Hi all! Is anyone here using Twilio SIP? I've done...
# ask-ai
d
Hi all! Is anyone here using Twilio SIP? I've done thousands of tests with
AgentSession
, but I still haven't found the secret sauce. When I use the real-time model, it skips the greeting and jumps straight into the conversation. But when I switch to regular TTS/STT, the LLM response latency increases — I get replies with a 3–5 second delay. Does anyone know how to fix or optimize these issues?
t
Thanks for reaching out, Ibragim. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
To address the issues you're experiencing with Twilio SIP and
AgentSession
, here are some optimization strategies: • 1. Reduce Latency: ◦ Ensure your agent is close (in terms of network latency) to your LLM, Speech-to-Text (STT), and Text-to-Speech (TTS) services. ◦ Choose STT, TTS, and LLM providers with low latency characteristics for your use case. • *2. Enable Preemptive Generation:*`session = AgentSession( preemptive_generation=True, # ... STT, LLM, TTS, etc. )`This allows the agent to speculatively begin LLM and TTS requests before detecting an end-of-turn. ◦ Configure your
AgentSession
with preemptive generation to reduce response latency: • *3. Optimize Greeting Delivery:*`async def on_enter(self): self.session.generate_reply(instructions="say hello to the user")`Alternatively, provide pre-synthesized audio for playback to skip the TTS step and reduce response time. ◦ Use
session.say()
to have the agent speak predefined messages: • *4. Configure AgentSession Parameters:*`session = AgentSession( min_interruption_duration=0.5, # Minimum speech length to register interruption min_endpointing_delay=0.4, # Minimum wait after end-of-utterance signal max_endpointing_delay=6.0, # Maximum wait before terminating turn allow_interruptions=True, # Enable user interruptions preemptive_generation=True, # Enable preemptive generation # ... other components )` ◦ Adjust key timing parameters for better real-time performance: • *5. Monitor Performance Metrics:*`from livekit.agents.metrics import UsageCollector usage_collector = UsageCollector() session = AgentSession( # ... other config metrics_handler=usage_collector )` ◦ Use the Agents metrics API to track and optimize latency metrics like Time To First Token (TTFT) and Time To First Byte (TTFB): These steps should help reduce latency and ensure greetings are delivered reliably. Let me know if you need further clarification or assistance! Sources: How can I reduce latency in voice agents using STT, TTS and LLM? | Voice Assistants | livekit/livekit_composite | DeepWiki | Agent speech and audio | LiveKit Docs | examples/voice_agents/preemptive_generation.py | Speech & Audio Processing | livekit/livekit_composite | DeepWiki