How does the DAS work?
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.
01
Distributed Storage
Store extensive knowledge bases across networked nodes, ensuring scalable data capacity for large AI experiments.
02
Real-Time Collaboration
Multiple AI agents can simultaneously read and write to the DAS, sharing insights and learning outcomes in real time.
03
Flexible Integration
Run DAS as a standalone server or integrate as a Python library, adapting seamlessly to diverse AI frameworks and pipelines.
04
Scalable Performance
Handle intensive knowledge queries and global searches, powered by parallel nodes and efficient data partitioning.