🎓 Adaptive Education Trainer

Agentic AI Meets Memory: Building Smarter Workflows That Learn Over Time

This project showcases how to build an adaptive AI learning assistant using CrewAI, Streamlit, OpenAI, and ChromaDB. The trainer teaches a topic, quizzes the user, evaluates the responses, and adapts its future teaching strategy by storing contextual memory.

It’s designed for educational, productivity, or coaching workflows that require agent-based reasoning and persistent memory.

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🚀 Features

• ✅ Multi-agent workflow using CrewAI
• ✅ Dynamic lesson and quiz generation using OpenAI GPT
• ✅ Feedback evaluation with real-time personalization
• ✅ Long-term memory powered by ChromaDB (Vector Store)
• ✅ Streamlit web app for clean UI and interactive demo

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🧰 Tech Stack

Component	Purpose
streamlit	Frontend UI
openai	LLM for teaching, quizzes, feedback
crewai	Agentic workflow management
chromadb	Persistent memory via vector DB

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📦 Requirements

• Python: >= 3.10 and < 3.13
  We recommend using pyenv to manage Python versions.

🔧 Install Dependencies

pip install streamlit==1.45.1
pip install openai==1.75.0
pip install chromadb==1.0.10
pip install crewai==0.121.0

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🛠️ How to Run

1. Set your OpenAI API key
   Edit adaptive_trainer.py and memory_store.py:

openai.api_key = "your-openai-api-key"

2. Run the app using Streamlit

streamlit run adaptive_trainer.py

3. Open your browser at http://localhost:8501

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📁 Project Structure

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├── adaptive_trainer.py    # Main Streamlit app with agents
├── memory_store.py        # Handles ChromaDB integration
├── chroma_storage/        # (auto-created) Persistent memory DB
└── README.md              # You're here

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📚 Use Case

This project is ideal for:

• Personalized education apps
• Agentic AI research demos
• Adaptive productivity tools
• Use cases needing workflow + memory

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🧠 Memory Flow

• Stores user responses, feedback, and weak areas
• Uses OpenAI embeddings + Chroma to persist memory
• Recalls similar learning sessions to guide next steps

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🧪 Example Topics to Try

• "Photosynthesis"
• "Basics of Python"
• "World War II history"
• "Introduction to Machine Learning"

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📝 License

MIT License. Open for customization and integration into your own projects.