What is the best way to orchestrate different voice agents in the same conversation, while keeping it transparent to the end user?

To orchestrate different voice agents in the same conversation while keeping it transparent to the end user, you can utilize LiveKit's framework to manage agent state and transitions. According to LiveKit Docs, you can create a single LiveKit server to host multiple agents, each with its specific configuration and domain knowledge. This setup allows you to manage different agents under one project, using separate API keys for each agent to maintain distinct configurations. For seamless orchestration, you can use the VoicePipelineAgent to handle speech-to-speech interactions, which allows for natural conversations with low latency. You can dynamically switch between agents by updating the function context or using a "change state" function call, as suggested in LiveKit Slack. This approach ensures that the transition between agents is smooth and transparent to the user. Would you like more detailed guidance on implementing this setup? Sources: - LiveKit Docs - VoicePipelineAgent - LiveKit Slack 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 yes please provide more details on how to manage agent state and transitions.

To manage agent state and transitions effectively in LiveKit, you can follow these steps: 1. State Management: Use the

VoiceAssistant

class to manage the conversation state. This class provides a way to track the agent's state, such as

listening

,

thinking

, and

speaking

. You can manipulate the chat context using

voice_assistant.chat_ctx

to dynamically modify the conversation flow. This allows you to maintain control over state transitions and objectives, such as user identity verification. 2. Dynamic Tool Availability: You can update the set of tools or functions available to the language model by modifying the

FunctionContext

. This is done by setting

voice_assistant.fnc_ctx = newfuncctx

, allowing you to adapt the agent's capabilities as the conversation progresses through different states. 3. Multi-Agent Scenarios: To implement scenarios where the agent's capabilities or identity might change, you can spawn different agents within the same session. This requires identifying the right participant for the

VoiceAssistant

to interact with, ensuring that the agent communicates with the user rather than another agent. 4. Data Persistence: Ensure that data extracted in earlier steps remains available for use in subsequent steps by injecting it into the

chat_ctx

. This allows the LLM to access the necessary context throughout the conversation. 5. Agent Lifecycle: According to the Anatomy of an Agent, the agent lifecycle involves worker registration, agent dispatch, and room closure. Workers register with the LiveKit server and wait for rooms to be created. When a user connects, the server dispatches an agent to the room. The agent can then manage multiple instances simultaneously, each in its own process. 6. Handling Transitions: Use the

before_llm_cb

hook to gain control before anything is sent to the LLM, allowing you to manage transitions effectively. This hook can be used to adjust the chat context or function context based on the current state of the conversation. By following these steps, you can orchestrate different voice agents in a conversation, ensuring smooth transitions and maintaining transparency for the end user. Sources: - VoicePipelineAgent - LiveKit Slack - Anatomy of an Agent 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

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