To create a modular design for setting up AI agents in LiveKit, follow these strategies: • Separate Concerns: Organize your project into distinct directories for agents, core logic, generators, and systems. For example:`role-playing/ ├── agent.py # Main entry point ├── agents/ # Agent implementations │ ├── base_agent.py # Base class for all agents │ ├── narrator_agent.py # Handles exploration & dialogue │ └── combat_agent.py # Handles combat encounters ├── core/ # Core game state management │ ├── game_state.py # Game state management │ └── game_mechanics.py # Core mechanics (dice, combat, skill checks) ├── generators/ # Dynamic content generation │ ├── npc_generator.py # NPC creation with LLM │ └── item_generator.py # Item generation with LLM └── systems/ # Game systems (to be implemented)` • Specialized Agents: Create separate agent classes for specific tasks, such as a narrator for dialogue and a combat agent for encounters. • Dynamic Content Generation: Use LLMs in generator modules to create NPCs, items, and other content dynamically based on the game state. • Plugin Architecture: Integrate plugins for LLM, STT, and TTS providers to allow flexible service swapping. Example:`session = AgentSession( vad=silero.VAD.load(), stt=deepgram.STT(model="nova-3"), llm=openai.LLM(model="gpt-4o-mini"), tts=elevenlabs.TTS(), )` • Event System: Implement an event system to monitor and manage agent states and interactions for better coordination. • Multi-Agent Handoff: Use multiple agents to handle different stages of interaction, passing user data between them as needed. These strategies enhance maintainability, scalability, and flexibility. Let me know if you need further details or examples! Sources: complex-agents/drive-thru/drivethru_agent.py | LiveKit Agents | Agents Core Architecture | livekit/livekit_composite | DeepWiki | README.md