Explainability in intracranial hemorrhage (ICH) diagnosis is essential for timely and accurate clinical decisions, especially in life–threatening situations. We propose a framework that generates explainable, clinically relevant text from 2D CT scans using two cooperative GPT-4o agents: a Multi-modal User Agent (MUA) and a Planner Agent. The MUA interprets scans with YOLOv10 (mosaic augmentation), SAM2, and clustering; the Planner selects tools and outputs key imaging parameters: bleed location, midline shift, calvarial fracture, and mass effect crucial for urgent interventions. Explainability is enforced via chain-of-thought prompting to ensure transparent decision-making. Experiments show YOLOv10 with mosaic improves mAP@0.5:0.95 by 4.1% over existing methods, and the LLM agents extract clinical parameters with 78.1% accuracy (Our code is available at https://github.com/Shashwathp/Explainable-ICH-Detection-with-LLM-Agents/tree/main ). These results underscore the potential of explainable AI to enhance trust and reliability in critical healthcare applications.