In indoor environments, accurate position estimation of multi-agent systems is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. If the multiagent system relies upon noisy measurements of the change in position and orientation, the integrated position estimate can drift potentially unboundedly. Magnetic field simultaneous localization and mapping (SLAM) has previously been proposed as a way to compensate for position drift in a single agent. We propose two novel algorithms that allow multiple agents to apply magnetic field SLAM using their own and the other agents’ measurements. Our first algorithm is a centralized algorithm that uses all measurements collected by all agents in a single extended Kalman filter. The algorithm simultaneously estimates the agent’s position and orientation and the magnetic field norm in a central unit that can communicate with all agents at all times. In other applications, there is no central unit available, and there are communication drop-outs between agents. Our second algorithm is therefore a distributed algorithm for multiagent magnetic field SLAM, that can be employed even when there is no central unit, and when there are communication failures between agents.