A multi-agent AI system that surpasses ChatGPT for Kaggle competitions by providing context-aware, targeted guidance with momentum preservation.

ChatGPT is great, but for Kaggle competitions it:

• ❌ Loses context between sessions
• ❌ Gives generic advice (not competition-specific)
• ❌ Can't track your progress
• ❌ Doesn't integrate with Kaggle's ecosystem

This tool fixes all of that.

1. CompetitionSummaryAgent - Deep competition analysis
2. NotebookExplainerAgent - Top solution insights
3. DiscussionHelperAgent - Community wisdom
4. ErrorDiagnosisAgent - Instant debugging
5. CodeFeedbackAgent - Best practice reviews
6. ProgressMonitorAgent - Stagnation detection
7. TimelineCoachAgent - Competition planning
8. MultiHopReasoningAgent - Cross-domain insights
9. IdeaInitiatorAgent - Novel approach generation
10. CommunityEngagementAgent - Feedback analysis

• Groq (Llama 3.3 70B) - Code handling
• Gemini (2.5 Flash) - Fast retrieval
• Perplexity (Sonar) - Strategic reasoning
• Ollama (CodeLlama) - Deep scraping

• ⚡ 15x faster repeat queries (25s → 1.5s)
• 🎯 Zero quality loss (caches detailed responses)
• 🚀 Production-ready performance

• 🌙 Beautiful dark theme
• 💬 Chat persistence
• 🔍 Competition autocomplete
• 📊 LangGraph visualization

# 1. Clone & setup
git clone https://github.com/YOUR-USERNAME/Kaggle-competition-assist.git
cd Kaggle-competition-assist
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# 2. Configure API keys
cp .env.example .env
# Add your API keys to .env

# 3. Run backend (separate terminal)
python minimal_backend.py

# 4. Run frontend (separate terminal)
streamlit run streamlit_frontend/app.py

# 5. Open http://localhost:8501 and try these queries:

Test Queries:

1. "What is the evaluation metric for Titanic?"
   (Wait 20s, then ask SAME question again - see 15x speedup!)

2. "Review my code: df['target_mean'] = df['target'].mean()"
   (Watch it catch data leakage!)

3. "Give me ideas for Titanic competition"
   (Get competition-specific advice!)

📖 Full guide: See docs/USER_GUIDE.md

User Query
    ↓
Intent Router (keyword-based)
    ↓
┌─────────────────────────────────┐
│   10 Specialized Agents         │
│   ↕                              │
│   4 LLM Providers                │
│   (Groq, Gemini, Perplexity)    │
└─────────────────────────────────┘
    ↓
ChromaDB Cache (15x speedup!)
    ↓
Final Response (1-2s!)

• Backend: Flask + Python 3.11
• Frontend: Streamlit (dark theme)
• LLM Orchestration: LangChain, CrewAI, AutoGen, LangGraph
• Vector DB: ChromaDB (RAG pipeline)
• Scraping: Playwright + Kaggle API
• Deployment: AWS EC2 (production-ready)

Cache Hit Rate: ~80% in production

Kaggle-competition-assist/
├── agents/                 # 10 specialized AI agents
├── orchestrators/          # CrewAI/AutoGen/LangGraph
├── workflows/              # LangGraph workflows
├── llms/                   # Multi-model LLM config
├── RAG_pipeline_chromadb/  # Vector database
├── scraper/                # Playwright scraping
├── Kaggle_Fetcher/         # Kaggle API
├── streamlit_frontend/     # Dark mode UI
├── docs/                   # Complete documentation
│   ├── USER_GUIDE.md       # 👈 Start here!
│   ├── QUICK_START.md
│   ├── AWS_DEPLOYMENT_GUIDE.md
│   └── [12+ more guides]
├── minimal_backend.py      # Flask backend (3,200+ lines)
└── requirements.txt        # All dependencies

Just created an AWS instance? Start here: NEXT_STEPS_AFTER_AWS_INSTANCE.md

Quick References:

• 🎯 DEPLOYMENT_QUICK_GUIDE.md - 30-minute guide
• ✅ DEPLOYMENT_CHECKLIST_PRINTABLE.md - Print & follow
• 🧪 DEPLOYMENT_TESTING_CHECKLIST.md - Comprehensive testing

Automated Scripts:

• deployment_script.sh - One-command setup
• setup_services.sh - Service configuration
• transfer_env_to_ec2.ps1 - Transfer .env (Windows)

Complete guide: docs/AWS_DEPLOYMENT_GUIDE.md

Quick deploy (30 minutes):

# 1. Launch t3.micro Ubuntu instance (FREE tier!)
# 2. SSH in and run:
wget https://raw.githubusercontent.com/YOUR-USERNAME/Kaggle-competition-assist/main/deployment_script.sh
chmod +x deployment_script.sh
./deployment_script.sh

# 3. Transfer .env, then:
./setup_services.sh

# 4. Access at http://YOUR-EC2-IP

• 📖 User Guide - Complete testing guide
• ⚡ Quick Start - 5-minute test
• 🚀 AWS Deployment - Production setup
• 🎨 LangGraph Visualization - Debug dashboard
• ⚡ Smart Cache - Performance details
• 📊 Features - Complete feature list

Live Demo: [YOUR-AWS-URL] (coming soon)

Test Locally:

git clone https://github.com/YOUR-USERNAME/Kaggle-competition-assist.git
cd Kaggle-competition-assist
# Follow Quick Start above

We want YOUR feedback! Try the tool and let us know:

1. Try 3 queries from docs/QUICK_START.md
2. Compare to ChatGPT
3. Share your experience on LinkedIn or GitHub Issues

Use the template in docs/USER_GUIDE.md

Open an issue with:

• Query you tried
• Expected vs actual behavior
• Screenshots if possible

• Lines of Code: 6,200+
• Agents: 10 specialized
• LLM Providers: 4 (Groq, Gemini, Perplexity, Ollama)
• Performance Gain: 15x (cache)
• Development Time: 2 weeks
• Documentation Pages: 12+

Contributions welcome! Check out:

• Open issues
• Feature requests
• Documentation improvements

Areas we'd love help with:

• More competition support
• Additional agents
• UI/UX improvements
• Performance optimization

MIT License - See LICENSE file

Built with:

• LangChain
• CrewAI
• AutoGen
• LangGraph
• Streamlit
• ChromaDB

• LinkedIn: [Your LinkedIn]
• GitHub: [Your GitHub]
• Email: [Your Email]

• More competitions support
• Advanced notebook analysis
• Real-time collaboration
• Mobile app
• API for programmatic access

⭐ If you find this useful, please star the repo and share with fellow Kagglers!

🚀 Built by a Kaggler, for Kagglers. Let's dominate competitions together!

Multi-agent workflow showing 13 nodes and intelligent routing

Last Updated: October 2025