EduPilot

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.

Category
访问服务器

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.

Python 3.10+ LangGraph CrewAI FastMCP

</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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选