This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations.
The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself through the ability to render photorealistic, synchronized digital avatars and integrate with SIP and PSTN networks for AI-driven telephony.
The capability surface covers a broad range of agent logic, from dynamic tool execution and multi-agent session handoffs to structured data extraction and conversational state management. It provides comprehensive infrastructure for agent deployment, including managed hosting, distributed job dispatching, and real-time observability tools for monitoring session health and model performance.
The project includes a Python SDK and command-line utilities for application scaffolding, local agent testing, and deployment management.
