We introduce DeepResearchEco, a novel agentic LLM-based system for automated scientific synthesis that supports recursive, depth- and breadth-controlled exploration of original research questions—enhancing search diversity and nuance in the retrieval of relevant scientific literature. Unlike conventional retrieval-augmented generation pipelines, DeepResearch enables user-controllable synthesis with transparent reasoning and parameter-driven configurability, facilitating high-throughput integration of domain-specific evidence while maintaining analytical rigor. Applied to 49 ecological research questions, DeepResearch achieves up to a 21-fold increase in source integration and a 14. 9-fold rise in sources integrated per 1, 000 words. High-parameter settings yield expert-level analytical depth and contextual diversity. Source code available at: https: //github. com/sciknoworg/deep-research.
D’Souza et al. (Sun,) studied this question.