I noticed Google just released Gemini 3.1 Flash Live. I want to use it as my STT and TTS but keep the ElevenLabs voice ID that I have. How do I do this? https://deepmind.google/models/model-cards/gemini-3-1-flash-live/

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

So what you are suggesting is in my case keep my Deepgram as the STT and ElevenLabs as TTS. We are just changing the LLM from what I have as OpenAI to Gemini Flash Live. Is that what you are suggesting?

Yes — that’s exactly right. You can keep Deepgram for STT and ElevenLabs for TTS, and replace your current OpenAI LLM with Gemini Flash Live as the language/reasoning model. The key is to have Gemini handle the LLM work (intent, dialogue, transcription post‑processing, etc.), keep Deepgram doing the live STT ingest, and route Gemini’s text responses into ElevenLabs for synthesis with your existing

voice_id

. Here’s a concise plan and steps to implement that with LiveKit Agents (Python) or a manual/API setup. High-level flow • Deepgram: capture and transcribe live audio → produces text. • Gemini (Realtime/Flash Live): consumes Deepgram text (or receives raw audio if you choose) and performs LLM reasoning. Configure Gemini to produce TEXT output (not native audio). • ElevenLabs: synthesize Gemini’s text responses into audio using your

voice_id

. • LiveKit: play ElevenLabs audio back into the session as the agent’s outgoing audio track. Essential details & cautions • Set Gemini’s

modalities=["TEXT"]

so Gemini returns text rather than producing native audio. This ensures ElevenLabs remains the TTS engine. • If you pick a Gemini model that is “native-audio” only, you won’t be able to force text-only output. Use a realtime/non-native-audio capable model or configure

modalities

accordingly. • Keep your ElevenLabs

voice_id

when creating the ElevenLabs TTS client so the same voice is used. Example integration approaches 1) LiveKit Agents (Python) — conceptual example 1. Ensure the Google plugin is installed and

GOOGLE_API_KEY

is set. 2. Keep your Deepgram integration as the STT source (transcripts come into your agent/session). 3. Configure the agent to use Gemini for LLM (TEXT modality) and ElevenLabs for TTS:

from livekit.plugins import google, elevenlabs
from livekit.agents import AgentSession

session = AgentSession(
    llm=google.realtime.RealtimeModel(
        # choose the Gemini realtime model you have access to
        # e.g., model="gemini-3.1-flash-lite-preview"
        modalities=["TEXT"],
        temperature=0.7,
        instructions="You are a helpful assistant."
    ),
    tts=elevenlabs.TTS(
        voice_id="your_elevenlabs_voice_id",
        model="eleven_multilingual_v2"
    )
)

# Flow: Deepgram transcripts → session.llm.process(text) → get text response
# → session.tts.synthesize(text) → play audio into LiveKit as outgoing track

2) Manual/API wiring (no Agents SDK) • Deepgram: Receive transcription webhook or stream → grab text segments. • Gemini Realtime API: Send text to the Gemini Live model (or send audio if you want Gemini STT; but since you’re using Deepgram, send text). Request TEXT response only. • ElevenLabs API: Send Gemini text output to ElevenLabs synth endpoint with your

voice_id

. • LiveKit: push ElevenLabs audio into the participant’s outgoing track. Practical tips • Keep latency in mind: Deepgram streaming → Gemini processing → ElevenLabs synthesis adds some end-to-end latency. Consider streaming partial transcripts or interim responses if you need faster turnarounds. • Test model behavior with

modalities=["TEXT"]

to ensure Gemini doesn’t return audio tags or native audio content. • Validate ElevenLabs audio format (sample rate, encoding) and convert it to the format your LiveKit client expects. Would you like: • A full, runnable Python example that wires Deepgram’s transcripts into Gemini and then into ElevenLabs audio playback for LiveKit? • Or a short API-by-API example (Deepgram → Gemini REST/Websocket → ElevenLabs REST → LiveKit publish) instead? Tell me which example you prefer and the Gemini model id you have access to (if known), and I’ll produce the exact code and request/response snippets. , with interactive elements

When I prompt Claude to change this. Should I just ask it to change the code where AgentSession is written? How would you prompt Claude to change the LLM?

Good question — asking Claude to make the change is a great shortcut. Below is a concise, copy‑pasteable prompt you can use with Claude to update your code where

AgentSession

is defined so it swaps OpenAI for Gemini Flash Live while keeping your existing Deepgram STT and ElevenLabs TTS. Before you send it, confirm: • the file and function/class name where

AgentSession

is created (e.g.,

agent.py

,

main.py

), and • the Gemini realtime model id you want to use (if unknown, mention that you want a realtime Gemini model and Claude can leave a placeholder). Prompt to give Claude --- Please update my code so the LiveKit Agent uses Gemini Flash Live as the LLM instead of OpenAI, while keeping Deepgram for STT and ElevenLabs for TTS. Requirements: • Locate the place where

AgentSession

(or equivalent) is instantiated and replace the OpenAI/`openai` LLM configuration with Google Gemini Realtime using the

livekit.plugins.google.realtime.RealtimeModel

. • Configure the Gemini RealtimeModel to return text only by setting

modalities=["TEXT"]

so ElevenLabs remains the TTS engine. • Preserve existing Deepgram integration (no change to STT wiring). • Preserve existing ElevenLabs TTS usage; ensure the ElevenLabs

voice_id

remains configured and is used to synthesize Gemini text responses. • Add or update imports as needed: ◦

from livekit.plugins import google, elevenlabs

◦ keep any existing

openai

imports removed or commented out if no longer used. • Use a clear placeholder for the Gemini model id if you don't know the exact id (e.g.,

model="GEMINI_REALTIME_MODEL_ID"

). • Provide a short runnable example block showing: a.

AgentSession

constructed with

llm=google.realtime.RealtimeModel(modalities=["TEXT"], model="GEMINI_REALTIME_MODEL_ID", temperature=0.7)

b.

tts=elevenlabs.TTS(voice_id="YOUR_ELEVENLABS_VOICE_ID", model="eleven_multilingual_v2")

c. A short comment showing the flow: Deepgram transcript → session.llm.process(text) → session.tts.synthesize(text) → publish audio to LiveKit. • If any other code changes are required (for example, handling Gemini-specific response fields), note them inline as comments rather than making broad structural changes. • Return only the modified code block(s) and minimal inline comments; do not include long explanatory text. If you need the exact file name or Gemini model id to be filled in, ask me for that before making changes. Example expected output (short snippet only):

from livekit.plugins import google, elevenlabs
from livekit.agents import AgentSession

session = AgentSession(
    llm=google.realtime.RealtimeModel(
        model="GEMINI_REALTIME_MODEL_ID",
        modalities=["TEXT"],
        temperature=0.7,
        instructions="You are a helpful assistant."
    ),
    tts=elevenlabs.TTS(
        voice_id="YOUR_ELEVENLABS_VOICE_ID",
        model="eleven_multilingual_v2"
    )
)
# Flow: Deepgram transcripts -> session.llm.process(text) -> session.tts.synthesize(text) -> publish audio to LiveKit

Would you like me to produce the exact updated code for your repository now? If so, tell me: • the filename where

AgentSession

is instantiated, and • the Gemini realtime model id (or say “leave placeholder”)., with interactive elements

The Gemini Realtime model id is Gemini 3.1 Flash Live. Can I call within my AgentSession? Will LiveKit give me access to it?

Thanks — I hear you: you want to call the Gemini 3.1 Flash Live model directly inside your AgentSession and you want to know whether LiveKit will give you access. Short answer • The exact model name “Gemini 3.1 Flash Live” is not documented as a supported model ID in LiveKit. LiveKit does support Gemini realtime models via

google.realtime.RealtimeModel

, but the confirmed realtime preview ID in the docs is

gemini-2.5-flash-native-audio-preview-12-2025

. Gemini 3.x Flash Lite and Pro text models (e.g.,

google/gemini-3.1-flash-lite-preview

) are available for inference, but the specific “3.1 Flash Live” realtime ID is not confirmed. What this means for you • You can absolutely call a Gemini realtime model from inside

AgentSession

using

google.realtime.RealtimeModel(...)

if you have a supported realtime model ID. • If Google has already made

gemini-3.1-flash-live

available to your Google project, you can try to pass that model ID into

RealtimeModel(model="gemini-3.1-flash-live")

— but there’s no guarantee LiveKit’s docs recognize that exact name yet. • If the model ID is not available, use one of the supported realtime preview IDs (for example

gemini-2.5-flash-native-audio-preview-12-2025

) or use a Gemini 3.1 text model (

google/gemini-3.1-flash-lite-preview

) with

modalities=["TEXT"]

if you want text-only output and will keep ElevenLabs for TTS. How to proceed (recommended steps) 1. Check whether the model is enabled in your Google project (Gemini Live API / Vertex AI): if Google has granted access to

gemini-3.1-flash-live

, it will be visible in your Google Cloud model list or Gemini Live console. 2. Try a quick test in your AgentSession: ◦ If you want realtime/native audio capabilities: attempt

google.realtime.RealtimeModel(model="gemini-3.1-flash-live")

. ◦ If you only need Gemini as the LLM (text) and will keep Deepgram for STT + ElevenLabs for TTS: use

model="google/gemini-3.1-flash-lite-preview"

and set

modalities=["TEXT"]

. 1. If you get errors or the model isn’t found, fall back to one of the confirmed realtime preview IDs (e.g.,

gemini-2.5-flash-native-audio-preview-12-2025

) or the Gemini 3.1 Flash Lite text model. Example (how to call in AgentSession) • Realtime/native audio attempt (only if model is available):

from livekit.plugins import google
from livekit.agents import AgentSession

session = AgentSession(
    llm=google.realtime.RealtimeModel(
        model="gemini-3.1-flash-live",   # try this if Google enabled it for you
        modalities=["AUDIO","TEXT"],    # or set modalities as you need
        temperature=0.7,
    ),
    # tts and other config unchanged
)

• Text-only (use Gemini text model + ElevenLabs TTS):

session = AgentSession(
    llm=google.realtime.RealtimeModel(
        model="google/gemini-3.1-flash-lite-preview",
        modalities=["TEXT"],
        temperature=0.7
    ),
    # keep ElevenLabs tts configured with your voice_id
)

If you want, I can: • Try a quick test snippet you can run: give me the exact error/output if it fails and I’ll help debug. • Produce a ready-to-run AgentSession code block with your ElevenLabs

voice_id

and your preferred fallback model ID filled in. • Walk through how to confirm model availability in your Google Cloud/Vertex console. Which would you like me to help with next — a test snippet to run, or a full AgentSession code block with fallbacks? Sources: Gemini Live API plugin | LiveKit Documentation | Gemini Realtime Agent with Live Vision | LiveKit Documentation | LiveKit Inference | LiveKit Documentation | Google Gemini LLM | LiveKit Documentation, with interactive elements

So I turn this code: session = AgentSession( stt=stt_instance, llm=openai.LLM( model=_llm_model_, temperature=temperature, # CRITICAL: Must be 0.0 to prevent question generation ), tts=tts_instance, vad=vad_instance, _turn_detection_=“server_vad”, # Enable turn detection for event handling (FIXED: was “manual”) _allow_interruptions_=True, # Session default; per-call overrides for warnings/acks _min_interruption_duration_=0.5, # Min user speech to trigger interrupt _discard_audio_if_uninterruptible_=True, # Drop user audio during non-interruptible TTS ) into this code: session = AgentSession( stt=stt_instance,

llm=google.realtime.RealtimeModel(
        model="google/gemini-3.1-flash-lite-preview",
        modalities=["TEXT"],
        temperature=temperature
    ),

tts=tts_instance, vad=vad_instance, _turn_detection_=“server_vad”, # Enable turn detection for event handling (FIXED: was “manual”) _allow_interruptions_=True, # Session default; per-call overrides for warnings/acks _min_interruption_duration_=0.5, # Min user speech to trigger interrupt _discard_audio_if_uninterruptible_=True, # Drop user audio during non-interruptible TTS ) Does that look right?

Yes — that change looks correct for switching the LLM to Gemini as a text-only model while keeping your existing STT and TTS. Nice and straightforward. A few small confirmations and tips to make this robust: • Functionality: Using

google.realtime.RealtimeModel

with

modalities=["TEXT"]

tells the model to return text only, so your Deepgram STT and ElevenLabs TTS remain the audio pipeline. That matches your intent. • Model choice: You used

google/gemini-3.1-flash-lite-preview

. That is a text-capable Gemini 3.1 Flash Lite model. If you later get access to a realtime/native-audio Gemini 3.1 Live model and want native audio, you would switch model id and modalities accordingly — but for your current flow, the Flash Lite + TEXT modality is appropriate. • Imports and env: Make sure you import the Google plugin: ◦

from livekit.plugins import google

And ensure your Google credentials are set (e.g.,

GOOGLE_API_KEY

or appropriate Vertex credentials) so the plugin can call Gemini. • Preserve critical behavior: You noted the OpenAI config required

temperature=0.0

to prevent question generation. If that behavior matters, set

temperature=0.0

(or your desired value) on the Gemini model as well — Gemini respects temperature-like params for deterministic output. • Minor syntax checks: In your pasted snippet ensure commas and quotes are correct and consistent in your real file. Example placement: ◦

llm=google.realtime.RealtimeModel(...)

(with a trailing comma after the llm arg if needed) ◦ Use straight quotes for strings (not curly smart quotes). • Example integrated snippet (conceptual):

from livekit.plugins import google
# other imports...

session = AgentSession(
    stt=stt_instance,
    llm=google.realtime.RealtimeModel(
        model="google/gemini-3.1-flash-lite-preview",
        modalities=["TEXT"],
        temperature=temperature  # set to 0.0 if you need deterministic output
    ),
    tts=tts_instance,
    vad=vad_instance,
    turn_detection="server_vad",
    allow_interruptions=True,
    min_interruption_duration=0.5,
    discard_audio_if_uninterruptible=True,
)

• Testing tip: Run a short session test to confirm: ◦ Deepgram transcripts reach the agent unchanged. ◦ Gemini returns only text (no audio payload or unexpected fields). ◦ ElevenLabs synthesis accepts the Gemini text and produces audio with your

voice_id

. ◦ Verify latency and any needed audio format conversion before publishing into LiveKit. If you want, I can: • Produce a full runnable example with error handling and sample calls to Gemini and ElevenLabs, or • Show how to add a fallback when Gemini 3.1 Live isn’t available (e.g., toggle model id at runtime). Which would you prefer? , with interactive elements

Got an error when I just change the llm: 155955.736 ERROR… livekit.agents unhandled exception while running the job task Traceback (most recent call last): File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-pa ckages/opentelemetry/util/_decorator.py”, line 71, in async_wrapper return await func(*args, **kwargs) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-pa ckages/livekit/agents/ipc/job_proc_lazy_main.py”, line 292, in _traceable_entrypoint await self._job_entrypoint_fnc(job_ctx) File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/agent.py”, line 239, in entrypoint session = await create_moderator_session( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/src/moderator_agent.py”, line 5116, in create_moderator_session llm=google.realtime.RealtimeModel( ^^^^^^^^^^^^^^^ AttributeError: ‘NoneType’ object has no attribute ‘realtime’

Thanks for the error trace — that helps a lot. You’re seeing: AttributeError: ‘NoneType’ object has no attribute ‘realtime’ That means the variable

google

is None when your code tries to access

google.realtime.RealtimeModel

. Usually this happens because the Google plugin object wasn’t imported or initialized properly (or the plugin extra isn’t installed), so I’ll walk you through the most likely causes and fixes. Why this happens •

google

is coming from

from livekit.plugins import google

. If that import failed or returned None (for example the plugin extra isn’t installed),

google

will be None and

google.realtime

will raise the AttributeError. • It can also happen if the environment variables/credentials required by the plugin are missing and the plugin initialization short-circuits (less common, but check credentials after fixing the import). Checks & fixes (quick checklist) 1. Ensure the Google plugin is installed ◦ If you used the LiveKit Agents extras, install the Google extra: ▪︎ uv/pip install or your project’s package manager should include

"livekit-agents[google]~=1.4"

(or the version your project expects). ◦ Verify the plugin is present in your virtualenv:

pip show livekit-agents

and that extras were installed. 1. Confirm imports in the file where you instantiate AgentSession ◦ Add at top of file:

from livekit.plugins import google

• If you previously removed or replaced the import when switching from OpenAI, restore it. 1. Confirm

google

is not None at runtime ◦ Add a quick sanity check before using it:

if google is None:
         raise RuntimeError("google plugin import returned None; check installation")

• This will fail fast and point you back to plugin installation/import issues. 1. Verify credentials/environment variables ◦ Ensure

GOOGLE_API_KEY

or required Vertex/GCP credentials are set in your environment so the plugin can operate. Missing credentials normally don’t make the import None, but they can cause later failures — set them early. 1. Example minimal snippet (place near where you build session)

from livekit.plugins import google
   from livekit.agents import AgentSession

   assert google is not None, "google plugin is None - check installation and imports"

   session = AgentSession(
       stt=stt_instance,
       llm=google.realtime.RealtimeModel(
           model="google/gemini-3.1-flash-lite-preview",
           modalities=["TEXT"],
           temperature=temperature
       ),
       tts=tts_instance,
       vad=vad_instance,
       turn_detection="server_vad",
       allow_interruptions=True,
       min_interruption_duration=0.5,
       discard_audio_if_uninterruptible=True,
   )

1. If you still get the None error ◦ Confirm you don’t have a local variable named

google

shadowing the import. ◦ Search your repo for

google =

accidental assignments. ◦ Run a small REPL test in the same virtualenv:

>>> from livekit.plugins import google
     >>> google
     <module 'livekit.plugins.google' ...>   # expected

If that returns None or raises, the package install/import is the problem. If you want, I can: • Produce the exact pip/uv install command to add the plugin for your project layout. • Review the top of your

agent.py

or

moderator_agent.py

file (paste the imports) and point out what to change. • Provide a small runnable test script you can run in your venv to confirm the plugin import and credentials are working. Which would help most right now — an install command, or paste your file’s import section and I’ll review it? , with interactive elements

Looks like I need to put into my pyproject.toml. What version of livekit.plugins import google should I install?

Yes — add the LiveKit Agents Google extra to your pyproject.toml so

from livekit.plugins import google

is available. Recommended dependency • Use the LiveKit Agents package with the Google extra at the 1.4 series: ◦ "livekit-agents[google] ~= 1.4" How to add it to pyproject.toml (poetry-style example) • Under [tool.poetry.dependencies] (or your pyproject tool’s dependency section), add: ◦ livekit-agents = {version = "~=1.4", extras = ["google"]} Example block:

[tool.poetry.dependencies]
python = "^3.12"
livekit-agents = { version = "~=1.4", extras = ["google"] }
# other deps...

If you use pip-style installs or a requirements tool, the equivalent installer command is: • pip: pip install "livekit-agents[google]~=1.4" • uv (if you use uv): uv add "livekit-agents[google]~=1.4" After installing • Verify the import in your venv: ◦ Run a short REPL test:

>>> from livekit.plugins import google
    >>> google
    <module 'livekit.plugins.google' ...>

• If that returns None or raises, the package/extras weren’t installed correctly. Environment/credentials • Ensure your Google credentials are set (e.g.,

GOOGLE_API_KEY

or Vertex AI credentials) before running the agent — the plugin will need valid credentials to call Gemini models. Notes about model IDs • The package gives you access to LiveKit’s Google plugin. If you plan to use Gemini in text mode,

google/gemini-3.1-flash-lite-preview

is appropriate with

modalities=["TEXT"]

. • If you try

model="gemini-3.1-flash-live"

, that ID may not be recognized unless Google has enabled it for your project. If you see model-not-found errors, fall back to a confirmed ID (e.g.,

gemini-2.5-flash-native-audio-preview-12-2025

for realtime preview or

google/gemini-3.1-flash-lite-preview

for text). Would you like: • A short test script I can provide to confirm the plugin import and a simple Gemini call work in your environment, or • Help editing your pyproject.toml directly (paste your current file and I’ll show the exact insertion)? Which would be most helpful now? Sources: Google Cloud STT plugin guide | LiveKit Documentation | Google Gemini LLM | LiveKit Documentation | LiveKit Inference | LiveKit Documentation, with interactive elements

What else do I need to add? I got another error: Traceback (most recent call last): File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/agent.py”, line 39, in <module> from src.moderator_agent import create_moderator_session, SurveyState File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/src/__init__.py”, line 2, in <module> from .moderator_agent import CommunityModeratorAgent, create_moderator_session File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/src/moderator_agent.py”, line 18, in <module> from livekit.plugins import openai, google, silero, noise_cancellation File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-packages/livekit/plugins/google/__init__.py”, line 22, in <module> from . import beta, realtime, tools File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-packages/livekit/plugins/google/beta/__init__.py”, line 1, in <module> from .. import realtime File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-packages/livekit/plugins/google/realtime/__init__.py”, line 2, in <module> from .realtime_api import RealtimeModel File “/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-packages/livekit/plugins/google/realtime/realtime_api.py”, line 18, in <module> from livekit.agents import APIConnectionError, LanguageCode, llm, utils ImportError: cannot import name ‘LanguageCode’ from ‘livekit.agents’ (/Users/ganeshkrishnan/Documents/Lever_AI_FINAL/ai-moderator-agent-edited-main/.venv/lib/python3.12/site-packages/livekit/agents/__init__.py)

Thanks — this error is helpful. You’re hitting: ImportError: cannot import name ‘LanguageCode’ from ‘livekit.agents’ That means the google realtime plugin expects

LanguageCode

to exist in the livekit.agents package you installed, but the installed livekit-agents version in your venv doesn't export that symbol. In short: a version mismatch between the plugin code and the core agents package. What to do next (step-by-step) 1. Confirm your installed livekit packages ◦ In your virtualenv run: ▪︎ pip show livekit-agents ▪︎ pip show livekit ◦ Note the versions shown. 1. Upgrade to the matching livekit-agents release ◦ Install the recommended package with the google extra: ▪︎ pip install --upgrade "livekit-agents[google]~=1.4" ◦ Or update your pyproject.toml to:

livekit-agents = { version = "~=1.4", extras = ["google"] }

then run your dependency tool (poetry install / pip sync / uv sync). 1. Make sure livekit core and agents are consistent ◦ If you have a separate

livekit

package installed, ensure it’s compatible with

livekit-agents

1.4. After upgrading, re-check: ▪︎ pip show livekit ▪︎ pip show livekit-agents ◦ If

livekit

is an older incompatible version, upgrade it as well: ▪︎ pip install --upgrade livekit 1. Clear stale installs if necessary ◦ Sometimes leftover files cause import mismatches. If errors persist: ▪︎ Deactivate venv, remove it, recreate it ▪︎ Reinstall dependencies fresh (including livekit-agents[google]) 1. Quick sanity test after upgrade ◦ In a Python REPL inside the same venv:

from livekit.plugins import google
     from livekit.agents import LanguageCode
     print(google, LanguageCode)

• Expected:

google

is a module and

LanguageCode

is present (not ImportError). 1. If you still see ImportError ◦ Search your project for another module named

livekit

or

livekit.agents

that could shadow the package. ◦ Ensure your project’s source tree does not contain a folder/file named

livekit

(that would be imported instead of the installed package). Why this fix works • The plugin’s code (realtime_api.py) imports

LanguageCode

from livekit.agents. That symbol was introduced/exported in newer agent releases; using an older agents package causes the ImportError. Upgrading to the livekit-agents release that includes the Google plugin’s expectations resolves the mismatch. If you want, I can: • Provide the exact pip commands and a short script to verify imports (REPL snippet). • Review your pyproject.toml if you paste it here and suggest the exact edits. • Help you recreate the venv step-by-step to ensure a clean install. Which would you like me to do next? , with interactive elements

Here are my dependencies in my pyproject.toml: dependencies = [ “livekit-agents[silero,turn-detector]~=1.2”, “livekit-plugins-noise-cancellation~=0.2", “livekit-agents[google] ~= 1.4”, “livekit-agents[openai]~=1.2", “livekit-agents[anthropic]~=1.2”, “livekit-agents[anam]~=1.4", “livekit-plugins-deepgram~=1.2”, “livekit-plugins-elevenlabs>=1.2", “python-docx>=1.1.0”, “python-dotenv>=1.0.0", “pandas>=2.3.3”, “fastapi>=0.128.6", “uvicorn>=0.39.0”, “python-multipart>=0.0.20", “jinja2>=3.1.6”, ]

Thanks — this is exactly the issue. I see mixed livekit-agents versions and extras in your dependency list (some 1.2, some 1.4). That mismatch causes the ImportError because parts of the Google plugin expect symbols (like

LanguageCode

) that only exist in the newer agents release. What to change • Use a single, consistent livekit-agents version for all extras. Upgrade all livekit-agents entries to the same 1.4 series (or whichever single version you intend to target). • Remove duplicate livekit-agents lines with different extras; specify one livekit-agents dependency with all extras combined. Suggested pyproject.toml dependency block • Replace your current livekit-agents lines with one unified entry:

dependencies = [
    "livekit-agents = { version = \"~=1.4\", extras = [\"silero\",\"turn-detector\",\"google\",\"openai\",\"anthropic\",\"anam\"] }",
    "livekit-plugins-noise-cancellation ~=0.2",
    "livekit-plugins-deepgram ~=1.2",
    "livekit-plugins-elevenlabs >=1.2",
    "python-docx >=1.1.0",
    "python-dotenv >=1.0.0",
    "pandas >=2.3.3",
    "fastapi >=0.128.6",
    "uvicorn >=0.39.0",
    "python-multipart >=0.0.20",
    "jinja2 >=3.1.6",
]

Why this fixes the ImportError • The google plugin code in your venv imports

LanguageCode

from

livekit.agents

. That symbol is present in the livekit-agents 1.4 series but not in older 1.2 exports. Mixing versions meant the plugin code and the core agents package were out of sync, causing the import failure. • Using one livekit-agents package with all extras ensures the plugin modules and core exports align. Next steps (recommended) 1. Update pyproject.toml with the unified dependency above. 2. Reinstall your environment: ◦ If using Poetry: run

poetry lock --no-update

then

poetry install

(or simply

poetry update livekit-agents

), or edit and run the normal install flow you use. ◦ If using pip/requirements, recreate the virtualenv and run pip install for the updated set: ▪︎ python -m venv .venv ▪︎ source .venv/bin/activate ▪︎ pip install -U pip ▪︎ pip install "livekit-agents[google,silero,turn-detector,openai,anthropic,anam]~=1.4" livekit-plugins-noise-cancellation~=0.2 livekit-plugins-deepgram~=1.2 livekit-plugins-elevenlabs>=1.2 ... 1. Verify versions in the venv: ◦ pip show livekit-agents ◦ pip show livekit Confirm livekit-agents is ~1.4 and that there’s no older livekit package shadowing it. 1. Quick sanity checks in a REPL:

from livekit.plugins import google
   from livekit.agents import LanguageCode
   print(google, LanguageCode)

Both should import without error. If anything goes wrong • If your lockfile or dependency resolver refuses the combined extras, paste your lockfile errors here and I’ll help resolve them. • If you prefer, paste the exact tool you use for dependencies (poetry, pip-tools, pip with requirements.txt) and I’ll give exact commands. Would you like me to: • Provide the exact pip install command that installs the unified extras, or • Help craft a Poetry-friendly change (I can show the exact [tool.poetry.dependencies] snippet and commands)? Which option is best for your workflow? , with interactive elements

What did I do wrong in my pyproject.toml? error: Failed to generate package metadata for

ai-moderator-agent==0.1.0 @ editable+.

Caused by: The build backend returned an error Caused by: Call to

hatchling.build.prepare_metadata_for_build_editable

failed (exit status: 1) [stderr] Traceback (most recent call last): File “<string>“, line 14, in <module> File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/build.py”, line 142, in prepare_metadata_for_build_editable f.write(builder.config.core_metadata_constructor(builder.metadata, extra_dependencies=extra_dependencies)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/metadata/spec.py”, line 576, in construct_metadata_file_2_4 if metadata.core.dependencies: ^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/metadata/core.py”, line 1220, in dependencies self._dependencies = list(self.dependencies_complex) ^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/metadata/core.py”, line 1195, in dependencies_complex requirement = Requirement(self.context.format(entry)) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/utils/context.py”, line 110, in format return self.__formatter.format(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/opt/anaconda3/lib/python3.12/string.py”, line 190, in format return self.vformat(format_string, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/utils/context.py”, line 146, in vformat result, _ = self._vformat(format_string, args, kwargs, used_args, 10) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/opt/anaconda3/lib/python3.12/string.py”, line 234, in _vformat obj, arg_used = self.get_field(field_name, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/opt/anaconda3/lib/python3.12/string.py”, line 299, in get_field obj = self.get_value(first, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/Users/ganeshkrishnan/.cache/uv/builds-v0/.tmpC8UmLx/lib/python3.12/site-packages/hatchling/utils/context.py”, line 159, in get_value raise ValueError(message) from None ValueError: Unknown context field

version = “~=1

hint: This usually indicates a problem with the package or the build environment.

Thanks — this error pinpoints a syntax problem in your pyproject.toml. Hatchling is trying to format dependency entries and encountering malformed strings like

version = “~=1

because your dependencies list used quoted strings with embedded extras instead of proper TOML table syntax. I’ll show the correct way to declare the unified livekit-agents dependency (with extras) and a properly formatted pyproject snippet you can drop in. What went wrong • You used a flat list of quoted dependency strings that included bracketed extras (e.g.,

"livekit-agents[google] ~= 1.4"

). Your project tool (hatchling/uv) expects TOML structures for complex dependency metadata and the string formatting confused the backend, causing the ValueError: Unknown context field

version = “~=1

. • Also you had multiple livekit-agents entries at different versions, which caused a runtime mismatch. Both the syntax and the mixed versions needed fixing. Correct approach (Poetry / pyproject TOML style) • Use a single livekit-agents entry and list all extras in the extras array. • Provide normal TOML comparison operators (no escaped brackets inside plain strings). Example pyproject.toml dependency section (Poetry-style) Replace your current dependencies block with something like this under [tool.poetry.dependencies] or the equivalent for your build tool:

[tool.poetry.dependencies]
python = "^3.12"

# Single unified livekit-agents entry with all needed extras
livekit-agents = { version = "~=1.4", extras = ["silero", "turn-detector", "google", "openai", "anthropic", "anam"] }

# Plugin libs
livekit-plugins-noise-cancellation = "~=0.2"
livekit-plugins-deepgram = "~=1.2"
livekit-plugins-elevenlabs = ">=1.2"

# Other deps
python-docx = ">=1.1.0"
python-dotenv = ">=1.0.0"
pandas = ">=2.3.3"
fastapi = ">=0.128.6"
uvicorn = ">=0.39.0"
python-multipart = ">=0.0.20"
jinja2 = ">=3.1.6"

If you use a different front-end (not Poetry) • If you’re using hatch/uv with a simpler dependencies array in pyproject, avoid embedding extras as strings. Instead either: ◦ Use the table form shown above (preferred), or ◦ Use pip-style install commands to set up your venv, e.g.: pip install "livekit-agents[google,silero,turn-detector,openai,anthropic,anam]~=1.4" livekit-plugins-noise-cancellation~=0.2 livekit-plugins-deepgram~=1.2 livekit-plugins-elevenlabs>=1.2 ... After editing pyproject.toml 1. Recreate or refresh your virtualenv to avoid stale installs: ◦ python -m venv .venv ◦ source .venv/bin/activate ◦ pip install -U pip ◦ If using Poetry: run

poetry install

(or

poetry update livekit-agents

) ◦ If using pip, run the pip install line above. 1. Verify installed versions: ◦ pip show livekit-agents ◦ pip show livekit Confirm livekit-agents is ~=1.4 and that there’s only one livekit-agents package in the env. 1. Sanity test imports:

from livekit.plugins import google
   from livekit.agents import LanguageCode
   print("google:", google)
   print("LanguageCode:", LanguageCode)

Both should import without ImportError. If you want, I can: • Provide the exact pyproject snippet formatted for your specific tool (Poetry, hatch, or uv). Paste the top of your current pyproject.toml and I’ll patch it directly. • Give the precise pip install command to run in your venv if you prefer not to modify pyproject right now. • Walk you through recreating the venv and confirming the imports step-by-step. Which would you like me to do next — patch your pyproject.toml for Poetry, give a pip install command, or walk through the venv recreate commands? , with interactive elements

Here is my actual pyproject.toml: [project] name = “ai-moderator-agent” version = “0.1.0" description = “AI-powered community discussion moderator using LiveKit” readme = “README.md” requires-python = “>=3.10” dependencies = [ “livekit-agents = { version = \“~=1.4\“, extras = [\“silero\“,\“turn-detector\“,\“google\“,\“openai\“,\“anthropic\“,\“anam\“] }“, “livekit-plugins-noise-cancellation ~=0.2”, “livekit-plugins-deepgram ~=1.2", “livekit-plugins-elevenlabs >=1.2”, “python-docx >=1.1.0", “python-dotenv >=1.0.0”, “pandas >=2.3.3", “fastapi >=0.128.6”, “uvicorn >=0.39.0", “python-multipart >=0.0.20”, “jinja2 >=3.1.6", ] [project.optional-dependencies] dev = [ “pytest>=7.0.0”, “pytest-asyncio>=0.21.0", “black>=23.0.0”, “flake8>=6.0.0", “mypy>=1.0.0”, ] [build-system] requires = [“hatchling”] build-backend = “hatchling.build” [tool.hatch.build.targets.wheel] packages = [“src”] [tool.black] line-length = 88 target-version = [‘py39’] [tool.mypy] python_version = “3.9” warn_return_any = true warn_unused_configs = true disallow_untyped_defs = false

Thanks — this pinpoints the problem: your current pyproject.toml places a TOML table (the braces + extras) inside a quoted string in the dependencies array, which breaks hatchling’s metadata formatting. I’ll show the correct fixes and give you exact copy‑pasteable sections and commands so you can rebuild cleanly. What’s wrong • This line is incorrect because it’s a quoted string containing table syntax: "livekit-agents = { version = \"~=1.4\", extras = [\"silero\",\"turn-detector\",\"google\",\"openai\",\"anthropic\",\"anam\"] }" • Hatchling tries to format that string and treats the braces/keys as format placeholders, causing the ValueError: Unknown context field

version = “~=1

. Two correct options — pick the one that matches your tooling Option A — If you use PEP 621 [project] dependencies (simple list form used by some tools like hatch/uv) • Use PEP 508 style strings for extras (no table syntax). Replace the erroneous dependency with a plain requirement string: dependencies = [ "livekit-agents[google,silero,turn-detector,openai,anthropic,anam]~=1.4", "livekit-plugins-noise-cancellation~=0.2", "livekit-plugins-deepgram~=1.2", "livekit-plugins-elevenlabs>=1.2", "python-docx>=1.1.0", "python-dotenv>=1.0.0", "pandas>=2.3.3", "fastapi>=0.128.6", "uvicorn>=0.39.0", "python-multipart>=0.0.20", "jinja2>=3.1.6", ] Option B — If you use Poetry-style [tool.poetry.dependencies] (table syntax) • Move to the table-style dependencies and give livekit-agents as a table with extras. This matches the example I gave earlier. Example (only include if you are actually using Poetry): [tool.poetry.dependencies] python = "^3.12" livekit-agents = { version = "~=1.4", extras = ["silero","turn-detector","google","openai","anthropic","anam"] } livekit-plugins-noise-cancellation = "~=0.2" livekit-plugins-deepgram = "~=1.2" livekit-plugins-elevenlabs = ">=1.2" python-docx = ">=1.1.0" python-dotenv = ">=1.0.0" pandas = ">=2.3.3" fastapi = ">=0.128.6" uvicorn = ">=0.39.0" python-multipart = ">=0.0.20" jinja2 = ">=3.1.6" Which one to pick • If you are using hatch/hatchling or the simple PEP 621 [project] dependencies array currently in your file, choose Option A. • If you’re actually using Poetry, choose Option B (and put the table under [tool.poetry.dependencies]). Recommended next steps (safe rebuild) 1. Edit pyproject.toml to use Option A (quick, works with hatch/uv): ◦ Replace your current dependencies list with the Option A block above. 1. Recreate the virtualenv and reinstall: ◦ python -m venv .venv ◦ source .venv/bin/activate ◦ python -m pip install -U pip ◦ pip install -e . # or use your usual install flow (uv, hatch, etc.) ◦ Alternatively, run pip install "livekit-agents[google,silero,turn-detector,openai,anthropic,anam]~=1.4" livekit-plugins-noise-cancellation~=0.2 livekit-plugins-deepgram~=1.2 livekit-plugins-elevenlabs>=1.2 ... (and any other deps) 1. Verify imports quickly: ◦ python -c "from livekit.plugins import google; from livekit.agents import LanguageCode; print('OK', google, LanguageCode)" 1. Run your app; the earlier ImportError for LanguageCode should be resolved because you’ll have a consistent livekit-agents ~=1.4 install. If you want I can: • Patch your exact pyproject.toml for Option A and return the full file (paste-confirmation), or • Provide the single pip install command with all deps expanded so you can skip editing pyproject and just install into a fresh venv. Which do you prefer — a patched pyproject.toml or the pip install command? , with interactive elements

Hey I got an issue. When I start my LiveKit demo, I do the following: Terminal 1 (Agent): PYTHONPATH=src uv run python agent.py dev Terminal 2 (API): PYTHONPATH=src uv run python web/backend/server.py Terminal 3 (ngrok): ngrok http 8080 Then I share the ngrok url to everyone. The issue this time was I shared the ngrok url to someone that has a mobile device. The moderator couldn’t hear the participant in a smartphone device. The voice agent didn’t acknowledge his audio at all. So what I do to get the voice agent to hear people via smartphone/IPad/Android device?

Thanks — that’s a frustrating demo break, I hear you. Let’s narrow this down and get mobile participants heard by your moderator agent. I’ll list the highest‑impact checks and quick fixes first, then a few diagnostic steps you can run so I can help further. Immediate checks (most likely causes) • Microphone permissions and user gesture: On mobile browsers (Safari, Chrome) the user must grant mic permission and often trigger it with a tap. Make sure your mobile user accepts the mic prompt and that your client calls setMicrophoneEnabled (or equivalent) after a user interaction. • Track published successfully: Confirm the mobile client actually publishes an audio track. If the client shows muted or silence warnings, the track may not be published. • TURN/STUN and ngrok: Mobile networks can need TURN for reliable media. ngrok tunnels your HTTP signaling but does not replace TURN for media. Ensure your LiveKit server/client config has reachable STUN/TURN servers. • VAD/STS/Noise cancellation: Aggressive noise suppression or VAD settings can drop short/quiet mobile speech. Try disabling or loosening VAD/noise-cancellation briefly for testing. Quick tests you can run right now 1. Client console + agent logs ◦ Ask the mobile user to open browser console (remote debugging) or reproduce on desktop mobile emulation and watch for: ▪︎ microphone permission errors ▪︎ MediaStreamError messages ▪︎ “silence detected on local audio track” warnings ◦ On the agent, watch for a track_subscribed event and any STT/VAD logs. If there’s no track_subscribed for that participant, the agent never received audio. 1. Verify published track on join ◦ In your client code, after connecting and enabling mic, log: ▪︎ room.localParticipant.audioTracks (or track IDs) ▪︎ a small snippet: await room.localParticipant.setMicrophoneEnabled(true); console.log("mic enabled, published tracks:", room.localParticipant.audioTracks) ◦ If no track appears, the mic wasn’t published. 1. Try with desktop/another mobile browser ◦ If the same account on a laptop works but the smartphone doesn’t, it’s likely client permissions or browser limitations (Safari iOS being most restrictive). ◦ Test Chrome on Android, Safari on iOS, and compare. Agent-side checks to run • Confirm subscription: ◦ Add a debug handler to print when audio tracks subscribe:

@room.on("track_subscribed")
    def on_track_subscribed(track, participant):
        print("Subscribed to track:", track.kind, "from", participant.identity)

• If you don’t see the mobile participant there, the server didn’t receive their audio. • STT/VAD logs: ◦ Look for VAD transitions (speaking -> silence) and STT start events for that participant. ◦ If VAD never starts for mobile, consider lowering sensitivity or increasing minimum speech length. Mobile-specific recommendations • Use an explicit join/mic enable button (user gesture) that runs both connect and mic enable. Example flow: ◦ User taps “Join” → call connect() → on success call setMicrophoneEnabled(true). • Enable pre‑connect audio buffering so audio capture begins earlier (supported in client SDKs). That helps on slow mobile networks. • If using the web client on iOS Safari, encourage the user to use the latest iOS and to allow microphone when prompted. Safari sometimes routes audio to earpiece or mutes audio until user interacts with the page. • Ask mobile users to disable Bluetooth headsets temporarily (AirPods/ANC can trigger silence detection). Network considerations • Make sure TURN servers are configured and reachable from mobile networks. WebRTC will often need TURN on mobile carriers. • If using ngrok only for signaling, ensure your LiveKit config still exposes STUN/TURN endpoints publicly. If you want me to debug with you, please provide: • Which mobile browser and OS the participant used (Safari iOS, Chrome Android, app, etc.). • A short excerpt of agent logs around the time the mobile user joined (track_subscribed events and STT/VAD traces). • The client code that connects and enables the mic (the join button handler or where you call setMicrophoneEnabled). • Whether you’re using TURN servers (and their config) or relying only on STUN. If you prefer, start by running these quick checks and paste any relevant logs: • Client JS console errors/warnings when the mobile user joins. • Agent “track_subscribed” logs (or absence thereof). I’ll walk you through the next steps as soon as you paste the logs or tell me the mobile browser/OS. Sources: Understanding &#34;silence detected on local audio track&#34; Warning | LiveKit | Understanding &#34;silence detected on local audio track&#34; warning | client-sdk-android | livekit/livekit_composite | DeepWiki | client-sdk-flutter | livekit/livekit_composite | DeepWiki | Why You Shouldn’t Build Real-Time Voice Agents Directly on Model APIs | Liv..., with interactive elements