Enhancing Analytical Dimension with Multi-Agent Collaboration
Open Deep Research optimizes the quality of market analysis by:
- Hierarchical search strategy: By setting the `-max_search_depth` parameter (default 2, 3-5 is recommended), we can realize progressive information mining, for example, when searching for "EV market", it will automatically extend the related dimensions such as "battery technology", "policy subsidies", etc. For example, when searching for "EV market", it will automatically extend the related dimensions such as "battery technology", "policy subsidies", etc.
- Multi-agent authentication mechanism: The planning agent determines the analysis framework, the search agent implements directed crawling, and the writing agent performs cross-validation to form a closed-loop quality control.
- Data Source Extension: Additional APIs such as Google Custom Search can be integrated to extend the information coverage (requires modification of `search_providers.py` in the project code).
- Multimodal Outputs: Work with DALL-E to generate data visualization covers and use TTS services to produce executive summary audio to enhance report presentation.
Operational Recommendations:
- Use a structured query like `python main.py -topic "Asia Pacific Cloud Computing Market Forecast 2025" -structure market_analysis.json`
- Improve search accuracy by specifying industry glossaries in configuration files
- Iterative optimization of the preliminary report using the `-refine` parameter
- Enhanced Data Authority with HuggingFace Integration for Local Commercial Databases
Advanced Tip: When analyzing competing products, you can use the project's Comparative Analysis module after generating multiple topic reports in parallel (requires Community Edition plugin).
This answer comes from the articleTogether Open Deep Research: Generating Indexed Deep Research ReportsThe
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