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Overview\n\nThis project implements a **Multi-Agent AI Study Planner System** that automatically generates a structured learning plan for a given subject.\n\nThe system uses **multiple AI agents** that collaborate to:\n\n- Break down a subject into learning topics\n- Organize topics logically\n- Find learning resources\n- Generate a study schedule\n\nThis project is developed for **SE4010 – CTSE Assignment 2**.\n\n---\n\n## 🧠 Multi-Agent Architecture\n\nThe system consists of 4 agents:\n\n### 🧠 Planner Agent\n- Breaks subject into learning topics\n\n### 🧩 Content Structurer Agent\n- Organizes topics from beginner to advanced\n\n### 🔎 Resource Finder Agent\n- Finds learning resources\n\n### 📅 Scheduler Agent\n- Creates study schedule\n\n---\n\n## 🔁 Workflow\n\nUser Input\n↓\nPlanner Agent\n↓\nStructurer Agent\n↓\nResource Finder Agent\n↓\nScheduler Agent\n↓\nFinal Study Plan\n\n\n---\n\n## 🛠 Tools Used\n\nEach agent uses custom Python tools:\n\n- `load_topics()`\n- `organize_topics()`\n- `find_resources()`\n- `create_schedule()`\n- `save_plan()`\n\n---\n\n## 🧠 Global State Management\n\nThe system uses a shared global state:\n\n```python\nstate = {\n    \"goal\": \"\",\n    \"topics\": [],\n    \"structured_topics\": [],\n    \"resources\": {},\n    \"schedule\": \"\"\n}\n```\n\n## ⚙️ Tech Stack\nPython\nCrewAI\nOllama (Local LLM)\nLlama3 / Phi3\nLangChain\n\n## 📁 Project Structure\n\n        AI-Study-Planner/\n        │\n        ├── agents/\n        │   ├── planner_agent.py\n        │   ├── structurer_agent.py\n        │   ├── resource_agent.py\n        │   └── scheduler_agent.py\n        │\n        ├── tools/\n        │   ├── planner_tool.py\n        │   ├── structurer_tool.py\n        │   ├── resource_tool.py\n        │   └── scheduler_tool.py\n        │\n        ├── tests/\n        │\n        ├── main.py\n        ├── state.py\n        ├── requirements.txt\n        └── README.md\n\n## 🚀 Installation\n1. 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