource use: STT will still consume CPU/network while you discard transcripts. If that matters, add lightweight debounce (e.g., drop repeated frames or limit prebuffer size). • Flag concurrency: use an asyncio.Lock, threading.Lock, or atomic boolean to prevent races when reading/updating the flag. • Buffer growth: if you buffer early transcripts, bound the buffer by count or time. Quick clarifying questions so I can give you copy-paste code that fits: 1. Are you using voice-only STT, text input, or both? (If text input must also be blocked, we’ll gate text messages the same way.) 2. Which SDK/language and event model do you use (Python async server session, Node, or client)? 3. How do you detect the agent’s first message completion (e.g.,

playback_finished

,

agent_speech_started

, or a resolved

generate_reply()

future)? If you reply with answers to those, I’ll provide a thread-safe, ready-to-run snippet matched to your stack. Sources: Turns overview | LiveKit Documentation | How to detect when an agent has finished speaking | Pipeline nodes and hooks | LiveKit Documentation