how can i low the letancy of of ai agent now it is more than 3-4 sec

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

i am using sarvam for tts and stt and gemini for llm

Thanks — since you’re using Sarvam (STT/TTS) and Gemini (LLM), focus on the following high-impact steps to lower your 3–4s latency now: 1. Deploy & network • Run your agent in the same region as Sarvam and Gemini to remove cross-region hops. Measure RTTs with ping/traceroute to both endpoints. 1. Use streaming everywhere • Ensure Sarvam STT is in streaming mode (partial transcripts as speech arrives). • Use Gemini streaming responses (start processing tokens as they come). • Enable streaming TTS from Sarvam so audio can play before full synthesis completes. 1. Start TTS early (preemptive generation) • Begin TTS synthesis as soon as the LLM produces the first tokens (preemptive_generation) so audio playback can start sooner. 1. Consider LiveKit Inference • If possible, route through LiveKit Inference for co-location and provisioned capacity to avoid public API queuing and reduce hops. 1. Tune endpointing / VAD • Reduce

min_endpointing_delay

slightly (default 0.5s) and adjust

max_endpointing_delay

to avoid added wait while not chopping speech. 1. Provider & model choices • For Gemini, prefer lower-latency variants (e.g., Gemini flash) or Realtime endpoints. • If Sarvam’s streaming or low-latency TTS options aren’t available, test alternatives (Deepgram for STT, low-latency TTS models) to compare. 1. Infrastructure • Avoid burstable CPU instances (AWS t3/t4); use m5/c5 or ensure CPU credits aren’t exhausted. • Check network bandwidth and packet loss between components. 1. Measure bottlenecks • Collect TTFT (LLM), TTFB (TTS), and STT latency via Agents Metrics API to see which stage adds most delay. • Example targets: TTFT <500ms, TTFB <300ms, end-to-end <1.5s. If you want, tell me: • which Sarvam models and API mode (streaming vs non-streaming) you’re using, • which Gemini endpoint/model (Realtime/flash), • where your agent is deployed (cloud provider + region and instance type), and I’ll give precise changes you can apply first. Sources: Introducing LiveKit Inference: A unified model interface for voice AI | Google Gemini LLM | LiveKit Docs | Sarvam STT plugin guide | LiveKit Docs | Gemini Live API plugin | LiveKit docs | Observability data hooks | LiveKit docs

how to enable above this

You're asking how to enable the latency optimizations we discussed (streaming STT/TTS, Gemini streaming, preemptive generation, endpointing tuning, metrics). Below are concise, actionable steps and code snippets you can apply right away. 1) Ensure Sarvam streaming STT/TTS • Use the Sarvam plugins in your AgentSession (streaming handled by LiveKit Agents).

from livekit.plugins import sarvam
stt = sarvam.STT(language="hi-IN", model="saarika:v2.5")
tts = sarvam.TTS(target_language_code="hi-IN", speaker="anushka", model="bulbul:v2")

• Verify you are using Sarvam’s streaming endpoints (check your Sarvam client config) so partial transcripts and incremental audio are available. 2) Enable Gemini streaming • Use Gemini via LiveKit Inference or Realtime endpoint so tokens stream as generated:

from livekit.agents import inference
llm = inference.LLM(model="google/gemini-2.5-flash")  # streaming-enabled model

3) Turn on preemptive generation • Start TTS while LLM is still producing tokens:

from livekit.agents import AgentSession
session = AgentSession(
  stt=stt,
  llm=llm,
  tts=tts,
  preemptive_generation=True
)

4) Tune endpointing / VAD • Reduce wait after user stops speaking (adjust conservatively):

session.min_endpointing_delay = 0.3
session.max_endpointing_delay = 2.0

• Ensure a reliable VAD (preload if available) so shorter delays don’t cut users off. 5) Use LiveKit Inference when possible • LiveKit Inference reduces network hops and can use provisioned capacity:

session.llm = "google/gemini-2.5-flash"   # routes via Inference
session.stt = "auto:hi-IN"
session.tts = "sarvam/bulbul:v2"
session.preemptive_generation = True

• If you stay with direct Sarvam, confirm Sarvam supports low-latency streaming for both STT and TTS. 6) Infrastructure checks • Deploy your agent in the same cloud region as Sarvam and Gemini. • Avoid burstable CPU instances (AWS t3/t4); use m5/c5 or ensure CPU credits are fine. 7) Instrument metrics to find bottlenecks • Capture TTFT (LLM), TTFB (TTS), and STT latency via the Agents Metrics API to see which stage is slowest. Aim for TTFT <500ms and TTFB <300ms. If you want, tell me: • exact Sarvam models and whether you’re using Sarvam streaming endpoints, • which Gemini endpoint/model (Realtime or other), • where the agent is deployed (cloud + region and instance type), and I’ll give the precise code/config changes and the next quick tests to run. Sources: Sarvam STT plugin guide | LiveKit Docs | Sarvam TTS plugin guide | LiveKit Docs | Agent speech and audio | LiveKit docs | Simple Call Answering Agent | LiveKit docs | examples/bank-ivr | livekit/livekit_composite | DeepWiki

import config.settings as settings from livekit.plugins import google, sarvam import httpx def get_http_client(): """Factory function to get the configured HTTP client.""" return httpx.AsyncClient(timeout=30.0) def get_llm(): """Factory function to get the configured LLM instance.""" provider = "google" model = "gemini-2.5-flash" if provider == "google": return google.LLM(model=model, api_key=settings.GOOGLE_API_KEY) else: raise ValueError(f"Unsupported AI provider: {provider}") def get_stt(): """Factory function to get the configured STT instance.""" model = "saarika:v2.5" return sarvam.STT( api_key=settings.SARVAM_API_KEY, language="unknown", model=model or "saarika:v2.5", ) def get_tts( language: str = None, voice: str = None): """Factory function to get the configured TTS instance.""" provider = "sarvam" language = language or "hi-IN" # Use provided language or default voice = "anushka" model = "bulbul:v2" return sarvam.TTS( api_key=settings.SARVAM_API_KEY, model= model or "bulbul:v2", speaker=voice or "karun", target_language_code=language or "hi-IN", enable_preprocessing=True, ) this is my llm tts and stt fix this

Thanks — I updated your factory functions to enable streaming-friendly defaults, set a concrete STT language, turn off extra preprocessing, and included the AgentSession settings you need (preemptive generation, VAD, endpointing). Apply these changes: 1) Updated factories

import config.settings as settings
import httpx
from livekit.plugins import google, sarvam

def get_http_client():
    return httpx.AsyncClient(timeout=30.0)

def get_llm():
    provider = "google"
    model = "gemini-2.5-flash"
    if provider == "google":
        return google.LLM(
            model=model,
            api_key=settings.GOOGLE_API_KEY,
            # streaming is used by default when LLM is used in AgentSession
        )
    raise ValueError(f"Unsupported AI provider: {provider}")

def get_stt():
    return sarvam.STT(
        api_key=settings.SARVAM_API_KEY,
        language="hi-IN",            # set real language to avoid detection delay
        model="saarika:v2.5",
        enable_preprocessing=False,  # disable heavy preprocessing to save time
    )

def get_tts(language: str = None, voice: str = None):
    language = language or "hi-IN"
    voice = voice or "anushka"
    return sarvam.TTS(
        api_key=settings.SARVAM_API_KEY,
        model="bulbul:v2",
        speaker=voice,
        target_language_code=language,
        enable_preprocessing=False,  # reduce TTS overhead
        # LiveKit Agents will stream generated audio when configured in AgentSession
    )

2) AgentSession: enable preemptive generation, VAD, and endpoint tuning

from livekit.agents import AgentSession, inference
from livekit.plugins import silero

vad = silero.VAD.load()

session = AgentSession(
    stt=get_stt(),
    llm=get_llm(),
    tts=get_tts(),
    vad=vad,
    preemptive_generation=True,    # start LLM/TTS early from partial transcripts
    min_endpointing_delay=0.3,     # respond faster on clear silence
    max_endpointing_delay=1.5,     # avoid long wait times
    turn_detection="vad",
)

3) Quick checklist to verify after deploying • Confirm Sarvam STT/TTS endpoints are using streaming mode (client config or WebSocket). • Confirm Gemini is reachable via the Realtime/flash endpoint and streams tokens. • Host your agent in the same region as Sarvam/Gemini (reduce RTT). • Avoid burstable CPU instances (use m5/c5) and monitor TTFT/TTFB via metrics. If you want, share: • exact Sarvam endpoint type you call (HTTP vs WebSocket), • the Gemini endpoint you use (Realtime vs REST), and I’ll give the exact config snippet or WebSocket client code to enable streaming. Sources: How can I reduce latency in voice agents using STT, TTS and LLM? | examples/frontdesk | livekit/livekit_composite | DeepWiki | Gemini Live API plugin | LiveKit docs | Simple Call Answering Agent | LiveKit docs

where you used inferense

Good question — in the snippets I gave, LiveKit Inference is used implicitly by passing an inference model string to the AgentSession/LLM instead of constructing a provider-specific client. That tells LiveKit to route LLM calls through the Inference service. What to change in your code • Current approach (provider client): you call

google.LLM(...)

which uses the direct Google client. • Inference approach: pass the model identifier string (e.g.

"google/gemini-2.5-flash"

) to the session or to

inference.LLM(...)

so LiveKit uses its Inference layer. Minimal examples 1) Use a model string (recommended — routes via LiveKit Inference)

# AgentSession will route LLM calls through LiveKit Inference
session = AgentSession(
  stt=get_stt(),
  llm="google/gemini-2.5-flash",
  tts=get_tts(),
  preemptive_generation=True,
  ...
)

2) Use the inference helper explicitly

from livekit.agents import inference

llm = inference.LLM(model="google/gemini-2.5-flash")
session = AgentSession(stt=get_stt(), llm=llm, tts=get_tts(), preemptive_generation=True)

Notes • LiveKit Inference provides co-location, provisioned capacity, and optimized routing; using the model string or

inference.LLM

enables those benefits. • You can still keep Sarvam for STT/TTS via the plugin; passing a Sarvam model string for `tts`/`stt` may also route through Inference if supported, otherwise continue using the sarvam plugin. • Make sure your deployment and LiveKit config have credentials/API keys available so Inference can call Gemini. If you want, I can convert your current

get_llm()

to return the

inference.LLM(...)

object or show the exact AgentSession file diff. Which do you prefer?

wich is better

Good question — here's a concise comparison and my recommendation. Quick answer • Use LiveKit Inference (pass the model string or use

inference.LLM

) for lower latency and reliability in production. • Use direct provider clients (

google.LLM(...)

) for local testing, debugging, or if you need provider-specific features not exposed by Inference. Why LiveKit Inference is better (recommended) • Lower latency: co-location and optimized routing reduce network hops and queuing. • Provisioned capacity: avoids public endpoint queuing during peak times. • Simplicity at the session level: just pass

"google/gemini-2.5-flash"

or

inference.LLM(...)

and LiveKit handles routing. • Observability: integrated metrics for TTFT/TTFB and request routing. When to use direct provider clients • You need a provider-specific feature or experimental flag not yet supported by Inference. • You’re debugging provider-specific behavior locally and want direct control. • Short-term prototype where you control auth and endpoint directly. How to switch (two minimal options) • Model string (recommended):

session = AgentSession(
  stt=get_stt(),
  llm="google/gemini-2.5-flash",
  tts=get_tts(),
  preemptive_generation=True,
)

• Explicit inference helper:

from livekit.agents import inference
llm = inference.LLM(model="google/gemini-2.5-flash")
session = AgentSession(stt=get_stt(), llm=llm, tts=get_tts(), preemptive_generation=True)

If you want, I can: • Convert your

get_llm()

to return

inference.LLM(...)

(code diff), or • Show the exact AgentSession file changes to switch to Inference. Which would you prefer?

123357.798 ERROR livekit.agents unhandled exception while running the job task Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\opentelemetry\uti l\_decorator.py", line 71, in async_wrapper return await func(*args, **kwargs) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ip c\job_proc_lazy_main.py", line 286, in _traceable_entrypoint await self._job_entrypoint_fnc(job_ctx) File "C:\Codeshastra_Projects\hamariai-health\api\services\agent-runner\main.py", line 20, in entrypoint agent = MyAgent(metadata=metadata,job_context=ctx) File "C:\Codeshastra_Projects\hamariai-health\api\services\agent-runner\my_agent\myage nt.py", line 153, in init super().__init__( ~~~~~~~~~~~~~~~~^ instructions=self.agent_prompt, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ...<8 lines>... turn_detection="vad", ^^^^^^^^^^^^^^^^^^^^^ ) ^ TypeError: Agent.__init__() got an unexpected keyword argument 'preemptive_generation'

You're seeing that error because

preemptive_generation

is not an argument of

Agent.__init__()

— it belongs on

AgentSession

. Move any

preemptive_generation

(and other session-level options like

min_endpointing_delay

,

max_endpointing_delay

,

vad

,

turn_detection

) into the AgentSession creation instead of the Agent constructor. Example fix (minimal): 1) Agent init — do NOT pass preemptive_generation here:

class MyAgent(Agent):
    def __init__(self, metadata, job_context):
        super().__init__(
            instructions=self.agent_prompt,
            # other Agent args (role, memory, etc.)
        )

2) Create AgentSession with preemptive_generation and start the session:

from livekit.agents import AgentSession
from livekit.plugins import silero

vad = silero.VAD.load()

session = AgentSession(
    stt=get_stt(),
    llm="google/gemini-2.5-flash",   # or inference.LLM(...)
    tts=get_tts(),
    vad=vad,
    preemptive_generation=True,      # ← correct place
    min_endpointing_delay=0.3,
    max_endpointing_delay=1.5,
    turn_detection="vad",
)

agent = MyAgent(metadata=metadata, job_context=ctx)
await session.start(agent=agent, room=ctx.room)

If you paste the current Agent init and the code where you build/start the session, I’ll point exactly where to move the options and give the exact diff.

instructions=self.agent_prompt, stt=get_stt(), llm=get_llm(), # llm=google.realtime.RealtimeModel(model="gemini-2.0-flash-exp",api_key=settings.GOOGLE_API_KEY,modalities=[Modality.TEXT]), tts=get_tts(), vad=silero.VAD.load(), preemptive_generation=True, # start LLM/TTS early from partial transcripts min_endpointing_delay=0.3, # respond faster on clear silence max_endpointing_delay=1.5, # avoid long wait times turn_detection="vad", )

Thanks — those session options need to be passed to AgentSession, not the Agent constructor. Move the lines you posted into the AgentSession creation. Example minimal fix: 1) Agent (no session args)

class MyAgent(Agent):
    def __init__(self, metadata, job_context):
        super().__init__(
            instructions=self.agent_prompt,
            # do NOT include stt/llm/tts/vad/preemptive_generation/etc. here
        )

2) AgentSession (correct place for your options)

from livekit.agents import AgentSession
from livekit.plugins import silero

session = AgentSession(
    stt=get_stt(),
    llm=get_llm(),                 # or "google/gemini-2.5-flash" / inference.LLM(...)
    tts=get_tts(),
    vad=silero.VAD.load(),
    preemptive_generation=True,    # ← correct spot
    min_endpointing_delay=0.3,
    max_endpointing_delay=1.5,
    turn_detection="vad",
)
agent = MyAgent(metadata=metadata, job_context=ctx)
await session.start(agent=agent, room=ctx.room)

If you paste your current Agent class and the code where you create/start the session, I’ll show the exact lines to remove and the precise diff.

123921.199 DEBUG livekit.…echStream Sent 200 audio chunks {"session_id": 1752987922656} 123922.480 WARNI… livekit.agents failed to generate LLM completion, retrying in 0.1s {"llm": "livekit.agents.inference.llm.LLM", "attempt": 1} Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 196, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\in ference\llm.py", line 341, in _run raise APIStatusError( ...<5 lines>... ) from None livekit.agents._exceptions.APIStatusError: object cannot be found (status_code=401, request_id=None, body=object cannot be found, retryable=True) 123922.895 WARNI… livekit.agents failed to generate LLM completion, retrying in 2.0s {"llm": "livekit.agents.inference.llm.LLM", "attempt": 2} Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 196, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\in ference\llm.py", line 341, in _run raise APIStatusError( ...<5 lines>... ) from None livekit.agents._exceptions.APIStatusError: object cannot be found (status_code=401, request_id=None, body=object cannot be found, retryable=True) 123925.065 WARNI… livekit.agents failed to generate LLM completion, retrying in 2.0s {"llm": "livekit.agents.inference.llm.LLM", "attempt": 3} Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 196, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\in ference\llm.py", line 341, in _run raise APIStatusError( ...<5 lines>... ) from None livekit.agents._exceptions.APIStatusError: object cannot be found (status_code=401, request_id=None, body=object cannot be found, retryable=True) 123925.922 DEBUG livekit.…echStream Sent 300 audio chunks {"session_id": 1752987922656} 123927.232 ERROR livekit.agents Error in _llm_inference_task Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 196, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\in ference\llm.py", line 341, in _run raise APIStatusError( ...<5 lines>... ) from None livekit.agents._exceptions.APIStatusError: object cannot be found (status_code=401, request_id=None, body=object cannot be found, retryable=True) The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ut ils\log.py", line 16, in async_fn_logs return await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\opentelemetry\uti l\_decorator.py", line 71, in async_wrapper return await func(*args, **kwargs) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\vo ice\generation.py", line 130, in _llm_inference_task async for chunk in llm_node: ...<35 lines>... ) File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\vo ice\agent.py", line 417, in llm_node async for chunk in stream: yield chunk File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 350, in anext raise exc # noqa: B904 ^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 177, in _traceable_main_task await self._main_task() File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\ll m\llm.py", line 208, in _main_task raise APIConnectionError( f"failed to generate LLM completion after {self._conn_options.max_retry + 1} attempts", # noqa: E501 ) from e livekit.agents._exceptions.APIConnectionError: failed to generate LLM completion after 4 attempts (body=None, retryable=True)

That 401 "object cannot be found" error means the Inference LLM calls are failing authentication. Here’s exactly what to check and how to fix it so your session can stream Gemini via LiveKit Inference. 1) Quick diagnosis • Error: APIStatusError / 401 with body "object cannot be found" from LiveKit Inference LLM. • Cause: Invalid/missing credentials for Gemini when using LiveKit Inference (model string or inference.LLM). Not an LLM model bug. 2) Immediate fixes (pick one) A — Use LiveKit Inference (recommended) • Ensure your environment has a valid Google API key accessible to the process: ◦ Set env: GOOGLE_API_KEY=your_gemini_api_key ◦ Or pass when constructing LLM via inference helper:

from livekit.agents import inference
    llm = inference.LLM(model="google/gemini-2.5-flash", api_key=settings.GOOGLE_API_KEY)
    session = AgentSession(llm=llm, ...)

• If you pass the model string to AgentSession, make sure the runtime that runs AgentSession has GOOGLE_API_KEY available in its environment. B — Use direct provider client (temporary test) • Test direct authentication to confirm the key is valid:

from livekit.plugins import google
  llm = google.LLM(model="gemini-2.5-flash", api_key=settings.GOOGLE_API_KEY)
  # run a quick chat/test call to confirm success

• If this works but Inference fails, the problem is Inference credentials or IAM configuration. 3) Other checks • No leading/trailing spaces/quotes in the key. • Key has necessary permissions for the Gemini/Vertex AI endpoint you target. • If using Vertex-specific flags, ensure they match (do not set vertexai=True unless using Vertex). • No proxy/firewall blocking outbound calls from your host to Google/Inference service. • If running in LiveKit Cloud / container, confirm the env var was injected into the container. 4) If you want me to patch your code • Tell me whether you prefer: ◦ A: session.llm = "google/gemini-2.5-flash" (Inference via model string), or ◦ B: session.llm = inference.LLM(..., api_key=...) and paste how you set env vars (local .env, Docker, or cloud). I’ll give the exact one-line change. Would you like me to show the minimal test script to verify the Google key locally?

llm = google.LLM(model="gemini-2.5-flash", api_key=settings.GOOGLE_API_KEY) # run a quick chat/test call to confirm success this is working but inferense is getting fail

Thanks — since

google.LLM(..., api_key=...)

works but LiveKit Inference fails with 401, the issue is Inference not receiving valid Google credentials. Quick checklist and exact fixes: 1) Ensure env var is available to the process that runs Inference • Export the key where your agent process runs: GOOGLE_API_KEY=your_gemini_api_key • If using a .env, Docker, or cloud service, confirm the env var is injected into the running container/process. 2) Use inference.LLM or model string and pass api_key explicitly • Option A — explicit inference object (safe for debugging):

from livekit.agents import inference
llm = inference.LLM(model="google/gemini-2.5-flash", api_key=settings.GOOGLE_API_KEY)
session = AgentSession(llm=llm, stt=get_stt(), tts=get_tts(), vad=vad, preemptive_generation=True)

• Option B — model string (requires env var visible to runtime):

session = AgentSession(llm="google/gemini-2.5-flash", ...)

3) Install & enable Inference extras and feature access • Ensure your runtime has the inference extras installed (if required): livekit-agents[inference] or similar. • Confirm Inference is enabled for your LiveKit deployment (console/config) so model routing is permitted. 4) Check permissions & key validity • No extra spaces/quotes in the key. • Key has permission to call Gemini/Vertex endpoints. • If using project-scoped credentials, ensure the model name is accessible in that project/region. 5) Test minimal Inference call • Use the explicit inference.LLM snippet above and run a simple chat/test to confirm. If explicit call fails but google.LLM works, capture the exact error and paste it. If you want, tell me: • how you inject env vars (local .env, Docker, or cloud), and • whether you prefer I provide the minimal test script using inference.LLM I’ll give the exact change/snippet for your deployment.