9
I built a Deep Researcher agent and exposed it as an MCP server!
r/aiagents
7/7/2025
Content Summary
User Arindam_200 developed a Deep Researcher Agent with three-stage workflow (Searcher, Analyst, Writer) using Scrapegraph, Nebius AI, Agno, and Streamlit. The system is exposed via MCP server for cross-tool compatibility and includes a Streamlit UI. The project is intentionally basic but provides a foundation for building research workflows, with open-source code and tutorial video available.
Opinion Analysis
Mainstream opinion supports the modular approach and open-source sharing. The author actively seeks feedback for improvements. No conflicting opinions present in the limited comments. Key focus areas include:
- Appreciation for MCP server integration
- Interest in expanding functionality
- Value seen in tutorial documentation
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Scrapegraph | Not mentioned | Web Scraping | Used for crawling and extracting live data |
| Nebius AI | Not mentioned | AI Models | Open-source models for data processing |
| Agno | Not mentioned | Agent Orchestration | Used for coordinating agent workflows |
| Streamlit | Not mentioned | UI Framework | Simple dashboard creation |
USER NEEDS
Pain Points:
- Need for multi-step web research automation
- Requirement for integrating AI agents into existing workflows
Problems to Solve:
- Automating complex research and report generation
- Making AI agents accessible through various interfaces
Potential Solutions:
- Modular agent architecture with specialized components
- MCP server integration for cross-tool compatibility
- Open-source codebase for community contributions
GROWTH FACTORS
Effective Strategies:
- Modular architecture design for flexible implementations
- Open-source approach to encourage community involvement
Marketing & Acquisition:
- Video tutorials demonstrating practical applications
- GitHub repository sharing for developer adoption
Monetization & Product:
- Focus on interoperability through MCP compatibility
- Basic but solid foundation for future expansion
User Engagement:
- Direct call for user feedback and feature requests
- Public code sharing to foster collaboration