📚 Multi-Agent AI Study Assistant

A powerful, personalized learning platform powered by multiple specialized AI agents. Get custom learning roadmaps, practice quizzes, AI tutoring, and RAG-powered document Q&A to supercharge your studies!

🌟 Features

🎯 Personalized Learning Analysis

• Student Profiling: AI analyzes your knowledge level, goals, and learning style
• Gap Analysis: Identifies what you need to learn and prerequisite knowledge
• Custom Recommendations: Tailored approach based on your unique needs

🗺️ Smart Learning Roadmaps

• Structured Learning Paths: Break down complex topics into manageable phases
• Time-Optimized: Plans adapted to your available study time
• Milestone Tracking: Clear checkpoints to measure progress
• Flexible Scheduling: Adjust pace based on your schedule

📝 Dynamic Quiz Generation

• Adaptive Difficulty: Questions matched to your knowledge level
• Multiple Question Types: MCQ, True/False, Short Answer, Problem-Solving
• Detailed Explanations: Learn from both correct and incorrect answers
• Custom Focus Areas: Target specific topics you want to practice

🤖 AI Tutor

• 24/7 Availability: Get help whenever you need it
• Personalized Explanations: Adapted to your learning style
• Step-by-Step Guidance: Break down complex concepts
• Real-World Examples: Understand through practical applications

📄 RAG-Powered Document Q&A

• Upload Study Materials: PDFs, textbooks, lecture notes
• Intelligent Search: Find relevant information instantly
• Context-Aware Answers: Get answers grounded in your materials
• Source Citations: Know where information comes from

🔍 Resource Finder

• Curated Resources: AI finds the best learning materials
• Quality Filtered: Only high-quality, relevant resources
• Learning Style Matched: Resources suited to how you learn best

🏗️ Architecture

This project uses a multi-agent architecture where specialized AI agents collaborate:

study_agents.py          # Specialized AI agents (Analyzer, roadmap Creator, Quiz Generator, Tutor, Resource Finder, RAG Tutor)
agent_handler.py         # Orchestrates agent workflows and manages state
rag_helper.py            # RAG functionality for document-based learning
config.py                # Configuration management
prompts.yaml             # Agent personas and prompt templates
app.py                   # Streamlit web interface

Agent Roles

1. Student Analyzer Agent: Assesses learning needs, identifies gaps, recommends approaches
2. Roadmap Creator Agent: Designs personalized learning paths with phases and milestones
3. Quiz Generator Agent: Creates adaptive assessments with explanations
4. Tutor Agent: Provides explanations and answers questions
5. Resource Finder Agent: Searches and recommends learning materials
6. RAG Tutor Agent: Answers questions using uploaded study documents

🛠️ Technologies Used

• Phidata: Multi-agent orchestration framework
• Streamlit: Web interface
• LangChain: Document processing and RAG
• ChromaDB: Vector database for RAG
• OpenAI/Groq: LLM providers
• DuckDuckGo: Web search for resource finding

📝 Tips for Best Results

1. Be Specific: The more detailed your learning goals, the better the roadmap
2. Honest Assessment: Accurately rate your knowledge level for best recommendations
3. Upload Materials: Use the RAG feature with your textbooks and notes for personalized help
4. Regular Quizzes: Test yourself frequently to reinforce learning
5. Follow the Roadmap: Trust the AI's structured approach
6. Ask Questions: Use the tutor liberally - there are no dumb questions!

🎨 Learning Styles Explained

The AI adapts to your preferred learning style to maximize effectiveness. Choose the one that best describes how you learn:

👁️ Visual Learners

How you learn best: Through seeing and observing

Characteristics:

• Prefer diagrams, charts, and images
• Remember faces better than names
• Like color-coded notes and mind maps
• Benefit from watching demonstrations

Recommended resources:

• 📊 Infographics and flowcharts
• 🎥 Video tutorials and demonstrations
• 🗺️ Mind maps and concept diagrams
• 📈 Visual data representations
• 🎨 Color-coded study materials

Study tips:

• Use highlighters and color coding
• Draw diagrams to understand concepts
• Watch video explanations
• Create visual summaries

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🎧 Auditory Learners

How you learn best: Through listening and speaking

Characteristics:

• Prefer verbal instructions
• Remember what you hear
• Enjoy discussions and lectures
• Often talk through problems

Recommended resources:

• 🎙️ Podcasts and audio lectures
• 💬 Discussion forums and study groups
• 🗣️ Verbal explanations and debates
• 🎵 Educational songs or mnemonics
• 📻 Audio books and recordings

Study tips:

• Read notes aloud
• Record and listen to lectures
• Discuss topics with others
• Use verbal repetition
• Explain concepts out loud

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🤸 Kinesthetic Learners

How you learn best: Through doing and hands-on experience

Characteristics:

• Learn by doing and practicing
• Prefer physical activity and movement
• Like real-world applications
• Need to try things yourself

Recommended resources:

• 💻 Interactive coding platforms (for programming)
• 🔬 Lab experiments and simulations
• 🎮 Educational games and interactive tools
• 🛠️ Hands-on projects and exercises
• 🏃 Physical models and manipulatives

Study tips:

• Build projects while learning
• Use interactive simulations
• Take frequent breaks to move
• Practice with real examples
• Create physical models or demonstrations

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📖 Reading/Writing Learners

How you learn best: Through reading and writing

Characteristics:

• Prefer written information
• Love taking detailed notes
• Enjoy reading textbooks and articles
• Learn by writing summaries

Recommended resources:

• 📚 Textbooks and comprehensive guides
• 📝 Written tutorials and documentation
• ✍️ Note-taking and summary exercises
• 📄 Articles and research papers
• 📋 Lists, definitions, and glossaries

Study tips:

• Take extensive notes
• Rewrite information in your own words
• Create written summaries
• Use flashcards with written content
• Read and re-read materials

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💡 How the AI Uses Your Learning Style

When you select your learning style, the AI agents will:

1. Roadmap Creator: Structures your learning path with style-appropriate milestones
2. Resource Finder: Prioritizes resources matching your preferred format
3. Tutor Agent: Adapts explanations using style-specific techniques
4. Quiz Generator: Includes questions that align with your learning preferences

Not sure which style fits you? Try the Mixed/Multimodal approach, which combines elements from all styles!

🐛 Troubleshooting

ModuleNotFoundError: No module named 'X'

Solution: Install missing dependencies

pip install -r requirements.txt

Common missing modules and their fixes:

• phidata → pip install phidata
• openai → pip install openai
• chromadb → pip install chromadb
• langchain_chroma → pip install langchain-chroma
• langchain_openai → pip install langchain-openai
• duckduckgo_search → pip install duckduckgo-search

"streamlit: command not found"

Solution: Use Python module syntax

python3 -m streamlit run app.py

Or add ~/.local/bin to your PATH:

export PATH="$HOME/.local/bin:$PATH"

"No API key found" or API errors

Solution:

1. Ensure .env file exists in the project root
2. Verify your API key is correctly formatted:
   • Groq keys start with gsk_
   • OpenAI keys start with sk-
3. Check that the key is valid and active
4. For OpenAI: Ensure you have credits in your account

"Failed to load document" (RAG feature)

Solution:

• Ensure PDF is not password-protected
• Check file is not corrupted
• Try converting to text format first
• Verify file size is reasonable (< 50MB recommended)

"Agent not responding" or timeout errors

Solution:

• Check your internet connection
• Verify API key is valid and active
• Try switching to a different model in the app settings
• For Groq: Check status.groq.com
• For OpenAI: Check status.openai.com

ChromaDB errors

Solution:

# Delete the vector database and restart
rm -rf chroma_db/
python3 -m streamlit run app.py

Ensure you have write permissions:

chmod -R 755 .

Port already in use (Address already in use)

Solution:

# Kill existing Streamlit processes
pkill -f streamlit

# Or use a different port
streamlit run app.py --server.port 8502

🚀 Future Enhancements

• Progress tracking and analytics
• Spaced repetition scheduling
• Collaborative study groups
• Integration with learning platforms (Coursera, Udemy, etc.)
• Mobile app version
• Voice interaction for auditory learners
• Gamification and achievements
• Export to Anki flashcards

📄 License

This project is open source and available for educational purposes.

🙏 Acknowledgments

• Built with Phidata multi-agent framework
• Powered by Groq and OpenAI LLMs
• Uses LangChain for document processing and RAG functionality

💬 Support

For issues, questions, or suggestions:

1. Check the troubleshooting section above
2. Review the configuration in prompts.yaml
3. Ensure all dependencies are correctly installed

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Happy Learning! 📚✨

Remember: The best way to learn is to start. Let AI agents guide your journey!