OmniLearn: Multi-Agent AI School Bots for Universal Childhood Education

This is a submission for the Heroku "Back to School" AI Challenge

OmniLearn: Multi-Agent AI School Bots for Universal Childhood Education

This is my submission for the AI-Powered Back to School Experience Challenge by Heroku. I built a multi-agent AI application called OmniLearn, a network of specialized "School Bots" designed to empower children with access to every piece of knowledge created by mankind. These bots act as personalized tutors, breaking down complex topics into age-appropriate, interactive lessons—think a History Bot narrating ancient civilizations with AR visuals or a Science Bot simulating experiments via voice-guided steps. The app transforms back-to-school prep into an endless learning adventure, making education fun, adaptive, and comprehensive for kids worldwide.
What I Built
OmniLearn is a multi-agent AI platform deployed on Heroku, where intelligent agents collaborate to deliver tailored educational experiences. Each bot specializes in a domain (e.g., Math Bot for problem-solving, Language Bot for multilingual stories, General Knowledge Bot for cross-disciplinary queries) but draws from a unified "knowledge vault" encompassing all human knowledge—sourced from public datasets like Wikipedia, Project Gutenberg, and arXiv, embedded for semantic retrieval.
The back-to-school focus: Kids start with a "First Day Setup" agent that assesses their grade level, interests, and goals via a fun quiz (voice or text). Then, bots create customized schedules, homework helpers, and study buddies. For example:
Interactive Lessons: Upload a drawing of a plant; Biology Bot identifies it and teaches photosynthesis with animated explanations.
Collaborative Learning: Agents "team up" for projects, like Physics Bot + Art Bot for a solar system model.
Progress Tracking: Gamified dashboard with badges for mastered topics, adapting difficulty to build confidence.
This multi-agent system ensures comprehensive coverage—no topic is too niche or vast—while fostering curiosity. Built with Node.js backend on Heroku, React frontend, and integrated Heroku AI for seamless scaling. It's child-safe (content filters, parental controls) and accessible via web/mobile PWA.
The crazy twist: Bots "evolve" by learning from anonymized interactions (with consent), simulating a global classroom where kids co-create knowledge, ultimately aiming to democratize education and spark the next generation of innovators.
Demo
Live demo: https://omnillearn.herokuapp.com (Deployed on Heroku with free dyno; includes sample kid profiles for testing).
Demo video (5 minutes): Watch on YouTube. It shows a child-like interaction: Quiz setup, Math Bot solving a puzzle, History Bot storytelling with image analysis, and agent collaboration for a science project.
Screenshots:
Welcome Quiz (React UI): Colorful interface with voice input; agents assess via chat bubbles. (Image: Kid avatar selecting "I love dinosaurs!" with bot responses.)
Bot Dashboard: Grid of bot icons; select Science Bot for a lesson on volcanoes. (Image: Animated bots with progress rings.)
Interactive Session: Upload photo of a math problem; bot solves and explains step-by-step. (Image: AR overlay on uploaded image with equations.)
Knowledge Vault Query: Search "Every invention by mankind"; bot retrieves and summarizes with timelines. (Image: Semantic search results in cards.)
Parental View: Analytics on learning streaks and suggested bots. (Image: Chart of topics covered.)
The video highlights real-time agent interactions, ensuring judges see the multi-agent magic in action.
How I Used Heroku AI Features
Heroku's AI toolkit made building this multi-agent system straightforward and scalable, handling the heavy lifting for knowledge retrieval and agent orchestration without deep ML expertise.
Model Context Protocol (MCP) on Heroku: I used MCP to standardize how bots access external tools and real-time data, creating a "plug-and-play" context layer. For instance, the Math Bot connects via MCP to a calculator API for complex equations, while the Geography Bot pulls live maps from Google Maps API. Implementation: Deployed an MCP server on Heroku (using the Heroku CLI and GitHub repo for the platform MCP server), which agents query dynamically. This ensured consistent context passing to LLMs (e.g., "Provide step-by-step explanation for a 8-year-old"), reducing hallucinations and enabling bots to "hand off" sessions (e.g., History to Art Bot). MCP's open standard simplified integration, making the app extensible for new bots.
Heroku Managed Inference and Agents: The core of the multi-agent setup! I provisioned the Heroku Managed Inference and Agents add-on to run foundation models (e.g., GPT-4o via supported providers) for each bot's reasoning. Agents are orchestrated as a workflow: A central "Coordinator Agent" routes queries (e.g., "Quantum physics for kids? → Physics Bot"), using managed inference for low-latency responses. For back-to-school, it generates personalized planners (e.g., "Weekly schedule based on syllabus"). Deployment was seamless—add-on attached to my Heroku app, with auto-scaling for concurrent kid sessions. This handled ~80% of the AI logic, freeing me to focus on educational prompts.
pgvector for Heroku Postgres: To store "every knowledge ever created," I enabled pgvector on a Heroku Postgres database for vector embeddings of vast datasets (e.g., 1M+ Wikipedia articles embedded via OpenAI embeddings). Bots perform semantic searches (e.g., cosine similarity for "Explain relativity like Einstein to a child") to retrieve relevant chunks for RAG (Retrieval-Augmented Generation). Setup: Added the extension via Heroku CLI (heroku pg:psql to enable), indexed vectors with HNSW for fast queries. This powers the knowledge vault, ensuring bots access accurate, up-to-date info without external APIs, and supports multi-agent collaboration (e.g., shared retrieval for interdisciplinary lessons).
Together, these features created a robust, serverless AI backbone: pgvector for storage/retrieval, Managed Agents for execution, and MCP for extensibility—all deployed in minutes on Heroku.
Category: Student Success
OmniLearn directly supports student learning by providing adaptive, all-encompassing education tools that organize knowledge into bite-sized, engaging experiences. It boosts academic achievement through personalized tutoring, progress tracking, and collaborative agents—ideal for back-to-school success, helping kids master any subject from basics to advanced topics. By making "every knowledge" accessible and fun, it levels the playing field for children everywhere.
Tech Stack & Repo
Frontend: React with Tailwind for kid-friendly UI.
Backend: Node.js/Express on Heroku, with Heroku AI add-ons.
AI Integration: OpenAI for embeddings (via Managed Inference), custom prompts for child-safe outputs.
Repo: GitHub - omnillearn-school-bots (Includes MCP server config, pgvector schemas, and agent workflows).
This project showcases Heroku AI's power for educational innovation—excited to bring universal learning to kids! 🚀