Vision Agents is an open-source Python framework designed to simplify the creation of real-time voice and video AI agents. By utilizing Stream’s global edge network, the framework achieves sub-500ms latency, making it suitable for high-performance applications like telehealth, live coaching, and automated support. The system is built to be provider-agnostic, allowing developers to plug in any LLM, speech, or vision model from over 25 supported providers without needing to rewrite agent logic. Whether building a simple voice assistant or a complex video analysis system, Vision Agents provides the core architecture to handle real-time streaming, session management, and integration.

The framework provides two primary operational modes: Realtime APIs (WebRTC/WebSocket) for the fastest deployment, and custom STT-LLM-TTS pipelines for granular control. It includes native support for video processing, allowing developers to integrate computer vision models like YOLO or Roboflow to analyze frames in real-time. The framework is production-ready, featuring a built-in HTTP server, Prometheus metrics, and support for Docker and Kubernetes deployment, ensuring that agents can scale from local development to a fully managed production environment.

Some of the key features are:

• Flexible Integrations: Support for over 25 AI providers including OpenAI, Gemini, Anthropic, Deepgram, and ElevenLabs.
• Real-time Performance: Built on Stream’s global edge network for sub-500ms latency in voice and video interactions.
• Modular Architecture: Swappable components for STT, TTS, and LLMs allow for easy switching between providers.
• Computer Vision Support: Ability to process video frames using YOLO, Roboflow, or custom ML models.
• Production Capabilities: Includes built-in telemetry, HTTP server for session management, and Kubernetes deployment guides.
• Multi-Modal Functionality: Supports function calling and Model Context Protocol (MCP) for connecting to external tools and knowledge bases.
• Phone Support: Direct integration with services like Twilio for bidirectional audio calls.
• Advanced Turn Detection: Built-in and pluggable turn detection to manage conversation flow and interruptions.

Developers can get started in minutes using a simple Python-based workflow. The framework uses a clean interface where developers define their Agent using specific plugins for their desired LLM, STT, and TTS engines. For video-based agents, developers can inject processors that intercept frames and run inference before forwarding information to the LLM. Once developed locally, the agent can be containerized using Docker and deployed to a Kubernetes cluster, where it can be monitored using standard tools like Prometheus and Grafana, providing a clear path from a local prototype to a scalable enterprise service.

Some common use cases include:

• AI Golf Coach: Using YOLO pose detection to monitor a user's swing while a model provides real-time feedback.
• Phone Support Agent: Deploying Twilio-powered agents that utilize RAG for knowledge retrieval to answer customer inquiries.
• Smart Security Camera: Running real-time face and object recognition to send alerts based on visual activity.
• Live Sports Commentator: Tracking player and ball movement with computer vision while an LLM generates live commentary.
• Interactive Avatars: Creating virtual characters that see, hear, and respond in real-time via synchronized lip-syncing.