An intelligent multi-agent skincare recommendation system built for the Kaggle AI Agents Intensive Capstone Project
- Overview
- Problem Statement
- Why Agents?
- Features
- Project Structure
- Future Enhancements
- Team
- Contributing
- License
- Acknowledgments
Glow Guide AI is a sophisticated multi-agent system that provides personalized skincare recommendations through intelligent collaboration between specialized AI agents. Built as part of Google and Kaggle's 5-Day AI Agents Intensive course, this project demonstrates practical applications of agentic AI, memory management, tool integration, and agent-to-agent communication.
Finding the right skincare products is overwhelming for most consumers navigating thousands of products with varying ingredients, conflicting reviews, and personalized skin concerns. Traditional solutions like dermatologist consultations are expensive and inaccessible, while generic online recommendations lack personalization and fail to consider individual skin histories, environmental factors, and product interactions.
Why This Matters:
- Improper skincare choices lead to adverse reactions and wasted money
- Skincare is deeply personal and requires adaptive, context-aware guidance
- Consumers need accessible, evidence-based recommendations at scale
This problem demands multiple interconnected capabilities that benefit from autonomous, goal-oriented systems:
- Specialization: Different agents handle profile analysis, ingredient research, and recommendation synthesis
- Memory & Adaptation: Long-term memory tracks skin evolution and product effectiveness over time
- Tool Integration: Agents access external databases, web search APIs, and scientific literature dynamically
- Natural Interaction: Conversational interface allows users to describe concerns naturally rather than filling complex forms
- Scalability: Multi-agent architecture enables independent scaling and enhancement of each component
- User Profile Agent: Maintains detailed user skin profiles with session and long-term memory
- Research & Analysis Agent: Leverages web search and ingredient databases for product research
- Recommendation Engine Agent: Synthesizes insights to generate personalized recommendations
- 🎯 Personalized Recommendations: Tailored product suggestions based on skin type, concerns, and preferences
- 🧠 Adaptive Memory: Tracks skin evolution, product effectiveness, and user feedback over time
- 🔍 Real-Time Research: Dynamic ingredient analysis and product verification using external APIs
- 💬 Natural Conversations: Intuitive chat interface for describing skin concerns and receiving guidance
⚠️ Safety Warnings: Identifies ingredient interactions and potential allergens- 📊 Routine Building: Complete morning/evening routines with application order and wait times
- 🔄 Continuous Learning: System improves recommendations based on user feedback
- 🖼️ Computer Vision Integration: Skin condition assessment from uploaded photos
- 👨⚕️ Dermatologist Agent: Medical knowledge base for condition identification
- 📈 Routine Tracking & Analytics: Progress visualization and correlation analysis
- 🌍 Community Learning: Federated learning from anonymized user feedback
- 🧪 Ingredient Interaction Database: Comprehensive formulation science rules
- ✅ Automated agent evaluation framework with benchmark datasets
- 🎤 Multi-modal interface with voice interaction support
- 📱 Mobile deployment with offline capabilities
- 🛒 E-commerce integration for seamless purchasing
- 🔬 Reinforcement learning for optimization
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow PEP 8 style guidelines
- Write unit tests for new features
- Update documentation as needed
- Ensure all tests pass before submitting PR
This project is licensed under the MIT License - see the LICENSE file for details.
- Google & Kaggle for the AI Agents Intensive course and capstone project
- Agent Development Kit (ADK) team for the powerful framework
- The open-source AI community for tools and inspiration
- Beta testers who provided valuable feedback on agent performance
- Project Link: https://github.com/SulthanNK/Glow-Guide
- Kaggle Notebook: https://www.kaggle.com/code/sulthannk/glow-guide-ai
- Competition: Agents Intensive Capstone Project
Made with ❤️ for the Kaggle AI Agents Intensive Capstone Project
Empowering personalized skincare through intelligent AI agents