Automating Literature Review Generation with Open Deep Research
Literature reviews in academic research often require a lot of time for data collection and organization.Open Deep Research offers the following solutions:
- Intelligent planning phase: The system automatically generates research outlines, and users can accept the default structure or provide customized chapter plans in JSON format.
- Multi-source information gathering: Web search through APIs such as Tavily, HuggingFace, etc., covering academic databases and open resources to automatically access relevant research materials.
- Auto-referencing mechanism: The tool retains all sources and automatically generates a standardized citation format to ensure academic integrity.
- Multi-Round Verification Process: Reduce the burden of manual verification by iteratively verifying the quality of information through a cyclical workflow of "plan→search→reflect".
Specific implementation steps:
- Installing Python 3.12+ and the uv toolchain
- Configure API keys for Together AI, Tavily, etc.
- Run commands like `python main.py -topic "Advances in Quantum Computing Research" -max_search_depth 3`
- Wait for the system to generate a report in Markdown format with full citations
Note: For highly specialized topics, it is recommended that manual review be supplemented at a later stage; targeting can be improved by providing more detailed section requirements through the `-structure` parameter.
This answer comes from the articleTogether Open Deep Research: Generating Indexed Deep Research ReportsThe
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