Sarsa is an onpolicy TD control algorithm:
$$Q\left(S_{t}, A_{t}\right) \leftarrow Q\left(S_{t}, A_{t}\right) + \alpha\left[R_{t+1} + \gamma{Q}\left(S_{t+1}, A_{t+1}\right)  Q\left(S_{t}, A_{t}\right)\right] $$
This update is done after every transition from a nonterminal state $S_{t}$. if $S_{t+1}$ is terminal, then $Q\left(S_{t+1}, A_{t+1}\right)$ is defined as zero.
To design an onpolicy control algorithm using Sarsa, we estimate $q_{\pi}$ for a behaviour policy $\pi$ and then change $\pi$ towards greediness with respect to $q_{\pi}$.
Source: Sutton and Barto, Reinforcement Learning, 2nd Edition
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