Distributed Atomspace (DAS) is an extension of the OpenCog Hyperon Atomspace, designed to store and manage massive hypergraphs across multiple machines. It offers flexible queries, parallel processing, and high availability—vital for complex AI inference and large-scale AGI initiatives.
Store extensive knowledge bases across networked nodes, ensuring scalable data capacity for large AI experiments.
Multiple AI agents can simultaneously read and write to the DAS, sharing insights and learning outcomes in real time.
Run DAS as a standalone server or integrate as a Python library, adapting seamlessly to diverse AI frameworks and pipelines.
Handle intensive knowledge queries and global searches, powered by parallel nodes and efficient data partitioning.
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