@millions-hair-24956 So, for agents we have only python and js libraries, correct? From what I understand the rust sdk doesn't yet support this? Asking because we have a rust project that we'd like to integrate livekit agents to, and having a rust library would make things a lot easier for us. Thanks in advance!

Yes, currently LiveKit Agents are primarily supported in Python and Node.js, as per the LiveKit Agents documentation. The Rust SDK does not yet support LiveKit Agents directly. However, the Rust SDK is used as a foundation for the Node.js and Python implementations, which means you might be able to build custom integrations using Rust, but it would require additional development effort. Would you like more details on how you might approach integrating LiveKit Agents with a Rust project? Sources: - LiveKit Agents Overview 👍 1 button 👎 0 button 💡 Hint: Mention @millions-hair-24956 in the thread for followups.

@millions-hair-24956 tell me more.

It seems likely that integrating LiveKit Agents into a Rust project would involve using the existing Rust SDK to handle lower-level WebRTC operations, while leveraging the Python or Node.js Agents framework for higher-level agent functionalities. Here's a potential approach: 1. Use Rust for WebRTC Operations: The Rust SDK can manage WebRTC connections, media handling, and other real-time communication tasks. This would involve setting up rooms, managing participants, and handling media tracks. 2. Bridge to Python/Node.js for Agent Logic: Since the agent logic, including AI interactions and task orchestration, is more mature in Python and Node.js, you could create a bridge between Rust and one of these languages. This could be done using inter-process communication (IPC) or a network-based approach where the Rust application communicates with a Python/Node.js service running the agent logic. 3. Custom Integration: Develop custom plugins or extensions in Rust that can interface with the Python/Node.js agent framework. This might involve creating bindings or using foreign function interfaces (FFI) to call Rust code from Python/Node.js. 4. Monitor and Optimize: Ensure that the integration is optimized for performance, especially if handling high volumes of real-time data. This might involve profiling both the Rust and Python/Node.js components to identify bottlenecks. This approach allows you to leverage the strengths of both Rust for performance-critical tasks and Python/Node.js for advanced agent functionalities. Would you like more detailed guidance on any specific part of this integration process? Sources: - https://docs.livekit.io/home/client/connect/ - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729735993.197419 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.