witty-kangaroo-6889
09/25/2025, 2:45 AMtall-airline-16756
09/25/2025, 2:45 AMtall-airline-16756
09/25/2025, 2:46 AMwitty-kangaroo-6889
09/25/2025, 2:47 AMtall-airline-16756
09/25/2025, 2:48 AMwitty-kangaroo-6889
09/25/2025, 2:52 AMwitty-kangaroo-6889
09/25/2025, 2:53 AMtall-airline-16756
09/25/2025, 2:53 AMchat_ctx (chat context) from the current agent to the new agent. Here’s how you can implement this:
• Direct Chat Context Handoff: Pass the existing chat_ctx to the new agent during the transfer. For example, in Python: `@function_tool()
async def transfer_to_technical_support(self):
await self.session.generate_reply(instructions="Inform the customer that you're transferring them to the technical support team.")
return TechnicalSupportAgent(chat_ctx=self.session.chat_ctx)`This ensures the new agent inherits the full conversation history, making the transition smooth and seamless.
• Context Summarization: If you'd like, you can also summarize the conversation for the new agent by iterating over the chat_ctx.items to create a helpful summary. This approach works really well for warm transfers to human agents or supervisors, keeping everyone on the same page.
• Custom Session State: For workflows involving multiple agents, you might consider implementing a base class to preserve and truncate context during transfers. This way, you can include just the most recent messages, ensuring the new agent has the most relevant information.
Please feel free to reach out if you'd like more details or need a hand with implementing any of these methods. I'm here to help! Sources: Workflows | LiveKit Docs | Workflows | LiveKit Docs | Agent-assisted warm transfer | LiveKit Docs | examples/warm-transfer/warm_transfer.py | complex-agents/medical_office_triage/triage.py