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Searches & Scrapes Web| C[Raw Research Markdown]\n    C --> D[Analyst / Critic Agent]\n    D -->|2. Identifies Gaps & Resolves Gaps| E[Refined Fact Report]\n    E --> F[Writer Agent]\n    F -->|3. Synthesizes & Polishes Report| G[Final Research Brief]\n```\n\n1. **Researcher Agent (`Senior Lead Researcher`)**: Conducts initial web queries and scrapes content to assemble a broad, raw list of facts, dates, figures, and reference URLs.\n2. **Analyst/Critic Agent (`Strategic Analyst and Critic`)**: Evaluates the researcher's output for inaccuracies, logical gaps, or contradictions, executing targeted follow-up searches to resolve anomalies himself.\n3. **Writer Agent (`Senior Technical Writer`)**: Formulates the verified facts and critical additions into a professional, publication-ready research brief.\n\n---\n\n## ✨ Features\n\n- 📁 **Proper CrewAI Folder Structure**: Separates logic (`crew.py`) from configuration (`agents.yaml` and `tasks.yaml`) for clean, production-ready maintainability.\n- ⚡ **Streamlit Dashboard**: A dark glassmorphic UI featuring secure API key overrides, model parameter sliders, and a thread-safe live console capturing logs in real-time.\n- 🚀 **Multi-Provider Support**: Supports Gemini, OpenAI, and Groq backends (with a built-in `cache_breakpoint` bypass hotfix for Groq/LiteLLM compatibility).\n- 🔍 **Dynamic Search Engines**: Automatically upgrades search queries to Tavily's premium API if a key is provided, falling back to a free DuckDuckGo search tool if not.\n\n---\n\n## 📁 Project Directory\n\n```text\nmulti_agent_research/\n├── .env.example             # Template for API keys\n├── requirements.txt         # Package dependencies\n├── agent.py                 # Streamlit adapter & root CLI entry\n├── app.py                   # Streamlit dashboard\n├── run.bat                  # Windows startup batch file\n└── src/\n    └── auto_research_brief/\n        ├── __init__.py\n        ├── crew.py           # Decorator-driven Crew definition (CrewBase)\n        ├── main.py           # Standard CLI run entry\n        ├── config/\n        │   ├── agents.yaml   # Decoupled agent definitions\n        │   └── tasks.yaml    # Decoupled task definitions\n        └── tools/\n            ├── __init__.py\n            └── custom_tool.py# Web search and scraping tools\n```\n\n---\n\n## ⚙️ Installation & Setup\n\nEnsure you have **Python 3.12** and **uv** installed.\n\n1. **Clone or navigate to the repository folder:**\n   ```bash\n   cd multi_agent_research\n   ```\n\n2. **Configure environment variables:**\n   Copy the example environment file and fill in your API keys:\n   ```bash\n   copy .env.example .env\n   ```\n   *Example `.env` structure:*\n   ```env\n   GROQ_API_KEY=gsk_your_groq_key_here\n   TAVILY_API_KEY=tvly-your_tavily_key_here\n   ```\n\n3. **Install dependencies and activate the environment:**\n   Using `uv` (recommended):\n   ```bash\n   uv venv --python 3.12\n   .\\.venv\\Scripts\\Activate.ps1   # Windows PowerShell\n   uv pip install -r requirements.txt\n   ```\n\n---\n\n## 🚀 Usage\n\n### 🖥️ Option A: Streamlit Web UI (Recommended)\nLaunch the interactive web application by running the batch launcher or executing:\n```bash\nstreamlit run app.py\n```\nThis launches a browser window where you can choose LLM backends, input keys securely, specify search engines, and watch the agents collaborate in real-time.\n\n### 💻 Option B: Command Line Interface (CLI)\nRun the script directly from your terminal:\n```bash\npython agent.py --topic \"Fusion Energy Breakthroughs\"\n```\nThe script will automatically detect active keys in your `.env` file, choose the correct provider (Groq/Gemini/OpenAI), and save the markdown output as `research_brief_<topic>.md`.\n\n---\n\n## 🧩 Customization\n\nTo modify agent backstories or task guidelines, tweak the respective configuration files under `src/auto_research_brief/config/`:\n- **Modify Agents**: Edit [`agents.yaml`](file:///c:/Kapish/multi_agent_research/src/auto_research_brief/config/agents.yaml) to customize agent personas or goals.\n- **Modify Tasks**: Edit [`tasks.yaml`](file:///c:/Kapish/multi_agent_research/src/auto_research_brief/config/tasks.yaml) to define custom deliverables, rules, or inputs.\n\n---\n\n## 📄 License\nThis project is open-source and available under the [MIT License](https://opensource.org/licenses/MIT).\n","readmeExcerpt":"🧠 Auto Research Brief (CrewAI Multi-Agent Pipeline) An autonomous multi-agent orchestration pipeline built with **CrewAI** designed to compile, critique, and synthesize highly structured, verified research briefs on any topic. 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