agreeable-student-14743
09/12/2025, 10:03 PMtall-airline-16756
09/12/2025, 10:03 PMtall-airline-16756
09/12/2025, 10:03 PMAgentSession
, which keeps all the user-specific data and context nicely separated. So, you can confidently manage all 1000 customers with shared agents, as each session is created dynamically and tailored to the individual user.
If you'd like, I’m here to help with any more details on how to set this up—just let me know!
Sources: complex-agents/personal_shopper/README.md | README.md | Agents Overview | LiveKit Docs | Workflows | LiveKit Docs | LiveKitagreeable-student-14743
09/12/2025, 10:05 PMtall-airline-16756
09/12/2025, 10:05 PMUserData
object: Think of this as a handy place to store all the customer-specific details like agent preferences, tools, and configurations. This object travels with the AgentSession
and stays accessible to all agents during the session.
2. Create specialized agents: You can design agents with unique setups—like particular instructions, tools, voices (TTS), or models (STT, LLM)—tailored just for what each customer needs.
3. Transfer control between agents: By using tool functions, you can smoothly switch between agents depending on what the customer requires. This way, the right agent is always handling the task, and the session flows without a hitch.
4. Preserve context during transfers: The on_enter()
method is your friend here! It helps copy the conversation history and update the chat context so customers experience a seamless transition between agents.
5. Optional - Use participant metadata: If you’re working with web or mobile frontends, you can pass customer-specific configurations through participant metadata. Agents can then read and apply these settings dynamically during the session.
This method helps you create personalized and smooth interactions for each customer, all while keeping things scalable. If you'd like, I’m here to provide more details or examples—just let me know!
Sources: Accepting incoming calls | LiveKit Docs | examples/voice_agents/multi_agent.py | complex-agents/personal_shopper/README.md | examples/src/restaurant_agent.ts | agent/main.pyagreeable-student-14743
09/12/2025, 10:08 PMtall-airline-16756
09/12/2025, 10:09 PM