diff --git a/README.md b/README.md
index 180b332..01e9562 100644
--- a/README.md
+++ b/README.md
@@ -139,4 +139,4 @@ You can also self-host LiveKit instead of using LiveKit Cloud. See the [self-hos
 
 ## License
 
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
\ No newline at end of file
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
diff --git a/pyproject.toml b/pyproject.toml
index 621bc8a..3f6a5ab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ description = "Simple voice AI assistant built with LiveKit Agents for Python"
 requires-python = ">=3.9"
 
 dependencies = [
-    "livekit-agents[silero,turn-detector]~=1.2",
+    "livekit-agents[silero,turn-detector]~=1.3",
     "livekit-plugins-noise-cancellation~=0.2",
     "python-dotenv",
 ]
diff --git a/src/agent.py b/src/agent.py
index d85c794..aab4ded 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -1,17 +1,16 @@
 import logging
 
 from dotenv import load_dotenv
+from livekit import rtc
 from livekit.agents import (
     Agent,
+    AgentServer,
     AgentSession,
     JobContext,
     JobProcess,
-    MetricsCollectedEvent,
-    RoomInputOptions,
-    WorkerOptions,
     cli,
     inference,
-    metrics,
+    room_io,
 )
 from livekit.plugins import noise_cancellation, silero
 from livekit.plugins.turn_detector.multilingual import MultilingualModel
@@ -48,11 +47,18 @@ def __init__(self) -> None:
     #     return "sunny with a temperature of 70 degrees."
 
 
+server = AgentServer()
+
+
 def prewarm(proc: JobProcess):
     proc.userdata["vad"] = silero.VAD.load()
 
 
-async def entrypoint(ctx: JobContext):
+server.setup_fnc = prewarm
+
+
+@server.rtc_session()
+async def my_agent(ctx: JobContext):
     # Logging setup
     # Add any other context you want in all log entries here
     ctx.log_context_fields = {
@@ -91,21 +97,6 @@ async def entrypoint(ctx: JobContext):
     #     llm=openai.realtime.RealtimeModel(voice="marin")
     # )
 
-    # Metrics collection, to measure pipeline performance
-    # For more information, see https://docs.livekit.io/agents/build/metrics/
-    usage_collector = metrics.UsageCollector()
-
-    @session.on("metrics_collected")
-    def _on_metrics_collected(ev: MetricsCollectedEvent):
-        metrics.log_metrics(ev.metrics)
-        usage_collector.collect(ev.metrics)
-
-    async def log_usage():
-        summary = usage_collector.get_summary()
-        logger.info(f"Usage: {summary}")
-
-    ctx.add_shutdown_callback(log_usage)
-
     # # Add a virtual avatar to the session, if desired
     # # For other providers, see https://docs.livekit.io/agents/models/avatar/
     # avatar = hedra.AvatarSession(
@@ -118,9 +109,12 @@ async def log_usage():
     await session.start(
         agent=Assistant(),
         room=ctx.room,
-        room_input_options=RoomInputOptions(
-            # For telephony applications, use `BVCTelephony` for best results
-            noise_cancellation=noise_cancellation.BVC(),
+        room_options=room_io.RoomOptions(
+            audio_input=room_io.AudioInputOptions(
+                noise_cancellation=lambda params: noise_cancellation.BVCTelephony()
+                if params.participant.kind == rtc.ParticipantKind.PARTICIPANT_KIND_SIP
+                else noise_cancellation.BVC(),
+            ),
         ),
     )
 
@@ -129,4 +123,4 @@ async def log_usage():
 
 
 if __name__ == "__main__":
-    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, prewarm_fnc=prewarm))
+    cli.run_app(server)