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Query Generator      │\n│ Generates search        │\n│ queries                 │\n└────────────┬────────────┘\n             ▼\n┌─────────────────────────┐\n│ 2. Product Search       │\n│ Searches the web        │\n│ using Tavily            │\n└────────────┬────────────┘\n             ▼\n┌─────────────────────────┐\n│ 3. Product Research     │\n│ Scrapes product pages   │\n│ using ScrapeGraphAI     │\n└────────────┬────────────┘\n             ▼\n┌─────────────────────────┐\n│ 4. Report Generator     │\n│ Compares products and   │\n│ selects Top 5           │\n└────────────┬────────────┘\n             ▼\n       HTML Report\n</pre>\n\n<h2>Technologies</h2>\n\n<ul>\n    <li><strong>CrewAI</strong> — Multi-agent orchestration</li>\n    <li><strong>AgentOps</strong> — Agent monitoring and observability</li>\n    <li><strong>Tavily</strong> — Web search</li>\n    <li><strong>ScrapeGraphAI</strong> — Web scraping and information extraction</li>\n    <li><strong>Gemini</strong> — Cloud LLM</li>\n    <li><strong>Ollama</strong> — Local LLM option</li>\n    <li><strong>Pydantic</strong> — Structured data validation and management</li>\n    <li><strong>Python</strong> — Core language</li>\n    <li><strong>Jupyter Notebook</strong> — Complete implementation</li>\n</ul>\n\n<h2>Example</h2>\n\n<p>\nFor a request such as:\n</p>\n\n<blockquote>\n    <strong>\"Find the best coffee machine for our office.\"</strong>\n</blockquote>\n\n<p>\nThe system researches different machines, gathers specifications,\nprices, features, reviews and other relevant information, then produces\na report similar to:\n</p>\n\n<ol>\n    <li>🏆 Best Overall Coffee Machine</li>\n    <li>💰 Best Value</li>\n    <li>☕ Best for Large Offices</li>\n    <li>⚡ Best for Convenience</li>\n    <li>💎 Premium Choice</li>\n</ol>\n\n<p>\nThe final report contains the researched products, detailed information,\ncomparison results, and the <strong>Top 5 recommendations</strong>.\n</p>\n\n<h2>LLM Options</h2>\n\n<p>\nThe notebook supports two approaches:\n</p>\n\n<ul>\n    <li>☁️ <strong>Gemini</strong> for cloud-based inference.</li>\n    <li>🖥️ <strong>Ollama</strong> for running an LLM locally.</li>\n</ul>\n\n<p>\nThis makes it possible to switch between cloud and local models depending\non the user's requirements.\n</p>\n\n<h2>Monitoring</h2>\n\n<p>\n<strong>AgentOps</strong> is integrated to monitor the agentic workflow,\nincluding agent executions, LLM calls, tool usage and overall task\nperformance.\n</p>\n\n<h2>Goal</h2>\n\n<p>\nThis project demonstrates how specialized AI agents can collaborate with\nexternal tools to perform a complete real-world research task:\n</p>\n\n<p>\n<strong>Plan → Search → Investigate → Analyze → Recommend → Report</strong>\n</p>\n\n</body>\n</html>\n","readmeExcerpt":"<!DOCTYPE html> <html lang=\"en\"> <head> <meta charset=\"UTF-8\"> <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\"> </head> <body> <h1>Agentic Product Research System</h1> <p> An AI-powered <strong>multi-agent product research system</strong> built with <strong>CrewAI</strong>. 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