EduPilot
A multi-agent AI tutor that delivers personalized lessons, resolves doubts with RAG, generates quizzes, and tracks progress, all accessible via MCP for Claude Desktop.
README
<div align="center">
🚀 EduPilot: Next-Gen AI Tutor Ecosystem
A Production-Ready, Multi-Agent Orchestration Engine built on the Model Context Protocol (MCP)
Delivering hyper-personalized, adaptive, and interactive learning experiences at scale.
</div>
🌎 The Vision
Traditional education scales poorly. Static curricula fail to adapt, and single-prompt LLMs lack the memory, pedagogical structure, and safety required for true learning.
EduPilot solves this by leveraging a decentralized Multi-Agent architecture. Instead of relying on a single omniscient LLM, we use LangGraph to act as a routing supervisor state-machine. It dynamically intercepts natural language intents and routes tasks to highly specialized, goal-oriented CrewAI expert agents. The result? A fully autonomous digital tutor that maintains permanent state, consults real textbooks via Vector RAG, and serves everything seamlessly over the newly minted Model Context Protocol (MCP).
⚡ The Tech Stack
We don't do monolithic architectures here. This is a modular, event-driven orchestration stack:
- Orchestration Layer:
LangGraph(Supervisor State Machine) - Agent Intelligence:
CrewAI&LangChain(Lesson Planner, Doubt Resolver, Quiz Generator) - Knowledge Retrieval & RAG:
ChromaDB(Local Embeddings for ultra-low latency contextual retrieval) - State & Memory Persistence:
SQLite(Native Offline Storage for longitudinal mastery mapping) - API & Extensibility:
FastAPI(REST endpoints) +FastMCP(Claude Desktop Integration) - Frontend App: Zero-dependency Vanilla HTML/CSS/JS (Glassmorphism Dark Mode)
🧠 The Agent Force
| Microservice Agent | Primary Responsibility | Associated Capabilities |
|---|---|---|
| 👑 The Orchestrator | Traffic Controller: Ingests the task limitlessly, classifies the semantic intent, fetches SQLite mastery memory, and routes to the correct Crew. | Routing, State Augmentation, Guardrails |
| 🧑🏫 Lesson Personalizer | Dynamic Curriculum: Composes 5E-Model tailored lesson plans dynamically adjusted for the student's exact learning style (Visual, Auditory, Kinesthetic) and age. | Bloom's Taxonomy Scaling, Adaptive Difficulty |
| 🛡️ Doubt Resolver | RAG Explainer: Triggers a Vector DB retrieval across curriculum textbooks to answer questions accurately without hallucinating non-school-board facts. | ChromaDB, Pinecone, Misconception Bridging |
| 📝 Quiz Generator | Assessment: Generates precise, misconception-targeted distractors for MCQs. | Formative Assessment |
| 📊 Progress Tracker | Memory Core: Parses session outputs and permanently updates student mastery levels (0-100) inside the persistent SQLite memory layer. | Spaced Repetition Data, Database Hydration |
📦 Quick Start Installation
Get up and running in your local dev environment in under 60 seconds.
1. Clone & Set up the Virtual Environment
python -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\Activate.ps1
2. Hydrate Dependencies
pip install -r requirements.txt
3. Inject Environment Keys
Copy .env.example to .env and configure your foundation model provider (Anthropic is recommended for reasoning, OpenAI for generation):
ANTHROPIC_API_KEY="sk-ant-..."
OPENAI_API_KEY="sk-proj-..."
🎯 Running The Platform
EduPilot is designed to run anywhere. Choose your preferred interaction method:
Method A: The Interactive Web App (Recommended)
Boot the REST backend:
uvicorn main:app --reload --host 0.0.0.0 --port 8000
Then simply double-click frontend/index.html on your local machine to launch our completely decoupled, zero-build-step, ultra-premium chat interface.
Method B: Native MCP Integration (Claude Desktop)
Because EduPilot is an official MCP Server, you can pipe the agent orchestrator directly into Claude!
Simply modify your claude_desktop_config.json file:
{
"mcpServers": {
"edu-pilot-agent": {
"command": "C:/path/to/venv/Scripts/python.exe",
"args": ["C:/path/to/mcp_server.py"]
}
}
}
Note: Make sure to point to the absolute path of the python.exe inside your virtual environment so dependencies resolve properly!
Method C: The MCP Inspector
Want to hit the bare-metal MCP tools locally?
npx @modelcontextprotocol/inspector .\venv\Scripts\python.exe mcp_server.py
Built with ❤️ for the future of AGI-driven education.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.