In the rapidly evolving landscape of AI applications, a new frontier is emerging: real-time vision AI agents that can see, understand, and respond to video streams instantly. Stream has just released Vision Agents, an open-source framework that makes building these sophisticated applications surprisingly accessible.
What Are Vision Agents?
Vision Agents combine the power of large language models with real-time video processing capabilities. Think of them as AI assistants that can watch video feeds, understand what’s happening, and provide intelligent feedback or coaching in real-time. The possibilities are vast: sports coaching, physical therapy guidance, interview assistance, drone surveillance, and even augmented reality overlays for everyday tasks.
Why Vision Agents Matter
Traditional AI applications work with text or static images. Vision Agents operate in real-time on live video streams, opening up entirely new categories of applications. Whether you’re building a golf coach that analyzes your swing, a fitness trainer that corrects your form, or a sales coach that listens to your calls and provides silent guidance, Vision Agents make these experiences possible with minimal code.
Key Features That Set Vision Agents Apart
Ultra-Low Latency
Stream’s implementation leverages their edge network to achieve remarkably fast performance: 500ms to join a session and just 30ms for audio/video latency. This speed is crucial for real-time coaching applications where delayed feedback breaks the user experience.
Model Agnostic Architecture
Unlike frameworks locked into a single provider, Vision Agents supports OpenAI’s Realtime API, Google’s Gemini, and Claude. The framework provides native SDK methods for each platform, ensuring you can always leverage the latest capabilities from any LLM provider.
Built for Extensibility
The framework is truly open-source and flexible. While it’s built by Stream and optimized for their edge network, you can integrate any video provider you prefer. This prevents vendor lock-in and ensures the framework adapts to your infrastructure needs.
Cross-Platform SDKs
With native SDKs for React, Android, iOS, Flutter, React Native, and Unity, you can build vision AI experiences across every major platform from a single codebase.
Real-World Use Cases
Golf Coaching with YOLO + OpenAI Realtime
One of the showcase examples demonstrates a golf coach that combines YOLO pose detection with OpenAI’s Realtime API. Here’s how elegant the code is:
python
agent = Agent(
edge=getstream.Edge(),
agent_user=agent_user,
instructions="Read @golf_coach.md",
llm=openai.Realtime(fps=10),
processors=[ultralytics.YOLOPoseProcessor(model_path="yolo11n-pose.pt")],
)This simple configuration creates an AI that can watch your golf swing, detect your body position using YOLO’s computer vision model, and provide real-time coaching through OpenAI’s conversational AI. The same pattern works for countless applications: workout coaching, dance instruction, physical therapy, and more.
The Invisible Assistant
Another fascinating example is the “invisible assistant” inspired by apps like Cluely. This agent watches your screen and listens to your audio using Gemini’s Realtime API but only provides text-based guidance without broadcasting audio. Perfect for:
- Sales coaching during live calls
- Interview preparation and assistance
- On-the-job training with AR glasses
- Real-world task guidance
python
agent = Agent(
edge=StreamEdge(),
agent_user=agent_user,
instructions="You are silently helping the user pass this interview.",
llm=gemini.Realtime()
)The Power of Processors
What makes Vision Agents truly flexible is the processor system. Processors let you:
- Run smaller, faster AI models alongside LLMs (like YOLO or Roboflow)
- Make API calls to maintain game state or relevant information
- Modify audio and video streams for avatars or effects
- Capture video at specific moments
This architecture means you’re not limited to what the LLM can see. You can combine specialized computer vision models for tasks like object detection or pose estimation with the conversational intelligence of GPT-4 or Gemini.
Getting Started
The Vision Agents team has made onboarding straightforward with comprehensive documentation at VisionAgents.ai. The site includes:
- Quickstart guides for voice AI and video AI apps
- Detailed tutorials for sports coaching and meeting assistants
- Integration guides for over 10 different services
The Ecosystem
Vision Agents doesn’t exist in isolation. Stream has curated a list of key people and projects pushing vision AI forward, from Demis Hassabis at Google DeepMind to the teams behind Ultralytics YOLO, Roboflow, and Moondream. The framework integrates with this broader ecosystem while providing a simpler, more unified developer experience.
How It Compares
Stream positions Vision Agents against other frameworks like Livekit Agents, Pipecat, and OpenAI Agents. The key differentiators are flexibility (model and provider agnostic), performance (ultra-low latency), and simplicity (cleaner syntax than alternatives).
What’s Coming Next
The roadmap is ambitious and addresses real production needs:
- Enhanced WebRTC capabilities
- Hosting and production deployment examples
- More built-in YOLO processors for common tasks
- Roboflow integration for custom models
- AI avatar support with services like Tavus
- Computer use capabilities
- Buffered video capture for event detection
The Bottom Line
Vision Agents represents a significant step forward in making real-time vision AI accessible to developers. By combining low latency infrastructure, model flexibility, and elegant APIs, Stream has created a framework that lowers the barrier to building the next generation of AI applications.
Whether you’re a startup building a new coaching app or an enterprise exploring vision AI use cases, Vision Agents provides the foundation to move quickly from concept to production. The framework is open-source, well-documented, and backed by Stream’s proven infrastructure.
The future of AI isn’t just about chatbots that respond to text. It’s about intelligent agents that can see our world and help us navigate it in real-time. Vision Agents is your toolkit for building that future.
Ready to build your own vision AI agent? Check out the framework at github.com/GetStream/Vision-Agents and start with the quickstart guide at VisionAgents.ai.
