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CrewAI)\n\n![Python](https://img.shields.io/badge/Python-3.10+-blue) ![CrewAI](https://img.shields.io/badge/CrewAI-Multi--Agent-red) ![Process](https://img.shields.io/badge/Process-Sequential-orange) ![Status](https://img.shields.io/badge/Status-Active-success) ![License](https://img.shields.io/badge/License-MIT-lightgrey)\n\n---\n\n## 📌 Overview\n\n**AI Research Assistant** is a **sequential multi-agent system** built using **CrewAI** that transforms a **raw learning topic** into a **structured, beginner-to-advanced learning roadmap**.\n\nInstead of relying on a single LLM prompt, this project uses **multiple specialized agents**, each responsible for one cognitive step—just like a human expert team.\n\n---\n\n## 🎯 What Problem Does This Solve?\n\nWhen learning a new topic, beginners often face:\n\n* Unclear learning order\n* Missing fundamentals\n* Overwhelming resources\n* Shallow explanations\n\nThis system solves that by:\n\n* Decomposing the topic\n* Generating deep guiding questions\n* Structuring a logical learning path\n* Explaining everything in human-friendly language\n\n---\n\n## 🧠 Core Idea (Mental Model)\n\nThink of this system like a **learning factory**:\n\n```\nRaw Topic\n   ↓\nTopic Decomposer\n   ↓\nQuestion Generator\n   ↓\nLearning Path Planner\n   ↓\nHuman-Friendly Explainer\n   ↓\nStructured Learning Guide\n```\n\nEach agent:\n\n* Has **one clear role**\n* Depends on **previous agent output**\n* Cannot skip or overlap responsibilities\n\n---\n\n## 🏗️ Architecture Overview\n\n### 🔹 Agents Used\n\n| Agent Name            | Responsibility                           |\n| --------------------- | ---------------------------------------- |\n| Topic Decomposer      | Breaks topic into logical subtopics      |\n| Question Generator    | Creates deep questions per subtopic      |\n| Learning Path Planner | Orders learning from beginner → advanced |\n| Human Explainer       | Converts plan into readable guidance     |\n\n---\n\n## 🔄 Sequential Agent Flow (Mermaid Diagram)\n\n```mermaid\nflowchart TD\n    A[User Input Topic] --> B[Topic Decomposer Agent]\n    B --> C[Question Generator Agent]\n    C --> D[Learning Path Planner Agent]\n    D --> E[Human-Friendly Explainer Agent]\n    E --> F[Final Learning Roadmap Output]\n```\n\n---\n\n## ⚙️ How the System Works (Step-by-Step)\n\n### Step 1: Input\n\nYou provide a **single topic**, for example:\n\n```\n\"Polars for Data Science\"\n```\n\n---\n\n### Step 2: Topic Decomposition\n\nThe system:\n\n* Identifies prerequisites\n* Breaks the topic into atomic subtopics\n* Ensures no conceptual gaps\n\nExample output:\n\n* Basics of Polars\n* Expressions\n* Lazy vs Eager Execution\n* GroupBy & Aggregations\n* Joins\n* Performance Optimization\n\n---\n\n### Step 3: Question Generation\n\nFor each subtopic, the system generates:\n\n* Beginner questions (conceptual)\n* Intermediate questions (practical reasoning)\n* Advanced questions (optimization & edge cases)\n\n---\n\n### Step 4: Learning Path Planning\n\nSubtopics are grouped into **stages**, such as:\n\n* Foundations\n* Core Operations\n* Advanced Optimization\n\nEach stage includes:\n\n* Concepts covered\n* Key questions\n* Expected learning outcome\n\n---\n\n### Step 5: Human-Friendly Explanation\n\nThe final agent:\n\n* Explains **why** this order matters\n* Sets learning expectations\n* Reduces beginner intimidation\n\nThe final output is saved as:\n\n```\nstudy-plans/planning.md\n```\n\n---\n\n## 🧪 Example Input & Output\n\n### 🔹 Input\n\n```text\nTopic: \"SQL Joins\"\n```\n\n### 🔹 Output (Summary)\n\n```text\nStage 1: Relational Thinking\nStage 2: Inner & Outer Joins\nStage 3: Complex Join Conditions\nStage 4: Performance Considerations\n```\n\n---\n\n## 🚀 How to Run the Project\n\n### 1️⃣ Clone the Repository\n\n```bash\ngit clone https://github.com/Rudra-G-23/ai-research-assistant.git\ncd ai-research-assistant\n```\n\n---\n\n### 2️⃣ Create Virtual Environment\n\n```bash\npython -m venv .venv\nsource .venv/bin/activate  # Windows: .venv\\Scripts\\activate\n```\n\n---\n\n### 3️⃣ Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n(or via `pyproject.toml` if using Poetry/UV)\n\n---\n\n### 4️⃣ Add Environment Variables\n\nCreate a `.env` file:\n\n```env\nOPENAI_API_KEY=your_api_key_here\n```\n\n---\n\n### 5️⃣ Run the System\n\n```bash\npython src/my_project/main.py\n```\n\n---\n\n## 🧠 Why This Project Is Strong\n\n✅ Demonstrates **multi-agent orchestration**\n✅ Shows **system design thinking**\n✅ YAML-driven configuration\n✅ Easy to extend and debug\n✅ Beginner-friendly yet industry-relevant\n\nThis is **much stronger** than a single-prompt LLM project.\n\n---\n\n## 🔧 Customization Ideas\n\nYou can easily extend this project by adding:\n\n* A **Resource Curator Agent**\n* Difficulty tagging (Beginner / Intermediate / Advanced)\n* Streamlit UI\n* CSV or JSON export\n* Memory / RAG using documents\n\n---\n\n## 🧩 Technologies Used\n\n* **Python**\n* **CrewAI**\n* **YAML-based agent configuration**\n* **LLMs (OpenAI / compatible providers/ Open Source)**\n\n---\n\n## 📌 Future Roadmap\n\n* [ ] Add evaluation/scoring agent\n* [ ] Add UI interface\n* [ ] Add dataset/topic history\n* [ ] Add RAG-based learning resources\n\n---\n\n## 🧑‍💻 Author\n\n**Rudra Prasad Bhuyan**\nBeginner Data Analyst | Multi-Agent System Enthusiast\n\n* GitHub: [https://github.com/Rudra-G-23](https://github.com/Rudra-G-23)\n* LinkedIn: [https://www.linkedin.com/in/rudra-prasad-bhuyan-44a388235](https://www.linkedin.com/in/rudra-prasad-bhuyan-44a388235)\n\n<!-- Two Master Repo Links -->\n<p align=\"center\">\n  <a href=\"https://github.com/Rudra-G-23/Data-Science-Roadmap\">\n    <img src=\"https://img.shields.io/badge/My_Data_Science_journey -Explore-red?style=for-the-badge\" alt=\"Data Science Roadmap Badge\"/>\n  </a>\n  <a href=\"https://github.com/Rudra-G-23/Data-Science-Projects-Portflio\">\n    <img src=\"https://img.shields.io/badge/My_Data_Science_Projects-View-green?style=for-the-badge\" alt=\"Data Science Projects Badge\"/>\n  </a>\n</p>\n---\n\n## 📜 License\n\nThis project is licensed under the **MIT License**.\n\n---\n","readmeExcerpt":"🤖 AI Research Assistant (Sequential CrewAI) --- 📌 Overview **AI Research Assistant** is a **sequential multi-agent system** built using **CrewAI** that transforms a **raw learning topic** into a **structured, beginner-to-advanced learning roadmap**. 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