🏎️ F1 Agentic Team Principal

The F1 season is here — so why not build something fun with it?

I recently came across the OpenF1 API — an open-source, real-time Formula 1 data API — and wanted to take it for a spin. So I built this: an AI-powered F1 assistant that actually knows what it's talking about. Ask it about lap times, telemetry, standings, paddock rumors — it'll dig up the answer.

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🧠 What It Does

This project combines a multi-agent AI system (built with Agno) with the full OpenF1 API to answer any F1-related question — live data, historical stats, race strategy, and more.

The system runs two specialist agents coordinated by a "Team Principal":

Agent	Role
🏎️ F1 Strategist	Pulls live telemetry, lap times, pit data, standings from OpenF1
🗞️ F1 News Scout	Searches the web for latest paddock news, driver transfers & rumors
🏁 Team Principal	Orchestrates both agents and synthesizes the final answer

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🔌 Data Sources

All live F1 data comes from OpenF1 — a free, open-source REST API providing real-time and historical F1 data:

• Sessions, meetings & race calendars
• Lap times & sector splits
• Car telemetry (speed, RPM, throttle, gear)
• Pit stops & tyre stints
• Race positions & intervals
• Driver and team standings
• Weather data
• Race control messages & flags
• Team radio references

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🛠️ Tech Stack

Layer	Technology
AI Framework	Agno (v2.5+)
LLM	Azure OpenAI (GPT-4o)
F1 Data	OpenF1 API
Web Search	DuckDuckGo (via ddgs)
Frontend	Streamlit
Language	Python 3.12+

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🚀 Getting Started

1. Prerequisites

• Python 3.12+
• uv (recommended) or pip
• An Azure OpenAI account with a GPT-4o deployment

2. Clone the Repo

git clone https://github.com/your-username/f1-agentic-team-principal.git
cd f1-agentic-team-principal

3. Set Up Environment Variables

Create a .env file in the root directory:

AZURE_OPENAI_API_KEY=your_api_key_here
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_DEPLOYMENT=gpt-4o
AZURE_OPENAI_API_VERSION=2024-12-01-preview

4. Install Dependencies

uv sync
# or with pip:
pip install -r requirements.txt

5. Run the App

streamlit run app.py

Open your browser at http://localhost:8501 and start asking questions!

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💬 Example Questions

• "Who is leading the Drivers' Championship after the last race?"
• "Show me Verstappen's lap times from the most recent session."
• "What are the latest rumors about the 2026 driver market?"
• "Compare the tyre strategies used in the last race."
• "What's the current Constructor standings?"

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📁 Project Structure

f1-agentic-team-principal/
├── app.py              # Streamlit frontend
├── src/
│   ├── f1_agent.py     # Multi-agent team definition
│   └── f1_client.py    # OpenF1 API client
├── requirements.txt
├── .env                # Your credentials (not committed)
└── README.md

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📝 Notes

• The INFO Reasoning model log you may see in the terminal is expected — Agno uses Chain-of-Thought reasoning when a native reasoning model isn't available.
• OpenF1 is a community-maintained API — some endpoints (e.g., championship standings) are in beta.

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📄 License

MIT — feel free to fork, remix, and build your own F1 toys.

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Built for fun. Lights out and away we go. 🏎️💨

Vrrrrrrrrrooooooommmmm 🔥