Hi — nice to meet you! How can I help today? If you’re reaching out about LiveKit, tell me a bit more so I can assist quickly: • Are you setting up a room, troubleshooting a connection, integrating an SDK, or something else? • If it’s an error or bug, please share any error messages, your SDK/language (web, iOS, Android, server), and relevant room or session IDs (if applicable). I’ll follow up with step-by-step help once I have a little more detail.

Thanks — I’ve reviewed the logs, traceback, and your agent code. You’re hitting the error: generate_reply timed out waiting for generation_created event I’m responding to the error in your realtime LLM setup (you’re using google.realtime.RealtimeModel) and the snippet where you call session.generate_reply(). Summary of the likely cause • When using a realtime LLM, LiveKit’s agent framework waits for a server-side event (

generation_created

) that signals the model started generating. The timeout means that event never arrived. • In your session config you set

tts=None

. For voice agents using a realtime model, omitting a TTS instance can break the realtime audio/output pipeline and prevent the expected generation events from being produced or handled. • The traceback shows the timeout happens inside

_realtime_reply_task

at the

self._rt_session.generate_reply()

call, confirming the problem occurs during the realtime generation handshake. Recommended next steps (prioritized) 1. Add a TTS plugin to the session (do not keep

tts=None

) ◦ Replace

tts=None

with a compatible TTS instance (for example, the Google TTS plugin) so the agent has a full audio output path:

session = AgentSession(
       llm=google.realtime.RealtimeModel(voice="Zephyr", temperature=0.8, instructions="You are a helpful assistant"),
       tts=google.TTS(),   # add an appropriate TTS plugin instead of None
       vad=vad,
   )

• This aligns the TTS/audio chain with realtime generation and is the most likely fix. 1. Keep your setup/join ordering (you already do this correctly) ◦ Your pattern (setup before ctx.connect(), then start the session after join) is correct. No change needed here. 1. Simplify the first generate call for testing ◦ Try a minimal

generate_reply

to rule out instruction parsing issues:

await session.generate_reply(instructions="Say hello", allow_interruptions=True)

• If this succeeds, iterate back to your original prompt. 1. Validate API connectivity and credentials ◦ Ensure the Google realtime model credentials/environment variables are correct and reachable from the agent process (invalid creds can cause silent failures). 1. (Optional) Increase the generation timeout temporarily for debugging ◦ Only for debugging: increase any client-side timeout around

generate_reply

to see if the event is delayed rather than missing. Don’t leave this in production. Diagnostic checks to run and share if the issue persists • After adding TTS, share a short excerpt of the logs around the next

generate_reply

attempt (include timestamps). Look for

generation_created

or related realtime session events. • Confirm the exact versions of the

livekit

agents package and the google realtime client you’re using. • Confirm environment variables or keys used by

google.realtime.RealtimeModel

are present in the runtime where the agent runs. If you want, I can: • Review a follow-up log excerpt after you add TTS. • Suggest a minimal reproducible example to run locally to verify realtime generation works end-to-end. Would you like me to review logs after you try adding

google.TTS()

(or another TTS) and re-run the failing flow? Sources: examples/voice_agents | livekit/livekit_composite | DeepWiki

Thanks — this is very helpful. I’ve reviewed your updated agent code and your logging example. Below I’ll cover (1) validation of your realtime audio generation setup and any changes to avoid delays, (2) a few code-level suggestions and small fixes, and (3) how to change Python logging so your logs match the SIP-style format you showed. Quick acknowledgement • I’m responding to your request to verify the agent won’t introduce audio-generation delays and to adjust logging format to include the container name, timestamp, and structured details like your SIP logs. 1) Realtime audio generation — validation and recommendations What looks good • You switched from

tts=None

to

google.beta.GeminiTTS(...)

— that was the right change to provide a full audio output chain for realtime LLMs. • You prewarm and reuse a Silero VAD in process userdata — this reduces cold-start cost. • Setup-before-join and session.start after join are correct and safe for real-time flows. • You added diagnostic hooks (

user_input_transcribed

,

conversation_item_added

,

speech_created

) which will help detect where latency happens. Possible causes of perceived delay and mitigations • LLM/Network latency: realtime models still rely on network/API server response. Some delay may be inherent. Mitigation: test latency from your environment to the Google realtime endpoint and sample end-to-end generation times. • TTS processing: TTS conversion can add time. GeminiTTS may stream audio (if streaming is supported) or may produce audio after generation finishes. To reduce audio start latency: ◦ Prefer streamed generation (if supported by the google.realtime client/plugin). Confirm whether

GeminiTTS

yields interim audio frames; if yes, enable streaming option. ◦ Keep replies short for initial acknowledgements so audio is produced faster. • Preemptive generation: you attempted preemptive triggers on interim transcripts which is good. Avoid simultaneous overlapping generate tasks for the same session if you don’t intend concurrency — race conditions can cause inefficiencies. Use

allow_interruptions=True

as you already do. • VAD tuning: your thresholds are appropriate for 8 kHz SIP audio. If you see missed speech starts or extra trailing silence, tune

activation_threshold

and

speech_end_silence

. Concrete checks to run • Run an isolated end-to-end test: capture timestamps for when

user_input_transcribed

final=true is fired, when

generate_reply

is invoked, when

generation_created

(or similar realtime event) arrives, and when the first audio frame is pushed to LiveKit. That will pinpoint where time is spent. • Verify that Google realtime credentials and region selection are optimized for your deployment (geographic proximity reduces latency). • Confirm that

GeminiTTS

instance in your client is using streaming or chunked output if available. If not, consider a TTS that supports streaming or ask for a config option that enables partial audio emission. 2) Small code suggestions / fixes • Duplicate

@session.on("user_input_transcribed")

handlers: you defined this handler twice. Consolidate to a single handler to avoid message duplication or unexpected multiple generate calls. • Masking log: logging the first 5 chars of the API key is fine for diagnostics, but avoid printing secrets even partially in production logs. • Error handler: in

on_error

you call

session.say(...)

to play a custom audio file — ensure that

session

is still usable at that point (some unrecoverable errors may require session shutdown). • Use

asyncio.create_task

consistently and manage created tasks if you need to cancel them during shutdown. • Consider adding guard logic to avoid spawning multiple concurrent

generate_reply

calls for the same final transcript (e.g., set a per-user generation lock or ignore duplicate final events within a short window). 3) Make your Python logs match the SIP-style format Your desired format: [container-name] 2026-02-13T081753.075Z DEBUG <module> path/file:line message {json meta...} You can approximate this style by configuring Python’s logging to include: container name (static), ISO8601 UTC timestamp with millisecond precision, level, logger name/module, file:line, message, and structured JSON extras. Example logging setup to include in your script (add near top where you configure logging):

import logging
import time
import json
from pythonjsonlogger import jsonlogger  # optional, pip install python-json-logger

CONTAINER_NAME = "blen-call-to-mpesa-agent-1"  # set dynamically if needed

class ContainerFormatter(logging.Formatter):
    def formatTime(self, record, datefmt=None):
        # ISO8601 UTC with ms and Z
        ts = time.gmtime(record.created)
        ms = int(record.msecs)
        return time.strftime("%Y-%m-%dT%H:%M:%S", ts) + ".%03dZ" % ms

    def format(self, record):
        # Base message
        base = super().format(record)
        # Optional: attach JSON metadata if present in record.extra
        extra = {}
        for k, v in getattr(record, "extra", {}).items():
            extra[k] = v
        if extra:
            return f"[{CONTAINER_NAME}]\n{self.formatTime(record)}\t{record.levelname}\t{record.name}\t{record.filename}:{record.lineno}\t{base}\t{json.dumps(extra)}"
        else:
            return f"[{CONTAINER_NAME}]\n{self.formatTime(record)}\t{record.levelname}\t{record.name}\t{record.filename}:{record.lineno}\t{base}"

# Configure root logger
handler = logging.StreamHandler()
fmt = "%(message)s"  # message only — we build full line in ContainerFormatter
handler.setFormatter(ContainerFormatter(fmt=fmt))
root = logging.getLogger()
root.setLevel(logging.DEBUG)
root.handlers = [handler]

Notes on the formatter: • This produces two-line logs where the first line is the container name and the second line resembles your SIP log with tab separators. If you prefer a single-line format, remove the newline between container and timestamp. • If you want fully structured logs, use

python-json-logger

to emit a single JSON object per log line, and include a

container

field. That fits many log-aggregation systems better. How to pass structured metadata (the JSON after the message) • Use the

extra

parameter of logging calls, and modify the formatter to pick up

record.extra

(example above uses

record.extra

). • Example logging call:

<http://logger.info|logger.info>("ignoring incoming text stream due to no handler for topic", extra={"extra": {"nodeID": node, "callID": call}})

Putting it together — sample formatted output (single-line JSON variant) • If you prefer a single-line JSON log similar to many SIP logs but with container included, use python-json-logger:

from pythonjsonlogger import jsonlogger

handler = logging.StreamHandler()
json_fmt = jsonlogger.JsonFormatter('%(asctime)s %(levelname)s %(name)s %(filename)s:%(lineno)s %(message)s %(container)s %(extra)s')
handler.setFormatter(json_fmt)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
root.handlers = [handler]

# set container on each record using a filter
class ContainerFilter(logging.Filter):
    def filter(self, record):
        record.container = CONTAINER_NAME
        record.extra = getattr(record, "extra", {})
        return True

root.addFilter(ContainerFilter())

4) Next diagnostic steps I can run through with you • After you enable the improved logs, re-run and capture logs for a single call. Share the logs around: ◦ VAD detected speech start (user_state_changed) ◦ user_input_transcribed final event ◦ when

generate_reply

is invoked (we can add a debug log right before the call) ◦ any

generation_created

or LLM/TTS events ◦ first audio frame emitted by the agent or

speech_created

event • If you paste the resulting logs here I’ll point exactly which stage is slower and recommend specific micro-optimizations. Would you like: • The single-line JSON logger config instead (for easier ingestion into log systems)? • Me to review a short log sample after you enable this logging and re-run one call?