Running
Can You Run It? LLM version
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Kartik Agrawal
Kartik2503
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25 days ago
Hello everyone, happy to share with you my experimentation of a Deep Research Assistant, using 7 agents and a quality assurance pipeline :
🤖 What makes this special:
✅ Agent-Based Architecture - 7 specialised AI agents working together:
- Planner Agent - Strategic search planning
- Search Agent - Multi-source web research
- Writer Agent - Comprehensive report generation
- Evaluator Agent - Automatic quality assessment
- Optimiser Agent - Iterative improvement when needed
- Email Agent - Professional report delivery
- Clarifier Agent - Interactive query refinement
✅ Quality Assurance Pipeline - Every report is scored (1-10) and automatically improved if it scores below 7/10
✅ Multiple Research Modes - From quick queries to deep, clarification-driven analysis
✅ Production-Ready - Deployed on Hugging Face Spaces with comprehensive documentation
🔧 Technical Stack:
- Frontend: Gradio with theme-adaptive UI
- Backend: OpenAI Agents framework
- Integration: SendGrid for email delivery
- Deployment: Containerised with full CI/CD pipeline
- Tracing: Full OpenAI trace integration for transparency
💡 Real-World Impact:
This isn't just another AI tool - it's a complete research workflow that delivers publication-quality reports with built-in fact-checking and optimisation. Perfect for consultants, researchers, analysts, and anyone who needs reliable, comprehensive research.
🚀 Key Features:
- Automatic quality evaluation and improvement
- Email delivery of formatted reports
- Interactive clarification for targeted results
- Full traceability and audit trails
- Professional documentation and deployment guides
- Built with modern AI engineering principles: modular design, quality assurance, and production deployment in mind.
- The entire codebase is organised with clean separation of concerns - each agent has a specific role, making it maintainable and extensible.
https://huggingface.co/spaces/mallocode200/Deep_Research_Assistant
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