DeepTutor
An AI learning system with multiple agents — problem solving, question generation, deep research, and guided interactive learning.
Overview
DeepTutor is a multi-agent AI system designed to help people learn things more effectively. It has agents for different tasks — one that solves problems step by step, one that generates practice questions, one that does deep research on topics, and one that guides you through material interactively like a tutor would.
Challenge
Getting multiple AI agents to work together coherently was the main challenge. Each agent needs enough context to be useful, but you can't just dump everything into the prompt. The orchestration between agents had to feel natural to the user.
Approach
Built the backend in Python with FastAPI, using a RAG pipeline for grounding the agents in actual content. The frontend is Next.js. I spent a lot of time on the agent orchestration layer — figuring out when to hand off between agents and how to maintain context across the conversation.
Outcome
The guided learning agent turned out to be the most useful part. It adapts to how you're responding — if you're getting things right, it moves faster; if you're struggling, it breaks things down differently. It's been a great learning project in itself about how to build multi-agent systems.