Skip to main content

View Golf Coach Example on GitHub

Check out the complete AI Golf Coach example in our GitHub repository
Build a real-time golf coach that watches your swing via camera and gives spoken feedback. It combines Ultralytics YOLO pose detection to analyze body position with Gemini Live for real-time coaching — all running on Stream’s low-latency edge network. This pattern applies to any video coaching use case: sports training, physical therapy, workout guidance, or drone monitoring.
Complete the Quickstart first. This example adds a video processor on top of a Realtime LLM.

​
What You Will Build

  • Analyze golf swings in real time using YOLO pose detection
  • Process video at configurable FPS with Gemini Live or OpenAI Realtime
  • Deliver spoken coaching feedback based on body position and movement
  • Hot-swap between AI providers with a one-line config change

​
Prerequisites

If using OpenAI instead of Gemini, also add OPENAI_API_KEY.

​
Run the example

Clone and install

Clone the repo and install dependencies from the root:

Configure environment

Create a .env file at the repo root with your Stream and Gemini API keys.

Run the agent

From the example directory:
The CLI opens a browser demo with your camera. Position yourself so the camera can see your full body, then perform a golf swing. The agent analyzes your form and gives spoken feedback.

​
How it works

The agent combines a Realtime LLM with a YOLO pose processor:
  1. Video capture — the user’s camera feeds video to the agent
  2. Pose detection — YOLO analyzes each frame and extracts body position data
  3. LLM processing — Gemini Realtime receives video frames at 3 FPS
  4. Feedback — the agent speaks coaching guidance based on golf_coach.md instructions
The fps=3 parameter controls how many frames per second are sent to the model. Higher FPS gives more detail but uses more tokens.

​
Customize

  • Change FPS: gemini.Realtime(fps=5) for lower cost, fps=10 for more detail
  • Switch to OpenAI: replace with openai.Realtime(fps=3) in agent.py
  • Edit coaching style: modify golf_coach.md in the example directory
  • Different YOLO models: use YOLOProcessor for object detection instead of pose estimation

​
Next Steps

Video Agents

VLMs, processors, and realtime video patterns

Video Processors

Build custom detection and analysis pipelines

Live Sports Commentator

Use Roboflow object detection for multi-object tracking

Ultralytics Integration

Explore YOLO model options and configuration