Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current bibliometric tools. The objective is to enable researchers to perform fully dynamic, code-based scientometric analysis using natural language NL instructions, eliminating the need for specialized programming skills while expanding analytical depth. Methodologically, the system integrates four coordinated AI agents: a custom analytics generator, a full-paper retriever, including a Retrieval Augmented Generation RAG based researcher assistant and an automated report generator. User queries are translated into executable Python scripts, run within a sandbox ensuring safety, reproducibility and auditability. The framework supports automated data cleaning, construction of co-authorship and citation networks, temporal analyses, topic modeling, embedding based clustering and synthesis of research gaps. Each analytical session produces an exportable, end to end report. The novelty lies in unifying NL to code scientometrics, multimodal full paper retrieval, agentic exploration and dynamic metric creation in a single adaptive environment, capabilities absent in existing platforms: VOSviewer, Bibliometrix, SciMAT. Unlike static GUI based workflows, the proposed framework supports iterative what if analysis, hybrid indicators and user driven pipeline modification. Results demonstrate that the framework generates valid analysis scripts, retrieves and synthesizes full papers, identifies frontier themes and produces reproducible scientometric outputs. It establishes a new paradigm for accessible, interactive and extensible bibliometric knowledge.

翻译：本文提出一个生成式多智能体AI框架，旨在克服当前文献计量工具在刚性、有限灵活性及技术壁垒方面的局限。该框架使研究者能够通过自然语言指令实现完全动态的、基于代码的科学计量分析，在消解特殊编程技能需求的同时拓展分析深度。方法论上，系统整合了四个协同智能体：定制化分析生成器、全文检索器（含基于检索增强生成的研究助理）与自动报告生成器。用户查询被转换为可执行Python脚本，在保障安全性、可复现性与可审计性的沙盒环境中运行。该框架支持自动化数据清洗、合著与引文网络构建、时序分析、主题建模、基于嵌入的聚类及研究缺口综合评估，每次分析会话均可生成可导出的端到端报告。其创新性在于将自然语言到代码的科学计量转换、多模态全文检索、智能体化探索与动态指标创建统一于单一自适应环境——该能力在现有平台（VOSviewer、Bibliometrix、SciMAT）中缺失。不同于静态GUI工作流，该框架支持迭代式假设分析、混合指标与用户驱动管道修改。实验结果表明，框架可生成有效分析脚本、检索并综合全文、识别前沿主题、产出可复现的科学计量输出，从而为可访问、交互式、可扩展的文献计量知识建立新范式。