Reorganize community environments - Move lean_proof_env, router_env, and philosophical_rlaif_env.py to environments/community/ - Add comprehensive README for community environments - This organizes community-contributed environments into a dedicated community folder for better maintainability and discoverability

environments/community/router_env/engine/agents/agent_mcp_mvp.py

@@ -0,0 +1,58 @@
+import logging
+import os
+from dotenv import load_dotenv
+from pathlib import Path
+from livekit.agents import Agent, AgentSession, JobContext, WorkerOptions, cli, mcp
+from livekit.plugins import deepgram, openai, silero
+from livekit.plugins.turn_detector.multilingual import MultilingualModel
+from dotenv import load_dotenv
+
+
+load_dotenv(os.path.join(os.path.dirname(__file__), '..', '..', '.env'))
+
+
+logger = logging.getLogger("mcp-agent")
+
+load_dotenv(dotenv_path=Path(__file__).parent.parent / '.env')
+
+class MyAgent(Agent):
+    def __init__(self) -> None:
+        super().__init__(
+            instructions=(
+                "You can retrieve data via the MCP server. The interface is voice-based: "
+                "accept spoken user queries and respond with synthesized speech."
+            ),
+            vad=silero.VAD.load(),
+            stt=deepgram.STT(model="nova-3", language="multi"),
+            llm=openai.LLM(model="gpt-4o-mini"),
+            tts=openai.TTS(voice="ash"),
+            turn_detection=MultilingualModel(),
+            mcp_servers=[
+            mcp.MCPServerHTTP(
+                url="https://mcp.gumloop.com/gcalendar/cY3bcaFS1qNdeVBnj0XIhnP4FEp2%3Aae99858e75594251bea9e05f32bb99b3",
+                timeout=5,
+                client_session_timeout_seconds=5,
+            ),
+        ]
+        )
+
+    async def on_enter(self):
+        self.session.generate_reply()
+
+async def entrypoint(ctx: JobContext):
+    await ctx.connect()
+
+    session = AgentSession(
+        vad=silero.VAD.load(),
+        stt=deepgram.STT(model="nova-3", language="multi"),
+        llm=openai.LLM(model="gpt-4o-mini"),
+        tts=openai.TTS(voice="ash"),
+        turn_detection=MultilingualModel(),
+    )
+
+    await session.start(agent=MyAgent(), room=ctx.room)
+
+
+if __name__ == "__main__":
+    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint,
+                              agent_name="mcp-agent"))