Accurate online sorting of spikes with many channels is still a challenging problem. Existing online sorters either use simple algorithms with low accuracy or can only process a handful of channels. We have developed a state-of-the-art online spike sorting platform in Python that enables large-scale, fully automatic real-time spike sorting and decoding on hundreds of channels. Our platform has comparable accuracy to offline sorters and can achieve an end-to-end sorting latency of around 160 ms for 128-channel signals. It will be useful for research in fundamental neuroscience, closed-loop feedback neuromodulation and brain-computer interfaces.