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Learn through adaptive quizzes.\nCome back tomorrow — the system still knows where you left off.\n\n[![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/downloads/)\n[![CrewAI](https://img.shields.io/badge/CrewAI-1.14-orange.svg)](https://crewai.com)\n[![xysq](https://img.shields.io/badge/xysq-memory-00b89a.svg)](https://xysq.ai)\n[![Bedrock](https://img.shields.io/badge/Amazon-Bedrock-yellow.svg)](https://aws.amazon.com/bedrock/)\n[![Gemini](https://img.shields.io/badge/Google-Gemini-4285F4.svg)](https://ai.google.dev/)\n[![OpenAI](https://img.shields.io/badge/OpenAI-GPT--4-412991.svg)](https://openai.com/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)\n\n</div>\n\n## 🚀 Live Demo\n\n**Frontend:**\n🔗 https://adaptive-learning-with-crewai-xysq.vercel.app/\n\n<br>\n\n## See it in action\n\n<!-- 🎬 Hero Demo Video Placeholder -->\n<!-- Replace this image with a high-quality GIF showing:\n     1. Uploading \"Attention Is All You Need\" paper\n     2. Selecting topic, difficulty, and question count\n     3. The learning session starting seamlessly\n-->\n<div align=\"center\">\n  <img src=\"assets/learning_material_quiz.gif\" alt=\"Demo: Upload to Learn Flow\" width=\"100%\" style=\"border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);\">\n  <p><em>A 60-second walkthrough showing how uploaded research papers become part of a persistent, adaptive learning workspace.</em></p>\n</div>\n\n<br>\n\nYour uploaded material doesn't disappear after the session.\nIt becomes part of every future learning interaction — surfacing in lessons, shaping quizzes, and informing progress reports.\n\n<br>\n\n---\n\n<br>\n\n## The problem\n\nMost AI learning tools have amnesia.\n\nYou upload notes. You answer questions. You close the tab.\nNext time? The AI has no idea you were ever there.\n\nEvery session starts from zero.\n\n<br>\n\n## Why continuity matters\n\nReal learning is cumulative.\n\nA tutor who remembers that you struggled with attention mechanisms last week\nwill teach differently today. That's the difference between a chatbot and a learning system.\n\nThis project gives AI agents **persistent memory** — powered by [xysq](https://xysq.ai).\n\n- Upload a research paper on Monday. The AI still references it on Friday.\n- Score 2/5 on self-attention. Next session targets exactly those gaps.\n- Kill the process. Restart the server. The memory survives.\n\nNo database to manage. No conversation logs to replay.\nThe AI simply remembers.\n\n<br>\n\n---\n\n<br>\n\n## What it does\n\n| | |\n|---|---|\n| 🎯 **Adaptive quizzes** | Difficulty adjusts based on how you've performed before |\n| 📚 **Persistent knowledge** | Uploaded PDFs, notes, and papers become permanent learning material |\n| 🧠 **Cross-session memory** | Quiz scores, weak areas, and understanding gaps survive restarts |\n| 📊 **Progress reports** | Detailed analysis with trend tracking and next-step recommendations |\n| 🔄 **Evolving difficulty** | The system suggests when you're ready to move up |\n| 📄 **Document understanding** | Uploaded content is extracted, indexed, and referenced in future sessions |\n\n<br>\n\n---\n\n<br>\n\n## Adaptive learning in practice\n\n<!-- 🎬 Learning + Quiz Flow Demo Video Placeholder -->\n<!-- Replace this image with a high-quality GIF showing:\n     1. Lesson generation and adaptive quiz taking\n     2. Real-time feedback and evaluation\n     3. Improvement suggestions\n-->\n<div align=\"center\">\n  <img src=\"assets/final_result.gif\" alt=\"Demo: Adaptive Quiz Flow\" width=\"100%\" style=\"border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);\">\n  <p><em>A walkthrough of a full learning session showing adaptive quiz generation, interactive answers, and AI-driven performance evaluation.</em></p>\n</div>\n\nHere's what a typical session looks like:\n\n**1.** You pick a topic — say, the Transformer architecture from a paper you uploaded earlier.\n\n**2.** The AI recalls what you know. If you've studied this before, it remembers where you struggled.\n\n**3.** A lesson is generated — adapted to your level and your gaps.\n\n**4.** You take a quiz. The questions aren't random — they probe the areas where you're weakest.\n\n**5.** After submitting, the AI evaluates every answer. Not just right or wrong — it explains *why*, identifies conceptual gaps, and suggests what to focus on next.\n\n**6.** Everything is stored. Next time you revisit this topic, the system picks up exactly where you left off.\n\n<br>\n\n---\n\n<br>\n\n## Memory that outlasts the session\n\n<!-- 🎬 xysq Memory Continuity Demo Video Placeholder -->\n<!-- Replace this image with a high-quality GIF showing:\n     1. xysq vault storing the session outcome\n     2. Starting a NEW session later\n     3. The \"Prior Learning Recalled\" card appearing automatically\n-->\n<div align=\"center\">\n  <img src=\"assets/xysq_vault_demo.gif\" alt=\"Demo: Persistent Memory\" width=\"100%\" style=\"border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);\">\n  <p><em>Showing how xysq stores learning history and session outcomes in a persistent vault that future sessions draw from automatically.</em></p>\n</div>\n\nThis is the core of the system.\n\nWhen you finish a learning session, the AI doesn't just show you a score.\nIt stores structured learning data — what you got wrong, which concepts you're improving on, what difficulty you're ready for.\n\nWhen you come back — hours, days, or weeks later — that data is recalled automatically.\n\n```\nSession 1 (Monday)             Session 2 (Thursday)\n──────────────────             ────────────────────\nUpload: attention paper        AI recalls: \"struggled with\nScore: 2/5 on self-attention     multi-head attention\"\nGaps stored → xysq             Quiz targets those exact gaps\n                               Score: 4/5\n                               Progress stored → xysq\n```\n\nNo shared runtime between sessions. No conversation replay.\nThe memory layer operates independently of the application lifecycle.\n\n**Kill the process. Redeploy. Crash. Come back.**\nThe learner profile persists.\n\n<br>\n\n---\n\n<br>\n\n## Architecture\n\n```\n┌─────────────────────────────────────────────┐\n│           React / Vite Frontend             │\n│    Topic · Difficulty · Quiz · Progress     │\n└──────────────────┬──────────────────────────┘\n                   │\n        ┌──────────┴────────────┐\n        │  FastAPI Backend API  │\n        └──────────┬────────────┘\n                   │\n        ┌──────────┴────────────┐\n        │     CrewAI Agents     │\n        │ Tutor · Quiz · Analyst│\n        └──────────┬────────────┘  \n                   │\n    ┌──────────────┼────────────┐\n    │              │            │\n┌───┴───┐   ┌──────┴─────┐  ┌───┴─────┐\n│ xysq  │   │  xysq      │  │ AI      │\n│Memory │   │ Organise   │  │Provider │\n│       │   │            │  │         │\n│capture│   │  upload    │  │         │\n│surface│   │  extract   │  │         │\n└───────┘   └────────────┘  └─────────┘\n```\n\nThree AI agents collaborate in sequence:\n\n| Agent | Role |\n|---|---|\n| 🎓 **Tutor** | Teaches the topic, adapting depth based on known gaps |\n| 🧪 **Quiz Master** | Generates quizzes that target weak areas, evaluates answers |\n| 📊 **Progress Analyst** | Analyzes trends, writes progress reports, suggests next steps |\n\nMemory and document storage are handled by [xysq](https://xysq.ai) — fully managed, no infrastructure to maintain.\n\n<br>\n\n---\n\n<br>\n\n## Quickstart\n\n### Prerequisites\n\n- Python 3.11+\n- Node.js 18+ and npm\n- [uv](https://docs.astral.sh/uv/) package manager\n- [xysq API key](https://app.xysq.ai/connect)\n- An LLM provider: Google Gemini, OpenAI, or AWS Bedrock\n\n### Local Development\n\n**1. Clone and install backend:**\n```bash\ngit clone https://github.com/<your-org>/xysq_crewai.git\ncd xysq_crewai\ncp .env.example .env   # fill in your keys\nuv sync\n```\n\n**2. Start the backend API:**\n```bash\nuv run uvicorn api_server:app --reload --port 8000\n```\n\n**3. Start the frontend (new terminal):**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\nThe app opens at `http://localhost:5173`. The Vite dev proxy forwards all `/api` calls to `localhost:8000` automatically.\n\nOn your **first launch**, the app will present a **Configuration Setup** screen where you can:\n1. Provide your `XYSQ_API_KEY` ([get it here](https://app.xysq.ai/connect)).\n2. Select your AI Provider (**AWS Bedrock**, **Google Gemini**, or **OpenAI**) and enter the required API keys.\n\n<br>\n\n---\n\n<br>\n\n## Deployment\n\n### Backend → Railway\n\n1. Push your code to GitHub.\n2. Create a new [Railway](https://railway.app) project and connect your GitHub repo.\n3. Railway will auto-detect `railway.toml` and use it as the build/start config.\n4. Set the following **Environment Variables** in the Railway dashboard:\n\n| Variable | Description |\n|---|---|\n| `XYSQ_API_KEY` | Your xysq API key |\n| `PROVIDER` | `Google Gemini`, `OpenAI`, or `AWS Bedrock` |\n| `MODEL` | e.g. `gemini/gemini-2.0-flash` or `gpt-4o` |\n| `API_KEY` | Your Gemini or OpenAI API key |\n| `AWS_ACCESS_KEY_ID` | *(Bedrock only)* |\n| `AWS_SECRET_ACCESS_KEY` | *(Bedrock only)* |\n| `AWS_DEFAULT_REGION` | *(Bedrock only)* |\n| `ALLOWED_ORIGINS` | Your Vercel frontend URL (add after Vercel deploy) |\n\n5. Deploy — Railway will expose your API at `https://<your-app>.up.railway.app`.\n\n### Frontend → Vercel\n\n1. Go to [Vercel](https://vercel.com) and create a new project.\n2. Import the same GitHub repo and set the **Root Directory** to `frontend`.\n3. Vercel auto-detects Vite — no extra build settings needed (`vercel.json` is already included).\n4. Set this **Environment Variable** in the Vercel dashboard:\n\n| Variable | Value |\n|---|---|\n| `VITE_API_URL` | `https://<your-railway-app>.up.railway.app/api` |\n\n5. Deploy — Vercel will expose your frontend at `https://<your-app>.vercel.app`.\n\n### Wire CORS (final step)\n\nAfter both are deployed, go back to Railway and add your Vercel URL to `ALLOWED_ORIGINS`:\n\n```\nALLOWED_ORIGINS=https://your-app.vercel.app\n```\n\nRailway will redeploy automatically. Your frontend and backend are now fully connected.\n\n<br>\n\n---\n\n<br>\n\n## Tech stack\n\n| Component | Technology |\n|---|---|\n| **Memory** | [xysq](https://xysq.ai) — persistent agent memory |\n| **Agents** | [CrewAI](https://crewai.com) — role-based multi-agent orchestration |\n| **LLM** | Amazon Bedrock, Google Gemini, or OpenAI |\n| **UI** | [React](https://react.dev) + [Vite](https://vitejs.dev/) — interactive web frontend |\n| **API** | [FastAPI](https://fastapi.tiangolo.com) — backend REST API |\n| **Tooling** | [uv](https://docs.astral.sh/uv/) — fast Python package management |\n\n<br>\n\n---\n\n<br>\n\n## License\n\nMIT\n\n<br>\n\n---\n\n<div align=\"center\">\n\n<br>\n\nBuilt with [xysq](https://xysq.ai) · [CrewAI](https://crewai.com) · [React](https://react.dev) + [FastAPI](https://fastapi.tiangolo.com)\n\n<br>\n\n**The session ends. The learning never does.**\n\n<br>\n\n</div>\n","readmeExcerpt":"<div align=\"center\"> 🧠 Adaptive Learning Companion **The AI remembers how you learn.** Upload your study material. Learn through adaptive quizzes. 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