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Web Researcher\n\n**Responsibility:** Find relevant and up-to-date information.\n\nCapabilities:\n\n- Web search\n- Information discovery\n- Source identification\n- Evidence gathering\n\n```text\nWeb Researcher\n      │\n      ▼\nSearch Tool\n      │\n      ▼\nResearch Findings\n```\n\n---\n\n## 2. Technical Analyst\n\n**Responsibility:** Analyze the engineering and technical aspects of the research topic.\n\nFocus areas include:\n\n- Architecture\n- Performance\n- Scalability\n- Latency\n- Cost\n- Implementation complexity\n- Engineering trade-offs\n\n---\n\n## 3. Industry Analyst\n\n**Responsibility:** Analyze real-world enterprise and business implications.\n\nFocus areas include:\n\n- Enterprise adoption\n- Business use cases\n- Benefits\n- Risks\n- Costs\n- Industry trends\n\n---\n\n## 4. Report Writer\n\n**Responsibility:** Synthesize the findings from the other agents into a final research report.\n\nThe writer combines:\n\n```text\nWeb Research\n     +\nTechnical Analysis\n     +\nIndustry Analysis\n     ↓\nFinal Research Report\n```\n\n---\n\n# 🔄 Task Pipeline\n\nEach agent is associated with a specific task.\n\n```text\nWeb Research Task\n        │\n        ▼\nTechnical Analysis Task\n        │\n        ▼\nIndustry Analysis Task\n        │\n        ▼\nReport Writing Task\n```\n\nTasks use **context** to provide information generated by previous tasks to downstream agents.\n\nFor example:\n\n```text\nWeb Research Task\n        │\n        │ output\n        ▼\nTechnical Analysis Task\n        │\n        │ output\n        ▼\nIndustry Analysis Task\n        │\n        ├───────────────┐\n        │               │\n        ▼               ▼\n                 Report Writing Task\n```\n\nThis demonstrates how CrewAI tasks can form an information pipeline rather than functioning as isolated LLM calls.\n\n---\n\n# 🌊 CrewAI Flow\n\nThe project also contains a Flow implementation:\n\n```text\nflows/\n└── crew_flow.py\n```\n\nThe Flow introduces a higher-level orchestration layer.\n\n### Crew\n\nA Crew represents a team of agents performing work.\n\n```text\nCrew\n ├── Agent\n ├── Agent\n └── Tasks\n```\n\n### Flow\n\nA Flow represents the application workflow.\n\n```text\nFlow\n │\n ├── Start\n ├── Execute Crew\n ├── Store State\n └── Listen for Completion\n```\n\nThis separation is important when building more complex agentic applications.\n\n---\n\n# 📁 Project Structure\n\n```text\nai-research-squad/\n│\n├── agents/\n│   ├── industry_analyst.py\n│   ├── report_writer.py\n│   ├── technical_analyst.py\n│   └── web_researcher.py\n│\n├── flows/\n│   └── crew_flow.py\n│\n├── tasks/\n│   ├── industry_analyst_task.py\n│   ├── report_writing_task.py\n│   ├── technical_analyst_task.py\n│   └── web_research_task.py\n│\n├── .gitignore\n├── config.py\n├── content.md\n├── crew.py\n├── main.py\n└── requirements.txt\n```\n\n### `agents/`\n\nContains the specialized CrewAI agents.\n\n### `tasks/`\n\nContains the tasks assigned to each agent.\n\n### `flows/`\n\nContains CrewAI Flow implementations.\n\n### `crew.py`\n\nDefines the Crew and orchestrates the agents and tasks.\n\n### `main.py`\n\nApplication entry point used to start the research process.\n\n### `config.py`\n\nContains project configuration and environment-related setup.\n\n### `requirements.txt`\n\nContains the Python dependencies required by the project.\n\n---\n\n# ⚙️ Installation\n\n## 1. Clone the repository\n\n```bash\ngit clone <repository-url>\ncd ai-research-squad\n```\n\n## 2. Create a virtual environment\n\n### Windows\n\n```bash\npython -m venv .venv\n.venv\\Scripts\\activate\n```\n\n### Linux / macOS\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\n```\n\n---\n\n## 3. Install dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n# 🔐 Environment Variables\n\nCreate a `.env` file in the project root.\n\nExample:\n\n```env\nGOOGLE_API_KEY=your_api_key\nSERPER_API_KEY=your_serper_api_key\n```\n\nUse the environment variables required by the LLM provider and search tool configured in your project.\n\n---\n\n# ▶️ Running the Project\n\nStart the application with:\n\n```bash\npython main.py\n```\n\nProvide a research topic through the input expected by the current implementation.\n\nFor example:\n\n```text\nRAG vs long-context LLMs for enterprise AI\n```\n\nThe CrewAI agents will then execute their assigned tasks and produce the final research output.\n\n---\n\n# 🧪 Example Research Topics\n\nTry topics such as:\n\n```text\nThe future of AI agents in software development\n```\n\n```text\nSmall Language Models vs Large Language Models\n```\n\n```text\nAgentic RAG architectures\n```\n\n```text\nMulti-agent systems in enterprise AI\n```\n\n\n---\n\n# 🏗️ Design Principles\n\nThis project follows several important multi-agent design principles.\n\n### 1. Specialized Agents\n\nInstead of giving one agent every responsibility:\n\n```text\nOne Agent\n ├── Research\n ├── Technical Analysis\n ├── Business Analysis\n └── Writing\n```\n\nwe separate responsibilities:\n\n```text\nResearcher\nTechnical Analyst\nIndustry Analyst\nReport Writer\n```\n\nThis allows each agent to have a focused goal.\n\n---\n\n### 2. Task-Oriented Execution\n\nAgents are assigned explicit tasks rather than receiving one enormous prompt.\n\n```text\nAgent\n  +\nTask\n  ↓\nSpecific responsibility\n```\n\n---\n\n### 3. Context Passing\n\nOutputs from earlier tasks can become context for later tasks.\n\n```text\nResearch\n   ↓\nTechnical Analysis\n   ↓\nIndustry Analysis\n   ↓\nReport\n```\n\nThis creates an information flow between agents.\n\n---\n\n### 4. 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