RiftRewindProject

Rift Rewind

AI-powered League of Legends year-end review generator for Riot Games API Challenge 2025. Creates personalized Spotify Wrapped-style dashboards with deep AI insights using Claude Sonnet 4.

✨ Features

Core Functionality

Two-Phase Data Flow: Fast profile load (<5s) + deep AI analysis (3-6 min)
Smart Caching System: 80-90% faster repeat analyses with JSON-based caching
Accurate Match Statistics: Real champion stats from match history (not estimates)
Deep AI Insights: Claude Sonnet 4 generates unique, personalized analysis with specific numbers
Challenge System Integration: Extracts Riot’s challenge data for deeper insights
Champion Pool Analysis: Identifies main champions with real champion icons from DDragon
Rate Limit Optimization: Intelligent handling of Riot API (20/s, 100/2min) and Bedrock limits

UI/UX

Polished Dashboard: Glass-morphism UI with smooth animations
Champion Spotlights: Circular progress rings with real champion portraits
Interactive Season Chart: Visualize rank progression over time with hover annotations
Performance Highlights: Key stats displayed with visual flair
AI-Generated Personality: Unique playstyle personas based on match data
Responsive Design: Works beautifully on desktop and mobile

Technical Excellence

Type-Safe APIs: Pydantic schemas for all endpoints
Config Validation: Clear error messages on startup
Enhanced Logging: Detailed progress tracking with cache hit rates
Health Monitoring: Real-time stats for rate limiters and cache
Retry-Safe Caching: Prevents re-fetching data on API throttling

🏗️ Architecture

Frontend: Next.js 16 + React 19 + TypeScript + Tailwind CSS + Framer Motion
Backend: FastAPI (Python 3.12+) with type-safe Pydantic schemas
AI: AWS Bedrock - Claude Sonnet 4 (anthropic.claude-sonnet-4-20250514-v1:0)
Data Source: Riot Games API (Match-V5, Summoner-V4, League-V4)
Caching: JSON-based file caching for 80-90% performance improvement
Region: North America only (expandable architecture)

🚀 Prerequisites

Node.js (v18+)
Python 3.12+
Riot Games API Key (Get one here)
AWS Account with Bedrock access to Claude Sonnet 4

⚡ Quick Start

1. Environment Setup

Backend - Create backend/.env:

RIOT_API_KEY=your_riot_api_key_here
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-1

Frontend - Create frontend/.env.local:

NEXT_PUBLIC_API_URL=http://localhost:8000

2. Installation

# Backend
cd backend
pip install -r requirements.txt

# Frontend
cd frontend
npm install

3. Run Application

Terminal 1 - Backend:

cd backend
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Terminal 2 - Frontend:

cd frontend
npm run dev

Access: Open http://localhost:3000

📊 Performance Metrics

Metric	First Analysis	Repeat Analysis	Improvement
Time	3-6 minutes	30-60 seconds	⚡ 80-90% faster
API Calls	~200	~10-20	💰 92% reduction
Cache Hit Rate	0%	95-100%	🎯 Perfect caching

🎨 UI Improvements (Latest)

✅ Real Champion Icons: Uses DDragon CDN for authentic champion portraits
✅ Polished Champion Cards: Enhanced hover effects, shadows, and animations
✅ Interactive Season Chart: Visualize rank progression with annotations
✅ Better Spacing: Improved layout with larger headings and emoji accents
✅ Smooth Transitions: Framer Motion animations throughout

🔧 API Endpoints

Core Endpoints

GET /api/health - Health check with cache & rate limiter stats
GET /api/profile/{game_name}/{tag_line} - Fast profile fetch (~5s)
POST /api/analysis/{game_name}/{tag_line} - Deep AI analysis (3-6 min)

Usage Example

# Check health & cache stats
curl http://localhost:8000/api/health

# Get profile
curl "http://localhost:8000/api/profile/Doublelift/NA1"

# Generate analysis
curl -X POST "http://localhost:8000/api/analysis/Doublelift/NA1" \
  -H "Content-Type: application/json" \
  -d '{"num_matches": 100}'

🧪 Testing

# Quick health check
curl http://localhost:8000/api/health

# Run improvement tests
cd tests
python test_improvements.py

# Test caching performance
# 1. Analyze a player (first time = slow)
# 2. Analyze same player (second time = fast!)
# 3. Check logs for: "💾 Cache hits: 100/100 (100%)"

See HOW_TO_RUN.md for comprehensive testing guide.

📁 Project Structure

backend/
  app/
    main.py           # API endpoints with type-safe responses
    schemas.py        # Pydantic models
  services/
    cache_manager.py  # JSON-based caching system (NEW)
    config.py         # Environment validation (NEW)
    agent_tools.py    # Match analysis tools
    structured_analysis_service.py  # AI coordinator
  cache/              # Auto-generated cache files
    matches.json
    profiles.json

frontend/
  app/                # Next.js app router
  features/
    dashboard/        # Main dashboard components
      Charts/         # Season trend visualization
      HeroRow/        # Champion spotlights with icons
  lib/
    api-client.ts     # Type-safe backend communication
    types.ts          # TypeScript definitions

🎯 Key Features Explained

JSON-Based Caching

Stores match data locally in backend/cache/
24-hour TTL for matches, 1-hour for profiles
Automatic cache hit tracking: “💾 Cache hits: 95/100 (95%)”
Reduces API calls by 92% for repeat analyses

AI Analysis

Uses Claude Sonnet 4 for deep insights
Analyzes 50-200 matches per player
Generates personality profiles based on playstyle
Provides specific stats and trends

Champion Icons

Real-time loading from Riot’s DDragon CDN
Fallback to styled initials if image fails
Circular progress rings showing win rates
Smooth hover animations

Season Chart

Interactive rank timeline
Hover to see monthly events
Click to pin annotations
Responsive design

🐛 Troubleshooting

Backend won’t start

Ensure .env file exists with valid API keys
Run pip install -r requirements.txt
Check Python version: python --version (need 3.8+)

Frontend errors

Run npm install in frontend directory
Verify .env.local has correct API URL
Check Node version: node --version (need 18+)

Cache not working

Look for “💾 Cache hits” in backend console
Check /api/health endpoint for cache stats
Cache files auto-create in backend/cache/

Champion icons not loading

Check internet connection (uses Riot CDN)
Verify champion names in backend response
Fallback to styled letters if CDN fails

📚 Documentation

HOW_TO_RUN.md - Comprehensive setup and testing guide
IMPROVEMENTS.md - Feature documentation and performance metrics
TESTING_GUIDE.md - Step-by-step testing procedures
IMPLEMENTATION_SUMMARY.md - Technical implementation details
.github/copilot-instructions.md - AI agent development guide

🏆 Hackathon Ready!

This project is production-quality with:

✅ Smart caching (80-90% performance boost)
✅ Type-safe APIs
✅ Comprehensive error handling
✅ Real-time champion icons
✅ Interactive charts
✅ Polished UI/UX
✅ Complete documentation

Perfect for Riot Games API Challenge 2025!

🎬 Demo Tips

Show First Analysis: 3-6 minutes, explain AI is analyzing 100 matches
Show Cache Working: Same player takes 30-60 seconds, highlight cache hits
Interactive Features: Hover on champions, click chart annotations
Performance Stats: Show /api/health with cache statistics

📝 License

MIT License - See LICENSE file for details

🙏 Acknowledgments

Riot Games for the API Challenge and comprehensive API
AWS Bedrock for Claude Sonnet 4 access
Next.js and FastAPI communities
shadcn/ui for beautiful components

Built with ❤️ for Riot Games API Challenge 2025

Frontend: Next.js 16 + React 19 + TypeScript + Tailwind CSS + Framer Motion- Frontend: Next.js with TypeScript and Tailwind CSS
Backend: FastAPI (Python 3.12)- Backend: FastAPI with Python
AI: AWS Bedrock - Claude Sonnet 4 (anthropic.claude-sonnet-4-20250514-v1:0)- AI Agent: AWS Bedrock (Claude 3 Haiku) with LangChain
Data Source: Riot Games API (Match-V5, Summoner-V4, League-V4)- Data Source: Riot Games API
Region: North America only (for hackathon scope)- Caching: In-memory retry-safe caching system

Prerequisites## Prerequisites

Node.js (v18+)- Node.js (v18+)
Python 3.12+- Python (v3.8+)
Riot Games API Key (Personal Key sufficient)- Riot Games API Key
AWS Account with Bedrock access to Claude Sonnet 4- AWS Account with Bedrock access

Quick Start## Setup

1. Environment Setup### 1. Environment Configuration

Backend - Create backend/.env:Create backend/.env:


RIOT_API_KEY=your_riot_api_key_hereRIOT_API_KEY=your_riot_api_key_here

AWS_ACCESS_KEY_ID=your_aws_access_keyAWS_ACCESS_KEY_ID=your_aws_access_key

AWS_SECRET_ACCESS_KEY=your_aws_secret_keyAWS_SECRET_ACCESS_KEY=your_aws_secret_key

AWS_REGION=us-east-1AWS_REGION=us-west-2

Frontend - Create frontend/.env.local:### 2. Installation


NEXT_PUBLIC_API_URL=http://localhost:8000# Install dependencies

```npm install

cd backend && pip install -r requirements.txt

### 2. Installation & Run```



**Option A: Using startup scripts (Windows)**### 3. Run Application

```bash

# Terminal 1 - Backend```bash

start_backend.bat# Backend (Terminal 1)

cd backend

# Terminal 2 - Frontend  python -m uvicorn app.main:app --reload --host 127.0.0.1 --port 8000

start_frontend.bat

```# Frontend (Terminal 2)

npm run dev

**Option B: Manual**```

```bash

# Install backend dependencies## API Endpoints

cd backend

pip install -r requirements.txt### Core Endpoints



# Install frontend dependencies- `GET /api/player/{game_name}/{tag_line}` - Get player PUUID

cd ../frontend- `POST /api/year-rewind/{game_name}/{tag_line}` - Generate AI review

npm install- `GET /api/health` - Health check with rate limiter stats



# Run backend (Terminal 1)### Usage Example

cd backend

python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000```bash

curl -X POST "http://localhost:8000/api/year-rewind/Faker/KR1"

# Run frontend (Terminal 2)```

cd frontend

npm run dev## Testing

### 3. Access Application# Run all tests

python tests/test_agent.py

- **Frontend**: http://localhost:3000

- **Backend API**: http://localhost:8000# Test rate limits

- **API Docs**: http://localhost:8000/docspython tests/test_rate_limits.py

### 4. Test It# Test caching behavior

python tests/test_throttling_behavior.py

Enter a Riot ID on the landing page:```

- Format: `GameName#TAG`

- Example: `i will int#akali` (NA region)## Performance

## API Endpoints| Metric              | Current     | Optimized           |

| ------------------- | ----------- | ------------------- |

### User-Facing Endpoints| Analysis Time       | 3-5 minutes | 1-2 minutes         |

| Retry Overhead      | 0% (cached) | Previously 200-300% |

- `GET /api/profile/{game_name}/{tag_line}` - Fast profile fetch (<5s)| Rate Limit Handling | Intelligent | Previously basic    |

  - Returns: Summoner icon, level, rank, main role

  ## Current Limitations & Improvements

- `POST /api/analysis/{game_name}/{tag_line}` - Deep AI analysis (3-6 min)

  - Returns: Top champions, stat highlights, AI insights, personality### Rate Limits

  - Query param: `num_matches` (default: 100)

- **Riot API**: 20 req/s, 100 req/2min (Personal key)

### System Endpoints- **Bedrock**: ~5-10 req/s (Default limits)

- `GET /api/health` - Health check with rate limiter stats**Improvements**:

- `GET /` - API version info

- Apply for Riot Production API key (500 req/s, 30k req/10min)

## Data Flow- Request Bedrock rate limit increase

- Switch to Claude 3.5 Sonnet for better quality

User Input (Riot ID)### Model Options

↓

Phase 1: Profile Service (fast)- Current: Claude 3 Haiku (fast, higher limits)

- Get PUUID from Riot ID- **Upgrade**: Claude 3.5 Sonnet (better quality, lower limits)

- Fetch summoner data (icon, level)- **Premium**: Claude 3 Opus (highest quality, lowest limits)

- Get ranked stats (tier, division, LP, winrate)

- Determine main role (last 10 matches)### Future Enhancements

↓

Phase 2: Analysis Service (deep)- OP.GG MCP Integration: Real-time meta insights and build recommendations

- Fetch 50-100 match history- **Champion Synergy Analysis**: Team composition suggestions

- Extract comprehensive stats (KDA, CS, vision, damage, gold)- **Trend Analysis**: Performance improvement tracking over time

- Analyze champion pool (games, winrates, playstyles)- **Multi-Region Support**: Support for all Riot regions

- Extract challenge data (lane dominance, vision advantage)

- Calculate timeline patterns (early vs late game)## Project Structure

- Generate AI insights with Claude Sonnet 4

↓```

Frontend DashboardRiftRewindProject/

- Display profile with rank├── app/                    # Next.js frontend

- Show top 3 champions (accurate stats)├── backend/

- Highlight 3 best stats│   ├── app/main.py        # FastAPI application

- Show AI-generated deep insight│   ├── services/

- Display personality type│   │   ├── year_rewind_agent.py    # Main AI agent

```│ │ ├── agent_tools.py # Analysis tools

│ │ ├── riot_api.py # Riot API client

Project Structure│ │ ├── rate_limiter.py # Riot API rate limiting

│ │ └── bedrock_rate_limiter.py # Bedrock rate limiting

```│ └── .env # Environment variables

RiftRewindProject/├── tests/ # Test suite

├── frontend/ # Next.js application└── README.md

│ ├── app/ # App router pages```

│ ├── components/ # UI components

│ ├── features/ # Feature modules## Contributing

│ ├── lib/ # Utilities

│ │ ├── api-client.ts # Backend API integration1. Model Experimentation: Test different models for quality and speed

│ │ └── usePlayerDataFetch.ts2. OP.GG Integration: Add real-time meta analysis using OP.GG MCP

│ └── .env.local # Frontend config3. Performance Optimization: Figure out a way to reduce analysis time.

│4. UI/UX: Improve frontend design and user experience

├── backend/ # FastAPI application5. Additional Features: Champion recommendations, team analysis, etc.

│ ├── app/6. Tool and System Prompts: Work on finding errors and enhancing the prompts for the agent tools and the system prompt that initalises the workflow.

│ │ └── main.py # API endpoints

│ ├── services/## Rate Limit Optimization

│ │ ├── profile_service.py # Fast profile fetching

│ │ ├── structured_analysis_service.py # AI analysisThe system includes intelligent rate limiting for both APIs:

│ │ ├── agent_tools.py # Data extraction tools

│ │ ├── riot_api.py # Riot API client- Riot API: Respects 20 req/s and 100 req/2min limits

│ │ ├── rate_limiter.py # Riot API rate limiting- Bedrock: Handles throttling with exponential backoff

│ │ └── bedrock_rate_limiter.py # Bedrock throttling- Caching: Prevents re-fetching on retries (major performance improvement)

│ └── .env # Backend config

│## Troubleshooting

├── tests/ # Test scripts

├── start_backend.bat # Backend startup script### Issues

└── start_frontend.bat # Frontend startup script

```- Bedrock Throttling: Switch to Claude 3 Haiku or request limit increase

Riot Rate Limits: Apply for Production API key

Testing- Long Analysis Time: Expected 3-5 minutes for 50+ matches

```bash### Debug Commands

Test profile service

cd tests```bash

python test_profile.py# Check rate limiter stats

curl http://localhost:8000/api/health

Test structured analysis

python test_structured_analysis.py# Test with specific player

curl -X POST “http://localhost:8000/api/year-rewind/YourName/TAG”

Test rate limiting```

python test_rate_limits.py

```## License

Performance MetricsMIT License - see LICENSE file for details.

Metric	Target	Notes
Profile Load	<5 seconds	Initial data for header
Full Analysis	3-6 minutes	100 matches with AI
Match Fetching	~30s	Riot API rate limited
AI Generation	~10-30s	Claude Sonnet 4 inference

Current Scope (Hackathon)

✅ Implemented

NA region only
Profile + Analysis endpoints
Top 3 champions with accurate stats
AI-generated insights with Claude Sonnet 4
Challenge system data extraction
Clean, production-ready codebase

🚧 Future Enhancements

Multi-region support
Historical progress/growth charts
Champion recommendations
Rank timeline visualization
Share/export functionality
Performance optimization (<2 min total)

Known Limitations

Analysis Time: 3-6 minutes for 100 matches (rate limit constrained)
Region: NA only (easily expandable)
AI Quality: Still being refined based on feedback
Growth Charts: Mock data (needs historical tracking implementation)

Contributing

Priority areas for improvement:

AI Prompt Engineering: Enhance insight quality and variety
Performance: Optimize match fetching and caching
Multi-Region: Add support for other regions
Historical Data: Implement rank/performance timeline tracking
UI Polish: Refine animations and responsive design

Troubleshooting

Backend Won’t Start

Check .env file exists in backend/ directory
Verify AWS credentials have Bedrock access
Ensure Riot API key is valid

Frontend Shows “Player not found”

Verify player exists in NA region
Check backend is running on port 8000
Check browser console for API errors

Analysis Takes Too Long

Expected for first-time analysis (3-6 minutes)
Check /api/health for rate limiter stats
Consider reducing num_matches parameter

License

MIT License - See LICENSE file for details.

Built for Riot Games API Challenge 2025

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