paperseek
LLM based Literature Search Agent. 一个面向研究者的文献发现工具，支持用自然语言发起检索、自动迭代查询、扩展候选论文、排序结果并导出可复核的文献列表。
PaperSeek takes a plain-language research question, uses AI to search academic databases like OpenAlex and Web of Science, ranks results by relevance, and exports a filterable paper list with citation graphs.
PaperSeek is a research literature discovery tool that accepts a question in plain language, then uses an AI model to search academic databases, rank the results by relevance, and export a reviewable list of papers. The README is primarily in Chinese with an English version available. The project is in alpha and is licensed under Apache 2.0.
The core workflow starts with you typing a research question in Chinese or English. The tool generates appropriate search queries for the selected data source, checks how many results come back, and automatically broadens or narrows the query across up to five iterations until it reaches the target number of results you specify. Each candidate paper is then scored for relevance by the AI model with a short explanation. Optionally, the tool can follow citation links for the top-matching papers, pulling in both the papers they cite and the papers that cite them, which helps surface related work that a keyword search might miss.
Data sources supported include OpenAlex, which is open and free to use, Crossref for DOI and publisher metadata, and the Web of Science Starter API for users with institutional access. Results include title, authors, journal, year, DOI, abstract, citation count, and links.
The tool works through a local web interface at port 8765 or a command-line interface. The web interface has four sections: a search workspace where you configure and monitor a run in real time, a results table where you filter and select papers to export as CSV, a citation graph showing directional links between papers, and a history view of past searches stored locally in SQLite.
You can connect almost any AI model. Supported providers include OpenAI, Anthropic, Google Gemini, DeepSeek, and many Chinese services including Alibaba DashScope, Kimi Moonshot, Tencent Hunyuan, Baidu Qianfan, and others. Local models through Ollama also work. Deployment options include running it directly with Python, using Docker for a complete setup, or deploying to Vercel for a lightweight hosted version.
Where it fits
- Type a plain-language research question and get an AI-ranked list of relevant academic papers from OpenAlex or Web of Science.
- Build a citation graph to surface related research that keyword searches would miss by following citation links for top results.
- Export a filtered paper list as CSV to include in a literature review or reference manager.
- Connect a local Ollama model or any major AI provider to power the relevance ranking without sending data to third-party services.