Today I learned about Vision Agents — an open-source framework by Stream for building real-time AI agents that process video and audio. It’s particularly useful for mobile app development since it ships with SDKs for iOS, Android, React Native, and Flutter.
What It Does
Vision Agents lets you build AI agents that can watch a video stream, listen to audio, and respond in real-time — all with ultra-low latency (~500ms join, <30ms audio/video latency) via Stream’s edge network.
Think: a golf coach that watches your swing via phone camera and gives live feedback, or a security camera that detects packages and recognizes faces autonomously.
Architecture
An agent combines four layers:
Edge Network (Stream) → ultra-low latency video/audio transport
↓
LLM (OpenAI / Gemini / Claude) → reasoning and conversation
↓
Processors (YOLO / Roboflow / custom) → frame analysis, object detection
↓
Speech (Deepgram STT / ElevenLabs TTS) → listen and speak
Processors are the key concept — they run between frames, handling real-time analysis (pose detection, object recognition, etc.) and managing agent state between interactions.
Why It Matters for Mobile Development
Vision Agents provides native SDKs for all major mobile platforms:
- iOS (native Swift)
- Android (native Kotlin)
- React Native
- Flutter
- Unity (for AR/VR)
This means you can build mobile apps where the camera feed goes to an AI agent that sees, understands, and responds in real-time — without building the video infrastructure yourself.
Quick Start
# Install
uv add vision-agents
# With common integrations
uv add "vision-agents[getstream, openai, elevenlabs, deepgram]"
Requires Stream API credentials (free tier: 333,000 participant minutes/month).
Supported Models and Services
LLMs: OpenAI (Realtime API with WebRTC video), Google Gemini (including Gemini Live), Anthropic Claude, AWS Bedrock, xAI Grok, Mistral, Qwen, Hugging Face
Vision/Detection: Ultralytics YOLO (pose + object detection), Roboflow (hosted/local), Moondream (VLM), NVIDIA Cosmos 2
Speech: Deepgram (STT), ElevenLabs (TTS), AWS Polly, Cartesia, Fast-Whisper, Fish Audio
Other: Twilio (phone calls), TurboPuffer (RAG/vector search), HeyGen (avatars), Decart (video styling)
Real-World Use Cases
Golf Coach
Combines YOLO pose detection with Gemini Live — the agent watches your swing through the phone camera, analyzes body position frame-by-frame, and gives real-time coaching feedback through voice.
Security Camera
Integrates face recognition + YOLOv11 package detection — detects unknown visitors, identifies packages left at the door, sends alerts, and can even generate “WANTED” posters automatically.
Phone + RAG
Enables inbound/outbound phone calls via Twilio with vector search retrieval — build a customer support agent that can see screen shares and search your knowledge base simultaneously.
Real-Time Video Styling
Uses Decart’s Mirage model — transforms video frames in real-time with artistic styles, frame by frame.
Key Features
- Ultra-low latency — join in ~500ms, audio/video latency under 30ms
- Multi-model — swap LLMs, vision models, and speech services freely
- Turn detection — natural conversation flow with speaker identification
- Tool/function calling — agents can execute code and APIs mid-conversation
- Memory via Stream Chat — context retention across turns and sessions
- Text back-channel — silent messaging during video calls (think: captions, metadata)
When to Use Vision Agents
Good fit:
- Mobile apps that need real-time camera + AI (coaching, accessibility, AR)
- Video call apps with AI assistants
- Security/monitoring with intelligent detection
- Customer support with screen sharing + AI
Not the right tool for:
- Static image analysis (just use an LLM vision API directly)
- Offline processing (this is built for real-time streaming)
- Text-only agents (overkill if you don’t need video/audio)
How LearnAI Team Could Use This
- Build demos that show students how multimodal agents combine video, audio, tools, and reasoning.
- Use the golf-coach or accessibility patterns as project prompts for real-time AI app lessons.
- Compare when to use real-time video agents versus simpler static image or text-only AI workflows.