How can I make my DSP app scale seamlessly?
Extending Data Spatial Semantics for Scale Agnostic Programming
This paper introduces "scale-agnostic programming" using Data Spatial Programming (DSP), enabling applications to seamlessly transition from single-user to multi-user, local to distributed, and ephemeral to persistent contexts without code changes. It extends DSP with a persistent root node (for state and user context isolation), walkers as API entry points, and topology-aware distribution.
For LLM-based multi-agent systems, this offers a framework for managing persistent agent states, individual user interactions with agent groups (via their own root node), and scaling the system across multiple machines as agent interactions grow. The walker-as-API model provides a natural way to expose LLM agent functionalities for external invocation, streamlining integration with other services. The focus on topological relationships simplifies managing complex agent interactions and data flows, potentially easing the implementation of distributed multi-agent reasoning and communication.