KataGo is a production-grade Go-playing AI engine that combines deep neural networks with Monte Carlo Tree Search (MCTS). The system supports self-play reinforcement learning, GTP protocol for GUI integration, analysis mode, and distributed training. Its architecture features a C++ core with multiple GPU backends (CUDA, ROCm, OpenCL, Metal, ONNX, TensorRT) for neural network inference, a comprehensive MCTS search implementation with graph enhancements, and Python training scripts. The codebase demonstrates strong engineering with well-abstracted backends, thread-safe utilities, and a modular configuration system. External dependencies include NVIDIA CUTLASS and cuDNN frontend for high-performance GPU kernels, indicating a focus on cutting-edge hardware utilization.