Hi Streamlit community! Just shipped my first AI product
and wanted to share it here 
Rise to Challenger is a League of Legends coaching app
that tells players what to fix after each game — not just
shows stats, but gives structured AI feedback benchmarked
against Challenger-level players.
Live demo: https://rise-to-challenger-m9ykf3u3jo5lhyzcvljcbu.streamlit.app/
GitHub: GitHub - Bubble0421/rise-to-challenger: AI-powered League of Legends coaching app with match review, counter guide, and agent-based analysis · GitHub
How it works:
- Pulls real match data from Riot Games API + Timeline API
- Benchmarks every stat vs Challenger average for your exact champion + role
- Multi-agent LangGraph pipeline: Comp → Execution → RAG → Reflection → Synthesis
- Local LLM via Ollama (Gemma 2B) — private, no API costs
- RAG with ChromaDB over 900+ Master+ match patterns
Three pages:
- Meta Analysis — champion tier list from Master+ data
- Player Review — post-game AI coaching report with training goals
- Counter Guide — 30-second pre-game matchup plan
This is my first AI product, built as a capstone project
Happy to answer any questions about the architecture or implementation!
