Leveraging LiveKit for Real-Time AI-Driven Conversational Agents
TL;DR
A LiveKit-based AI Assistant designed to enable seamless, low-latency human-computer interaction by integrating real-time audio streaming, large language model (LLM) reasoning, and automated system control within a unified architecture is introduced.
Abstract
: Recent progress in real-time communication frameworks has significantly advanced the development of multimodal intelligent assistants that can interact with users in a natural, human-like manner. This paper introduces a LiveKit-based AI Assistant designed to enable seamless, low-latency human-computer interaction by integrating real-time audio streaming, large language model (LLM) reasoning, and automated system control within a unified architecture. The proposed framework utilizes LiveKit Agents for real-time connectivity, employs noise suppression plugins for robust speech processing, and leverages LangChain-based orchestration to ensure contextual understanding and reasoning [1], [2]. In addition, the assistant incorporates a versatile toolkit for web-based search, voice-command interpretation, and operating system-level automation, enabling it to function effectively across diverse platforms and environments. Experimental evaluations indicate that the system achieves high responsiveness and scalability while maintaining lightweight deployment characteristics. Compared to existing conversational agents such as traditional virtual assistants, the proposed model offers superior customizability, cross-platform adaptability, and real-time feedback performance. This research underscores the potential of LiveKit-powered multimodal agents in shaping the future of real-time interactive AI systems suitable for both academic exploration and real-world deployment [3], [4].