FluxVLA is a comprehensive, config-driven framework for training, evaluating, and deploying Vision-Language-Action (VLA) models for robotic manipulation. It supports multiple VLA architectures (OpenVLA, Pi0/Pi0.5, GR00T, Cosmos3, DreamZero, etc.) and robot platforms (LIBERO simulation, RoboCasa, Franka, UR3, ALOHA, Oli humanoid, Tron2). The codebase provides a modular pipeline with registries and builders for datasets, transforms, models (backbones, heads, projectors), optimizers, collators, and inference runners. Production capabilities include distributed training (FSDP/DDP), real-time ROS inference, custom CUDA/Triton kernels for performance, and evaluation with metrics reporting to Feishu. While primarily an engineering framework for applied robot learning, it also contains research contributions such as flow-matching action heads, reward models (ARM, SARM), and real-time chunking (RTC) guidance. The architecture is clean and extensible, though config proliferation and vendored third-party code introduce maintenance overhead.