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local Multi-Agent System for in-depth research, analysis, and content creation.**\n\nBuilt with **[CrewAI](https://crewai.com)** and **[Ollama](https://ollama.com)**, this project creates a team of AI agents that collaborate to research a topic, analyze the findings, write a comprehensive report, and save it to your local machine—all running locally on your hardware using 3B parameter models (like Qwen 2.5).\n\n---\n\n## 🌟 Features\n\n* **100% Local Execution:** Uses Ollama to run quantized LLMs (e.g., Qwen 2.5 3B), ensuring privacy and zero cost.\n* **Multi-Agent Pipeline:** Four specialized agents work sequentially:\n    1.  **Senior Research Analyst:** Scours the web for recent developments, facts, and expert opinions.\n    2.  **Critical Content Strategist:** Critiques the raw data, identifies gaps, and builds a logical outline.\n    3.  **Lead Content Creator:** Drafts a professional, well-formatted Markdown report.\n    4.  **File Publishing Utility:** Safely handles file I/O to save your report.\n* **Real-Web Access:** Integrated **DuckDuckGo Search** allows agents to fetch real-time information.\n* **Safe File System:** Custom `SafeFileWriteTool` ensures agents can only write to a designated `output/` directory.\n* **Interactive CLI:** A continuous loop interface allows you to run multiple research tasks in one session.\n\n---\n\n## 🏗️ Architecture\n\nThe system operates on a \"Chain of Thought\" workflow:\n\n```mermaid\ngraph LR\n    A[User Input] --> B(Researcher)\n    B -->|Raw Findings| C(Analyst)\n    C -->|Structured Outline| D(Writer)\n    D -->|Final Markdown| E(Publisher)\n    E -->|Save File| F[output/filename.md]\n```\n## The Agents\n\n1. **Senior Research Analyst:** Uses DuckDuckGoSearchTool to gather raw information.\n\n2. **Critical Content Strategist:** Pure logic agent. Structures chaotic data into a coherent narrative.\n\n3. **Lead Content Creator:** Creative agent. Writes engaging prose based on the strategist's outline.\n\n4. **File Publishing Utility:** Functional agent. Strictly prohibited from \"thinking\" or \"formatting\"; its only job is to execute the save command.\n\n## 🚀 Getting Started\n### Prerequisites\n\n- **Python 3.10+**\n\n- **Ollama** installed and running.\n\n### Installation\n1. **Clone the repository:**\n\n```Bash\ngit clone https://github.com/pranav-wakode/deep-research-crew.git\ncd deep-research-crew\n```\n2. **Create and activate a virtual environment:**\n\n```Bash\n# Linux/Mac\npython3 -m venv venv\nsource venv/bin/activate\n\n# Windows\npython -m venv venv\nvenv\\Scripts\\activate\n```\n3. **Install dependencies:**\n\n```Bash\npip install -r requirements.txt\n```\n4. **Setup the Local Model:** Make sure Ollama is running, then pull the model used in the configuration (Qwen 2.5 3B Instruct is recommended for speed/quality balance):\n\n```Bash\nollama pull yxchia/qwen2.5-3b-instruct:Q4_K_M\n```\n(Note: If you want to use a different model, edit 'model=' in 'src/agents.py').\n\n## 🏃‍♂️ Usage\n1. **Start the Ollama Server** (in a separate terminal):\n\n```Bash\nollama serve\n```\n2. **Run the Crew:**\n\n```Bash\npython3 main.py\n```\n3. **Interact:**\n\n- Enter your research topic when prompted (e.g., \"The future of solid state batteries\").\n\n- The crew will execute the pipeline (Research -> Analyze -> Write -> Publish).\n\n- Once finished, the script will tell you where the file is saved (e.g., 'output/the_future_of_solid_state_batteries.md').\n\n## 📂 Project Structure\n```Plaintext\ndeep-research-crew/\n├── main.py               # Entry point (CLI loop, filename logic)\n├── requirements.txt      # Python dependencies\n├── output/               # Generated reports are saved here\n└── src/\n    ├── __init__.py\n    ├── agents.py         # Agent definitions and LLM configuration\n    ├── tasks.py          # Prompt engineering for specific tasks\n    └── custom_tools.py   # Wrapper for Search and File System tools\n```\n\n## 📄 License\nDistributed under the MIT License. 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