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Multi-Agent AI Research Assistant\n\n**Automated academic paper analysis, summarization, and quality review — powered by CrewAI and Google Gemini**\n\n[![Python](https://img.shields.io/badge/Python-3.10+-3776AB?style=flat&logo=python&logoColor=white)](https://www.python.org/)\n[![CrewAI](https://img.shields.io/badge/CrewAI-Multi--Agent-6C5CE7?style=flat)](https://www.crewai.com/)\n[![Gemini](https://img.shields.io/badge/LLM-Google%20Gemini-4285F4?style=flat&logo=google&logoColor=white)](https://ai.google.dev/)\n[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)\n\n<br>\n<img src=\"assets/AI_Research_Assistant_Thumbnail.png\" alt=\"ResearchMind AI Thumbnail\" width=\"600\"/>\n\n</div>\n\n---\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Problem Statement](#problem-statement)\n- [Key Features](#key-features)\n- [Architecture & Workflow](#architecture--workflow)\n- [Meet the Agents](#meet-the-agents)\n- [Tech Stack](#tech-stack)\n- [Getting Started](#getting-started)\n- [Usage](#usage)\n- [Sample Output](#sample-output)\n- [Repo Structure](#repo-structure)\n- [Skills Demonstrated](#skills-demonstrated)\n- [Future Enhancements](#future-enhancements)\n- [Author](#author)\n- [License](#license)\n\n---\n\n## Overview\n\n**ResearchMind AI** is a multi-agent AI system that reads an academic research paper (PDF) and autonomously produces a structured, publication-ready report — complete with an executive summary, methodology breakdown, key findings, and a final quality-reviewed writeup.\n\nRather than relying on a single LLM call, the system coordinates **three specialized AI agents**, each responsible for a distinct stage of the analysis, mirroring how a real research team (analyst → writer → reviewer) would collaboratively produce a report.\n\n## Problem Statement\n\nResearchers and students spend hours manually reading, interpreting, and summarizing dense academic papers. This project automates that process end-to-end — extracting key technical insights, translating them into clear educational content, and running an automated quality-assurance pass — cutting review time from hours to minutes.\n\n## Key Features\n\n- 📄 **Automated PDF ingestion** — extracts and processes text directly from research paper PDFs\n- 🤖 **Multi-agent orchestration** — three purpose-built AI agents collaborate in a coordinated pipeline\n- 🧩 **Sequential task chaining** — each agent's output becomes the next agent's input, mirroring a real editorial workflow\n- 📝 **Structured, publication-ready reports** — executive summary, methodology, findings, applications, and conclusion\n- ✅ **Built-in AI peer review** — a dedicated reviewer agent audits the final report for accuracy, clarity, and grammar before publishing\n- ⚡ **Async execution** — runs the full crew workflow asynchronously for efficient orchestration\n\n## Architecture & Workflow\n\nThe system follows a **sequential multi-agent pipeline**: the output of each agent becomes the input for the next, ensuring progressively refined output at every stage.\n\n```mermaid\nflowchart LR\n    A[📄 PDF Research Paper] --> B[Text Extraction<br/>pypdf]\n    B --> C[🔎 Research Analyst<br/>Extracts objectives, methodology,<br/>results & limitations]\n    C --> D[✍️ Academic Writer<br/>Converts analysis into a<br/>student-friendly report]\n    D --> E[🧐 Peer Reviewer<br/>Audits accuracy, clarity<br/>& readability]\n    E --> F[📑 Final Structured<br/>Research Report]\n\n    style A fill:#1a1a2e,stroke:#4285F4,color:#fff\n    style F fill:#1a1a2e,stroke:#34A853,color:#fff\n    style C fill:#16213e,stroke:#6C5CE7,color:#fff\n    style D fill:#16213e,stroke:#6C5CE7,color:#fff\n    style E fill:#16213e,stroke:#6C5CE7,color:#fff\n```\n\n**Pipeline stages:**\n\n1. **Ingestion** — the target PDF is loaded (from Google Drive in the reference implementation) and text is extracted from its pages using `pypdf`\n2. **Analysis** — the Research Analyst agent extracts the paper's title, objective, problem statement, methodology, dataset, models, results, strengths, limitations, and future work\n3. **Synthesis** — the Academic Writer agent transforms that technical analysis into a clear, well-structured educational report\n4. **Review** — the Peer Reviewer agent checks the draft for technical accuracy, missing information, logical flow, grammar, and readability, rewriting sections as needed\n5. **Output** — the final polished report is returned as plain text, ready to save, publish, or share\n\nThis is orchestrated using CrewAI's `Process.sequential` execution model, where a single `Crew` manages all three agents and tasks end-to-end.\n\n## Meet the Agents\n\n| Agent | Role | Responsibility |\n|---|---|---|\n| **Research Analyst** | Extracts technical substance | Reads the paper and accurately pulls out its core technical information |\n| **Academic Writer** | Simplifies without losing meaning | Converts dense technical findings into clear, educational content |\n| **Peer Reviewer** | Quality gatekeeper | Improves clarity, completeness, and factual consistency before final delivery |\n\n## Tech Stack\n\n| Category | Technology |\n|---|---|\n| **Multi-Agent Framework** | [CrewAI](https://www.crewai.com/) |\n| **LLM Interface** | [LiteLLM](https://www.litellm.ai/) |\n| **LLM Provider** | Google Gemini |\n| **PDF Processing** | pypdf |\n| **Language** | Python 3 |\n| **Development Environment** | Google Colab |\n| **Storage** | Google Drive |\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.10+\n- A [Google Gemini API key](https://ai.google.dev/)\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/nabankur14/ai-research-assistant-crewai.git\ncd ai-research-assistant-crewai\n\n# Install dependencies\npip install -q -U crewai litellm pypdf\n```\n\n### Configuration\n\nSet your Gemini API key as an environment variable (or enter it securely at runtime — the reference notebook uses `getpass` so the key is never hardcoded or persisted):\n\n```bash\nexport GEMINI_API_KEY=\"your-api-key-here\"\n```\n\n## Usage\n\n1. Place your target research paper (PDF) in your working directory or connected Google Drive\n2. Update the `pdf_path` variable to point to your file\n3. Run the notebook (or script) end-to-end\n4. The three agents will execute sequentially, printing progress as they work\n5. The final structured report is available via `result.raw`\n\n```python\nresult = await crew.kickoff_async()\nprint(result.raw)\n```\n\n## Sample Output\n\nTested end-to-end on the seminal paper *\"Attention Is All You Need\"* (Vaswani et al.), the pipeline produced a complete academic review, opening with:\n\n> *\"Demystifying the Transformer: An Academic Review of 'Attention Is All You Need'\"* — covering the Transformer's departure from recurrence and convolution in favor of self-attention, its parallelization advantages, and its lasting impact on modern deep learning, followed by a full breakdown of introduction, methodology, results, and conclusions.\n\n## Repo Structure\n\n```\nai-research-assistant-crewai/\n│\n├── notebook/                     \n│   └── AI_Research_Assistant_(ResearchMind AI).ipynb   # Main notebook: agents, tasks, crew, execution\n├── README.md                     # Project documentation\n└── LICENSE                       # License file\n```\n\n## Skills Demonstrated\n\n### Technical Skills\n![Python](https://img.shields.io/badge/PYTHON-3776AB?style=flat&logo=python&logoColor=white) ![CrewAI](https://img.shields.io/badge/CREWAI-6C5CE7?style=flat) ![Google Gemini](https://img.shields.io/badge/GOOGLE%20GEMINI-4285F4?style=flat&logo=google&logoColor=white) ![LLM Orchestration](https://img.shields.io/badge/LLM%20ORCHESTRATION-FF9900?style=flat) ![Prompt Engineering](https://img.shields.io/badge/PROMPT%20ENGINEERING-00C7B7?style=flat) ![Agentic Workflow](https://img.shields.io/badge/AGENTIC%20WORKFLOW-4CAF50?style=flat) ![PDF Processing](https://img.shields.io/badge/PDF%20PROCESSING-E34F26?style=flat) ![Async Python](https://img.shields.io/badge/ASYNC%20PYTHON-8E44AD?style=flat)\n\n### Soft Skills\n![Analytical Thinking](https://img.shields.io/badge/ANALYTICAL%20THINKING-008080?style=flat) ![Problem Solving](https://img.shields.io/badge/PROBLEM%20SOLVING-800080?style=flat) ![Technical Writing](https://img.shields.io/badge/TECHNICAL%20WRITING-FF6347?style=flat) ![Research Analysis](https://img.shields.io/badge/RESEARCH%20ANALYSIS-2E8B57?style=flat)\n\n## Future Enhancements\n\n- [ ] Add OCR support for scanned/image-based PDFs\n- [ ] Remove the 12-page extraction limit for full-length paper support\n- [ ] Build a Streamlit front-end for non-technical users\n- [ ] Support multiple LLM providers (OpenAI, Anthropic, local models)\n- [ ] Export final reports directly to PDF/DOCX\n- [ ] Add a citation-extraction agent for reference validation\n\n## Author\n\n**Nabankur Ray**\nData Scientist & ML Engineer\n\n- 🌐 Portfolio: [analyticswithnaban.com](https://analyticswithnaban.com)\n- 💻 GitHub: [@nabankur14](https://github.com/nabankur14)\n- 🔗 LinkedIn: [@nabankur-ray](https://www.linkedin.com/in/nabankur-ray-876582181/)\n\n## License\n\nThis project is licensed under the MIT License — see the [LICENSE](LICENSE) file for details.\n\n---\n\n<div align=\"center\">\n\n⭐ If you found this project interesting, consider giving it a star!\n\n</div>\n","readmeExcerpt":"<div align=\"center\"> 🧠 ResearchMind AI Multi-Agent AI Research Assistant **Automated academic paper analysis, summarization, and quality review — powered by CrewAI and Google Gemini** $1 $1 $1 $1 <br> <img src=\"assets/AI_Research_Assistant_Thumbnail.png\" alt=\"ResearchMind AI Thumbnail\" width=\"600\"/> </div> --- Table of Contents - 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