💡 The paper introduces OpenRath, a programming model for multi-agent systems that addresses the issue of fragmented runtime state. In current agent systems, various aspects such as transcripts, tool effects, and memory events are recorded separately, making it difficult to inspect or reproduce the system's behavior. OpenRath solves this problem by introducing a central runtime abstraction called Session, which is a first-class value that can be passed between agents and workflows.

The Session abstraction is designed to be branchable, inspectable, replayable, backend-aware, and composable, allowing it to record various execution state information such as conversation chunks, sandbox placement, and tool evidence. This enables explicit fork, merge, and replay operations as runtime operations rather than reconstructing states from external traces.

OpenRath also defines other key concepts such as Sandbox, Tool, Agent, Memory, Workflow, and Selector, which work together to provide a comprehensive programming model for multi-agent systems. The Selector is particularly important as it turns control flow into runtime-routed decisions.

The paper presents the programming model, architecture, and evidence protocol of OpenRath, and claims that the Session abstraction provides agent systems with a first-class runtime value for auditable composition. The results of this work are limited to controlled runtime properties, and further evaluation is needed to compare the performance of OpenRath with other systems and to assess its availability and quality.

Overall, OpenRath contributes a novel programming model for multi-agent systems that provides a unified and explicit way to manage runtime state, making it easier to inspect, reproduce, and debug the behavior of these systems.