Financial research report generation is challengingdue to diverse data types, real-time requirements, and thecomplexity of financial analysis. This paper introduces FinGPT-Agent, a multi-agent framework that uses Large LanguageModels (LLMs) to tackle these challenges. The frameworkincludes multimodal fusion for handling different data types,task-specific optimization with Low-Rank Adaptation (LoRA),retrieval-augmented generation with contrastive learning forbetter context, and reinforcement learning with human feedbackto improve report quality. A hierarchical attention mechanismhelps summarize long financial documents. Experiments showthat FinGPT-Agent performs better than baseline models andsets a benchmark for financial report generation using LLMs.