It is a framework for building programmable, multimodal AI agents that orchestrate large language models (LLMs) and other AI models to accomplish tasks.
It is a framework for building programmable, multimodal AI agents that orchestrate large language models (LLMs) and other AI models to accomplish tasks. The LiveKit Agents framework enables developers to create AI agents using Python or Node.js, which operate as stateful, long-running processes. These agents connect to the LiveKit network via WebRTC, facilitating low-latency, real-time media and data exchange with frontend applications.
Unlike traditional HTTP servers, LiveKit Agents are designed to handle multimodal interactions, allowing agents to exchange voice, video, and text with users. This simplifies frontend development by leveraging LiveKit’s SDKs to manage WebRTC transport, media device handling, and audio/video encoding and decoding. The framework also benefits from LiveKit Cloud’s global mesh network, which minimizes transport latency by connecting users to the nearest edge server.
The framework centralizes business logic within the agent process, enabling support for clients across platforms, including telephony integrations. It also provides a stateful approach to managing end-user interactions, eliminating the need for synchronizing client-side state through traditional request/response cycles.
To use the framework, developers write a Python or Node.js application (the agent) and a frontend for users. The agent code includes configuration, functions, and plugins for tasks like LLM integration, speech-to-text (STT), text-to-speech (TTS), voice activity detection (VAD), and text processing. Developers can also define entrypoint functions and optional preprocessing logic for connections.
When deployed, the agent registers with a LiveKit server (self-hosted or LiveKit Cloud) and runs as a background worker process. It waits for users to connect, and upon session initiation, dispatches an agent to the user’s LiveKit room. Users connect via a frontend application, where the agent interacts with them based on the custom logic defined in the agent code.
The framework is ideal for building AI voice agents, real-time APIs, and other programmable participants. Developers can test and develop agents using the Agents Playground. For more details, refer to the LiveKit documentation on integrations, worker options, and quickstart guides.
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