An AI-powered multi-agent platform that helps beginners and experienced data scientists navigate Kaggle competitionsโfrom understanding datasets to generating notebooks and preparing submissions.
Empowering Kaggle enthusiasts with AI-driven guidance from dataset exploration to competition submission.
Kaggle Mentor AI was developed as part of the Kaggle Vibe Coding Agents Capstone Project 2026.
This project was created to explore the capabilities of modern AI agent systems in solving real-world machine learning workflow challenges. The goal was to design an intelligent assistant capable of guiding users throughout the entire Kaggle competition lifecycleโfrom dataset exploration to submission generation.
The project aligns closely with the competition's focus on:
- Intelligent AI agents
- Multi-agent collaboration
- Developer productivity
- AI-assisted workflows
- Educational accessibility
- Practical deployment of agent systems
I would like to thank the Kaggle team and the Google Developer ecosystem for organizing initiatives that encourage innovation in AI agents and human-AI collaboration.
Kaggle competitions involve multiple complex stages including dataset understanding, exploratory data analysis, feature engineering, model selection, notebook creation, model evaluation, and submission generation.
Beginners often struggle to determine the next step, while experienced participants spend considerable time performing repetitive tasks.
Kaggle Mentor AI addresses this challenge through a collaborative multi-agent architecture where specialized AI agents work together to provide intelligent, contextual assistance at every stage of the competition lifecycle.
Kaggle Mentor AI introduces a modular multi-agent system where each AI agent specializes in a specific task.
Instead of relying on a single general-purpose chatbot, multiple specialized agents collaborate to:
- Analyze datasets
- Perform exploratory data analysis
- Recommend ML strategies
- Generate starter notebooks
- Guide submission preparation
Kaggle Mentor AI is an intelligent multi-agent AI platform designed to simplify the entire Kaggle competition workflow.
Whether you're participating in your first Kaggle competition or aiming to climb the leaderboard, Kaggle Mentor AI provides structured, AI-powered assistance throughout your journey.
Kaggle Mentor AI follows a multi-agent orchestration architecture inspired by principles from the Google Agent Development Kit (Google ADK) ecosystem.
The system decomposes complex machine learning workflows into specialized AI agents, each responsible for a clearly defined task:
| Agent | Responsibility |
|---|---|
| Dataset Agent | Dataset understanding and feature analysis |
| EDA Agent | Exploratory data analysis and visualization |
| Strategy Agent | Model recommendations and ML strategy |
| Notebook Agent | Starter notebook generation |
| Submission Agent | Submission preparation and competition guidance |
This design follows the core Google ADK philosophy of:
- Task specialization
- Agent collaboration
- Modular orchestration
- Tool-augmented reasoning
- Scalable AI systems
Rather than relying on a single monolithic AI assistant, Kaggle Mentor AI uses specialized agents that communicate through a prompt-routing layer to provide more accurate, explainable, and context-aware recommendations.
โ Multi-Agent Architecture โ Agent Orchestration โ Tool-Augmented Agents โ Prompt Routing โ Specialized Agent Responsibilities โ Modular Agent Design
This architecture improves maintainability, scalability, and reasoning quality while enabling future expansion into persistent memory, autonomous workflows, and multi-LLM support.
https://www.youtube.com/watch?v=QY6ccoUa9B8
https://kaggle-mentor-ai-409406860748.asia-southeast1.run.app
- Automatic dataset understanding
- Target variable detection
- Missing value analysis
- Feature overview
- Data summary generation
- Distribution plots
- Correlation heatmaps
- Missing value reports
- Feature statistics
- Dataset summaries
Provides recommendations for:
- Data Cleaning
- Feature Engineering
- Model Selection
- Cross Validation
- Hyperparameter Tuning
- Performance Optimization
Automatically generates starter notebooks containing:
- Data Loading
- Data Cleaning
- Feature Engineering
- Model Training
- Evaluation
- Prediction
- Submission Creation
Helps users:
- Understand competition objectives
- Interpret evaluation metrics
- Recommend workflows
- Improve leaderboard performance
User
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React + TypeScript Frontend
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Prompt Routing Layer
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โผ โผ โผ โผ
Dataset EDA Agent Strategy Notebook
Agent Agent Agent
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Submission Agent
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Gemini API
| Traditional Chatbot | Kaggle Mentor AI |
|---|---|
| Single Prompt | Multiple Specialized Agents |
| Generic Responses | Domain Expertise |
| Limited Context | End-to-End Competition Guidance |
| Difficult to Scale | Modular Architecture |
| Technology | Purpose |
|---|---|
| React | Frontend Framework |
| TypeScript | Type Safety |
| Vite | Build Tool |
| Tailwind CSS | Styling |
| Gemini API | AI Intelligence |
| Multi-Agent Architecture | Agent Collaboration |
- Node.js v18+
- npm
- Git
Verify installation:
node -v
npm -v
git --versiongit clone https://github.com/Yuwin2008/Kaggle-Mentor-AI.git
cd Kaggle-Mentor-AInpm installCreate .env
VITE_GEMINI_API_KEY=your_api_keynpm run devOpen:
http://localhost:5173
npm run buildnpm run previewnpm run lint| Command | Description |
|---|---|
| npm install | Install dependencies |
| npm run dev | Start development server |
| npm run build | Create production build |
| npm run preview | Preview production build |
| npm run lint | Run ESLint |
- Multi-agent architecture
- Prompt routing system
- Responsive UI
- Type-safe codebase
- Environment variable support
- Production build support
- Scalable frontend architecture
Add screenshots for:
- Home Page
- Dataset Analysis
- Auto EDA
- Strategy Recommendations
- Notebook Generation
- Submission Assistant
Competition
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Dataset Analysis
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Exploratory Data Analysis
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Strategy Recommendation
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Notebook Generation
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Model Training
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Submission File
- Beginner Friendly
- AI-Powered Guidance
- Multi-Agent Collaboration
- End-to-End Workflow
- Faster Learning
- Intelligent Recommendations
- Modern Responsive Interface
- Authentication
- Persistent Memory
- AutoML
- Team Collaboration
- Cloud Notebook Execution
- Leaderboard Analytics
- Explainable AI
- Dataset Version Tracking
- Multi-LLM Support
- Agent Memory
Contributions are welcome.
- Fork the repository
- Create a feature branch
- Commit your changes
- Push the branch
- Open a Pull Request
godofthunder_2407
https://www.youtube.com/@GodofThunder2407