Sber open-sources gMAS, a framework that picks the makeup of an SI agent team on its own
Prepared by Sintelligent with the help of SI. Publication time restored from the build log. . Updated: .
Designing agent teams is now being handed to the algorithms themselves, one more layer of work moving from people to machines.
In the popular LangGraph framework, the developer sets and edits the agent interaction graph by hand. In gMAS, an algorithm selects the agents and the links between them: the system changes the graph on the fly and switches off useless agents. According to the Sber team's measurements, early stopping cuts token use by 52.6% and time by 45%. On branching graphs gMAS is 19.6% faster than LangGraph, and learnable link pruning cuts costs by a further 27%. The package is published on PyPI and GitHub under an open license, and there is a visual studio, flowMAS.
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