> ## Documentation Index
> Fetch the complete documentation index at: https://visionagents.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# AI Golf Coach
> Build a real-time golf coaching agent with YOLO pose detection and voice feedback
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](https://docs.ultralytics.com/tasks/pose/) pose detection to analyze body position with [Gemini Live](https://gemini.google/overview/gemini-live/) for real-time coaching — all running on [Stream's](https://getstream.io/) 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](/introduction/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](https://www.ultralytics.com/yolo) pose detection
* Process video at configurable FPS with [Gemini Live](https://ai.google.dev/) or [OpenAI Realtime](https://platform.openai.com/docs/guides/realtime)
* Deliver spoken coaching feedback based on body position and movement
* Hot-swap between AI providers with a one-line config change
## Prerequisites
```bash theme={null}
STREAM_API_KEY=
STREAM_API_SECRET=
GOOGLE_API_KEY=
```
If using OpenAI instead of Gemini, also add `OPENAI_API_KEY`.
## Run the example
Clone the repo and install dependencies from the root:
```bash theme={null}
git clone git@github.com:GetStream/Vision-Agents.git
cd Vision-Agents
uv sync
```
Create a `.env` file at the repo root with your Stream and Gemini API keys.
From the example directory:
```bash theme={null}
cd examples/02_golf_coach_example
uv run golf_coach_example.py run
```
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:
```python theme={null}
agent = Agent(
edge=getstream.Edge(),
agent_user=User(name="AI golf coach"),
instructions="Read @golf_coach.md",
llm=gemini.Realtime(fps=3),
processors=[
ultralytics.YOLOPoseProcessor(model_path="yolo26n-pose.pt")
],
)
```
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
VLMs, processors, and realtime video patterns
Build custom detection and analysis pipelines
Use Roboflow object detection for multi-object tracking
Explore YOLO model options and configuration