hi

Thanks for reaching out, adit-kulkarni. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

can i have a langgraph voice agent which hands off to a openai realtime voice agent? do i need to deploy both agent separately for this or do i get both to join the room?

Thanks — great question. I’m responding to your ask about whether a LangGraph voice agent can hand off to an OpenAI Realtime voice agent, and whether you need to deploy them separately or have them both join the same room. Short answer • Yes — you can have a LangGraph voice agent hand off to an OpenAI Realtime voice agent. • You should deploy each agent as a separate service/worker, and have both agents join the same LiveKit room to interact directly. What you need to do (high level) 1. Deploy both agents separately ◦ Each agent runs as its own service/process so it can accept events, run logic, and produce audio output independently. 1. Have both agents join the same LiveKit room ◦ Both agents connect as participants in the same room so they can hear each other and users in real time. 1. Enable agent-to-agent speech detection ◦ Configure the room options so agents can process audio from other agents. Ensure

participant_kinds

includes

PARTICIPANT_KIND_AGENT

so agents will detect and handle speech from other agents. 1. Implement the handoff in your agent logic ◦ In the LangGraph agent, implement a tool/function that triggers a handoff. When that tool is called, return/activate the OpenAI Realtime agent instance (or signal it to take over). The receiving agent should already be connected or be dispatched to join the room at that time. 1. Configure the OpenAI Realtime agent ◦ Use the Realtime model/session for the OpenAI agent (e.g., a

MultimodalAgent

or

AgentSession

configured with the Realtime model and desired voice). Ensure it can produce/consume audio in the room. Why this approach works • Running agents as separate deployments gives you isolated runtime and easier scaling, while joining the same LiveKit room provides low-latency audio exchange and natural handoffs. • The

PARTICIPANT_KIND_AGENT

setting lets agents detect and process other agents’ audio so the handoff is audible and actionable. If you want, I can help with next steps: • Example code snippets for connecting an agent to a room (LangGraph and OpenAI Realtime). • A sample tool implementation that triggers the handoff from LangGraph to OpenAI Realtime. • Tips for handling audio overlap, detecting end-of-turn, or guaranteeing a clean handoff. Which would you like next? If you want code, tell me which language/environment you’re using (Python, Node, etc.) and whether the OpenAI agent is already set up. Sources: How to Enable Agent-to-Agent Speech Detection in LiveKit | How to Test Your Agent Using Another Agent | LiveKit | Tool definition and use | LiveKit Documentation | OpenAI and LiveKit partner to turn Advanced Voice into an API | OpenAI Realtime API plugin guide | LiveKit Documentation

and how long can i run an openai realtime agent in a room for/

Thanks — I’m responding to your question about how long an OpenAI Realtime agent can run in a LiveKit room. Short answer • There’s no fixed time limit imposed by LiveKit on how long an OpenAI Realtime agent can stay in a room. The agent can run for minutes or hours as long as the session remains active and connected. What controls the session duration • Your application logic: you can explicitly end the agent session by calling

session.shutdown()

(or equivalent) when you want the agent to stop. • Connection stability: network drops or participant disconnects will end or interrupt the session. • Idle/timeout handling you implement: if you want automatic end-of-call behavior, implement idle timeouts (for example, end after a period of silence using a

user_away_timeout

or similar silence-detection logic). Practical tips • If you expect long conversations, ensure your agent code can gracefully handle reconnections and state restoration after transient network issues. • Use silence detection or activity checks to free resources when a session is truly idle. • Make the shutdown path explicit in your agent logic so you can close resources cleanly when the agent is no longer needed. If you want, I can: • Share example code for cleanly shutting down an OpenAI Realtime agent session. • Show a simple silence-detection pattern to auto-end idle sessions. • Discuss reconnection approaches for long-running agents. Which of those would be most helpful? Sources: Azure AI Services and LiveKit | LiveKit Documentation | How to detect and handle silence during calls | LiveKit | Deploy and scale agents on LiveKit Cloud