🧠 Souma — Real-Time AI League of Legends Coach

“Don’t just play League — learn it while you play.”

Souma is an AI-powered real-time coaching overlay for League of Legends.
It analyzes your live in-game state and provides specific, actionable advice — helping players improve their game sense, mechanics, and decision-making while they play.

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🌟 Inspiration

Most existing League tools only help before or after your game — with builds, stats, or post-match reviews.
But what about during the match, when decisions matter most?

League has an incredibly high skill floor — both strategically and mechanically.
Many players give up before learning how to actually enjoy the game.

So we asked:

What if an AI coach could guide you live, teaching better habits and strategy in real time?

That’s how Souma was born.

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⚙️ What It Does

Souma watches your gameplay and reacts just like a live coach would.
It reads your health, mana, gold, items, minimap state, and lane conditions, and gives guidance like:

• 🩸 “Back — low HP, enemy jungler nearby.”
• 💰 “Recall now — major item spike available.”
• 🧠 “Freeze the wave under tower.”
• 🗺️ “Rotate to dragon — 30 seconds to spawn.”

This helps players build instincts and learn strategy faster, turning frustration into confidence and wins.

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🏗️ How It Works

🔧 Backend

• FastAPI — Asynchronous API backend for real-time data processing.
• OpenCV — Captures game frames and extracts ROIs (health bar, mana, gold, minimap).
• Tesseract / EasyOCR — Optical character recognition for reading in-game text.
• Riot API Client — Fetches live match data (rate-limited).

🧠 AI Engines

• Rule Engine (F1, F6) — For deterministic events like low-health alerts and recall timing.
• LLM Engine (F2, F4) — For reasoning-based advice such as wave management and objective control.

🖥️ Frontend

• Electron + React + TypeScript — Cross-platform overlay interface.
• Zustand — Lightweight state management.
• TailwindCSS — Clean and adaptive styling.
• WebSocket — Real-time connection between backend and overlay.

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🧩 Challenges

• Building stable image processing for a live, animated game environment was a huge challenge.
• Audio synchronization for abilities and cues was complex to crossmatch in real time.
• Integrating voice input → LLM interactions took multiple rewrites, but it made the coach feel truly alive.

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🏆 Accomplishments

• Developed a working live computer vision pipeline for League of Legends.
• Implemented ROI-specific logic for gold, health, mana, and minimap awareness.
• Built an AI that prioritizes coaching advice intelligently (e.g., safety > objectives > wave control).
• Watching Souma call out a gank before it happened was an unforgettable moment.

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💡 What I Learned

• Never give up. Debugging real-time systems tests patience like nothing else.
• Image processing for AI in games still has huge room to grow.
• Audio-visual fusion is key to capturing complex game states.
• Voice-driven LLM input opens up new frontiers for interactive, adaptive AI experiences.

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🚀 Next Steps

• 🧩 Champion-specific advice modules (e.g., different coaching for Garen vs. Fiora).
• 🔊 Real-time audio feedback from the AI coach.
• 🧠 Personalized learning paths based on player history.
• 🕹️ Expand to other esports titles (Dota, Valorant, etc.).

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⚡ Running Locally

Clone & Install

git clone https://github.com/yourname/souma.git
cd souma
npm install