Background
While researchers often need to collect multiple papers for trend analysis, which is traditionally inefficient to do manually, LangManus' agent collaboration automates data collection, cleansing, and report generation.
core element
- Mission design: Enter a goal, such as "Analyze the distribution of models for top papers in NLP in 2023" via the API or command line.
- Agent Collaboration Process:
- The researcher agent searches for papers on platforms such as arXiv/ACL using the Tavily API;
- The encoder agent extracts chart data from PDF and structures it;
- The Reporter Agent generates analysis reports in Markdown format.
- tool integration: Configure Jina neural search to improve relevance filtering, or call HuggingFace model for text summarization.
- Output Optimization: Customize the report template in src/prompts/ to require the inclusion of statistical charts and references.
Summary points
Prior access to academic database APIs (e.g., Semantic Scholar) is required, and complex analyses can combine multiple subtasks to be completed in stages.
This answer comes from the articleLangManus: an open source AI automation framework supporting multi-intelligence collaborationThe
May not be reproduced without permission:AI productivity tools " How can LangManus be applied to automate thesis data analysis in academic research scenarios?
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