GeneGenie: enhancing biomedical question-answering with agentic graphs

Large language models (LLMs) have revolutionized biomedical research, yet they remain prone to hallucinations and struggle with the precise, multi-hop reasoning required for biomedical analysis. To bridge this gap between generative capability of AI model and factual rigor, this article introduces GeneGenie, a model-agnostic, multi-agent framework built upon a directed acyclic graph architecture. Unlike static prompting strategies, GeneGenie implements a deterministic five-node pipeline that orchestrates query planning, intelligent retrieval-augmented generation across curated databases (GenCC, HGNC, and UniProt), and the dynamic execution of bioinformatics tools, including NCBI E-Utilities and local BLAST+. We evaluated the system using the updated 16-module GeneTuring benchmark, comprising 1600 question–answer pairs. The experimental design compared six state-of-the-art models—including GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro—operating in a standalone “Direct Mode” versus the agentic “Graph Mode.” The results demonstrate that the graph-based architecture consistently outperforms single-model baselines across all metrics. Notably, among the six selected LLM models we explored, Gemini 2.5 Pro achieved the highest performance, correctly answering 1158 questions (72.375% accuracy), compared with the best baseline score of only 15.8%. Furthermore, our evaluation utilized an “LLM-as-Judge” semantic assessment, revealing that the agentic approach significantly enhances not only lexical accuracy but also the completeness and factual grounding of responses. While limitations remain in named entity recognition for protein-coding genes, GeneGenie establishes a robust, reproducible paradigm for future biomedical AI systems, proving that tool-augmented orchestration is superior to reliance on raw model scale alone.