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The system adapts to each student's learning level, provides comprehensive explanations, generates quizzes, evaluates performance, and automatically re-teaches weak concepts.\n\n### Key Features\n\n- **Multi-Agent Architecture**: 4 specialized AI agents working together\n- **Adaptive Learning**: Content adapts to Beginner/Intermediate/Advanced levels\n- **Intelligent Assessment**: Mix of MCQ and short answer questions\n- **Feedback Loop**: Automatic re-teaching for scores below 70%\n- **Persistent Memory**: Tracks student progress across sessions\n- **Beautiful UI**: Modern Streamlit interface with progress tracking\n- **Error Handling**: Robust handling of edge cases and failures\n\n## 🤖 Agent Roles\n\n### 1. Coordinator Agent\n- Validates student requests and topics\n- Manages learning context and workflow\n- Delegates tasks to appropriate agents\n- Handles re-teaching decisions\n\n### 2. Explainer Agent\n- Teaches topics with adaptive explanations\n- Uses examples, analogies, and bullet points\n- Adapts content to student's learning level\n- Re-teaches weak concepts when needed\n\n### 3. Quiz Master Agent\n- Generates 5 assessment questions\n- Creates mix of MCQ and short answer\n- Returns structured JSON output\n- Adapts difficulty to student level\n\n### 4. Evaluator Agent\n- Compares student answers to correct answers\n- Scores with partial credit support\n- Provides detailed feedback\n- Identifies weak concepts\n\n## 🏗️ Architecture\n\n```\nStudent\n    │\n    ▼\nCoordinator\n    │\n    ▼\nExplainer\n    │\nExplanation\n    │\n    ▼\nQuiz Master\n    │\nQuestions\n    │\nStudent Answers\n    │\n    ▼\nEvaluator\n    │\nFeedback\n    │\nWeak?\n ┌──Yes──────────┐\n ▼               │\nExplainer ◄──────┘\n```\n\n## 🔄 Orchestration Pattern\n\nThe project uses **CrewAI's sequential process** for agent orchestration:\n\n1. **Sequential Flow**: Agents execute in defined order\n2. **Task Delegation**: Coordinator delegates to specialist agents\n3. **Context Passing**: Each agent receives output from previous agents\n4. **Feedback Loop**: Low scores trigger re-teaching workflow\n\n## 💾 Memory System\n\nLeo AI Tutor maintains persistent memory in JSON format:\n\n```json\n{\n  \"students\": {\n    \"student_name\": {\n      \"name\": \"Student Name\",\n      \"learning_level\": \"Beginner\",\n      \"sessions\": [...],\n      \"quiz_history\": [...],\n      \"weak_topics\": [\"topic1\", \"topic2\"],\n      \"total_sessions\": 5,\n      \"average_score\": 75.5\n    }\n  }\n}\n```\n\n### Memory Features:\n- Tracks student name and learning level\n- Stores complete quiz history\n- Identifies and remembers weak topics\n- Calculates average performance\n- Suggests level progression\n\n## 🚀 Installation\n\n### Prerequisites\n- Python 3.11 or higher\n- OpenAI API key\n\n### Steps\n\n1. **Clone the repository**\n```bash\ngit clone <repository-url>\ncd leo-ai-tutor\n```\n\n2. **Create virtual environment**\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n```\n\n3. **Install dependencies**\n```bash\npip install -r requirements.txt\n```\n\n4. **Configure environment**\n```bash\ncp .env.example .env\n# Edit .env and add your OpenAI API key\n```\n\n5. **Run the application**\n```bash\nstreamlit run app.py\n```\n\n## 📁 Project Structure\n\n```\nleo-ai-tutor/\n│\n├── app.py                      # Main Streamlit application\n├── requirements.txt            # Python dependencies\n├── README.md                   # Project documentation\n├── .env.example                # Environment variables template\n│\n├── agents/                     # Agent definitions\n│   ├── __init__.py\n│   ├── coordinator.py          # Coordinator Agent\n│   ├── explainer.py            # Explainer Agent\n│   ├── quizmaster.py           # Quiz Master Agent\n│   └── evaluator.py            # Evaluator Agent\n│\n├── prompts/                    # Prompt templates\n│   ├── coordinator.txt         # Coordinator prompts\n│   ├── explainer.txt           # Explainer prompts\n│   ├── quizmaster.txt          # Quiz Master prompts\n│   └── evaluator.txt           # Evaluator prompts\n│\n├── memory/                     # Persistent storage\n│   └── student_memory.json     # Student data storage\n│\n├── crew/                       # CrewAI orchestration\n│   ├── __init__.py\n│   ├── crew.py                 # Crew configuration\n│   └── tasks.py                # Task definitions\n│\n├── utils/                      # Utility functions\n│   ├── __init__.py\n│   ├── memory.py               # Memory management\n│   └── helpers.py              # Helper functions\n│\n└── assets/                     # Static assets\n```\n\n## 🎯 How to Use\n\n1. **Enter your name** in the student name field\n2. **Select your learning level** (Beginner/Intermediate/Advanced)\n3. **Enter a topic** you want to learn (e.g., \"Operating System Process Scheduling\")\n4. **Click \"Start Learning\"** to begin\n5. **Read the explanation** provided by the Explainer Agent\n6. **Take the quiz** with 5 questions\n7. **Submit your answers** for evaluation\n8. **Review your results** and feedback\n9. **If needed**, the system will automatically re-teach weak concepts\n\n## 📊 Example Topics\n\n- Operating System Process Scheduling\n- Machine Learning Neural Networks\n- Database Normalization\n- Object-Oriented Programming Concepts\n- Computer Networking TCP/IP\n- Data Structures and Algorithms\n\n## 🖼️ Screenshots\n\n![Input Form](assets/screenshot_input.png)\n![Learning Progress](assets/screenshot_progress.png)\n![Quiz Interface](assets/screenshot_quiz.png)\n![Results Display](assets/screenshot_results.png)\n\n## 🔮 Future Improvements\n\n- **Support for Multiple LLMs**: Add support for Claude, Gemini, etc.\n- **Visual Learning**: Integrate diagram and chart generation\n- **Spaced Repetition**: Implement spaced repetition algorithms\n- **Collaborative Learning**: Add group study features\n- **Progress Analytics**: Detailed performance dashboards\n- **Mobile Optimization**: Responsive design for mobile devices\n- **Voice Interaction**: Add speech-to-text capabilities\n- **Resource Links**: Provide external learning resources\n- **Certification**: Generate completion certificates\n- **API Access**: RESTful API for external integrations\n\n## 🛠️ Technical Details\n\n### Dependencies\n- **CrewAI**: Multi-agent orchestration framework\n- **LangChain**: LLM application framework\n- **Streamlit**: Web application framework\n- **OpenAI**: GPT-4 language model\n\n### Code Quality\n- Type hints throughout\n- Comprehensive docstrings\n- Logging at all levels\n- PEP8 compliant\n- Functions under 50 lines\n- Clean architecture patterns\n\n## 📄 License\n\nThis project is for educational purposes.\n\n## 👥 Contributors\n\nCreated as a university assignment demonstrating multi-agent AI systems.\n\n---\n\n**Note**: This project requires an OpenAI API key. 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