For instance, a simple keyword search such as \"Aged AND tDCS\" often retrieves only a fraction of relevant studies, overlooking important trials due to variations in terminology and inconsistent reporting. Moreover, vital protocol details frequently reside within unstructured text, requiring extensive human curation for accurate extraction [39; 40]. Consequently, aging-related tDCS trials serve as an ideal model topic for testing automated data extraction methods, providing valuable insights into the capabilities and limitations of LLM-based approaches within a clinically relevant and complex research area. In the current study, we automated and standardized significant portions of this data extraction process using a multi-agent LLM pipeline [41]. After downloading and consolidating clinical trial data, each model independently parsed the **BriefSummary** and **DetailedDescription** fields before outputting a structured, machine-readable JSON summary. Comparing outputs revealed areas of consensus (e.g., yes/no presence of tDCS interventions) as well as points of ambiguity (e.g., complex parameter descriptions or anatomical targets). This approach not only captured more relevant trials than conventional searches but also demonstrated how multi-agent LLMs can streamline systematic reviews [8; 42; 43; 44; 45]. While the findings highlight the benefits of automated pipelines--quicker extraction and reduced manual workload--they also underscore the need for human oversight to reconcile ambiguous information and address the variability of LLM performance. Furthermore, by enabling a rapid iterative refinement of the search criteria and extraction parameters, multi-agent systems can significantly enhance the responsiveness and adaptability of systematic reviews [46] in dynamic research domains. Illustrating both the potential and limitations of this multi-agent paradigm, this work advances aging-related tDCS research and lays the groundwork for applying similar methods to other domains, such as electronic medical records (EMRs), clinical trial guideline synthesis, and broader medical data processing. This study is among the first to systematically benchmark multiple state-of-the-art LLMs specifically for extracting structured clinical trial data from aging-related research protocols involving neuromodulatory interventions such as tDCS. Unlike previous research, which predominantly assesses single-model performance or focuses on general clinical texts, this investigation uniquely evaluates a multi-agent ensemble approach. By demonstrating the strengths and limitations of individual LLMs and the added value of cross-model consensus methods, this work introduces a novel, rigorous methodology for automated data extraction tailored to the nuanced and heterogeneous domain of aging research. The primary research question (RQ1) of this study was to examine how consistently five state-of-the-art LLMs--openai/01-mini, x-ai/grok-2-1212, Llama-3.3-70B-Instruct, Gemini-Flash-1.5-8B, and DeepSeek-R1-70B-Distill--extract key methodological fields from aging-related tDCS trial protocols. We hypothesized substantial, systematic performance differences and expected instruction-tuned, high-parameter models (e.g., Llama-3.3-70B-Instruct, DeepSeek-R1-70B-Distill) to outperform smaller, general-purpose models (e.g., openai/01-mini) on complex, multi-field extractions involving numeric and multi-arm design details. The secondary research question (RQ2) investigated whether a simple ensemble consensus method using majority voting for categorical fields and mean or median values for numeric fields could achieve greater accuracy and inter-rater reliability compared to the best-performing individual model. We anticipated that the ensemble method would yield significantly higher agreement with expert-validated reference labels (\(\kappa>0.90\) for categorical fields, ICC > 0.95 for numeric fields) than any single model, thus demonstrating the practical value of multi-agent pipelines.