What to Build

Build an AI-powered agent using AWS AI services and a League API to help League of Legends players reflect, learn, and improve.

Using League Developer AI end-of-game match data, you’ll create an intelligent agent that generates personalized end-of-year insights players can actually use. The agent should help answer the kinds of questions players ask themselves or potentially even share surprising insights — highlighting trends, compiling key statistics and achievements, identifying areas for growth, and generating engaging retrospectives for players to celebrate and reflect on their past year in League. 

Participants will work with the following dataset:

• Full-Year Match History – use this to identify growth areas, uncover persistent habits, and generate an end-of-year recap experience.

Build tools that enable:

• Insights into persistent strengths and weaknesses

• Visualizations of player progress over time

• Fun, shareable year-end summaries (e.g., most-played champions, biggest improvements, highlight matches)

• Social comparisons (e.g., how you stack up against friends or which playstyles complement yours)

• Socially shareable moments and insights — creative ways for players to engage with friends on social platforms using their data

These tools should go beyond what’s available on op.gg — they must demonstrate how generative AI on AWS can turn raw gameplay data into personalized, meaningful, and enjoyable retrospectives for players of all skill levels.

What to Submit

• Access: Provide a public URL to your working application 

• Code: Provide a URL to your public code repository to show how your project was built.

  • The repository must be public and open source by including one of the following open source license files:
    • MIT - https://opensource.org/licenses/MIT
    • Apache 2.0 - https://opensource.org/licenses/Apache-2.0

• Demo: Include a video (should be about 3 minutes) that demonstrates your submission. Videos must be uploaded to YouTube, Vimeo, or Facebook Video and made public.

• Methodology Write Up: Include a brief explanation of how your coaching agent works — your approach to analyzing match data, any additional data sources used, and the logic behind key insights or recommendations. Share what you discovered or learned during the development process, including challenges, improvements, or surprising patterns.

• Tooling: Include an explanation of the AWS AI services used to build the project.

• Optional Tagging: The AWS platform enables you to assign custom tags to your resources. A tag is a key-value pair applied to a resource to hold metadata about that resource. Each tag is a label consisting of a key and an optional value. Not all services and resource types currently support tags (see Services that support the Resource Groups Tagging API). We recommend that any infrastructure you launch be tagged with “key: rift-rewind-hackathon value: 2025”.