AI代理的「时间旅行」：一个Session搞定所有
现在的AI代理系统，运行状态散落各处：对话记录、工具调用、内存事件、工作区位置、分支来源、回放证据……各存各的，想查个完整流程得拼图。OpenRath 提出一个反直觉的思路：让一个叫 Session 的「运行时值」像 PyTorch 的张量一样，成为多代理系统的核心抽象。这个 Session 能分支、能检查、能回放、能组合，它随身携带对话片段、沙箱位置、血缘元数据、token用量、待办工作和工具证据。最关键的是，fork（分叉）、merge（合并）、replay（重放）不再是事后从外部痕迹重建的状态，而是显式的运行时操作。你可以在程序执行中直接回溯到某个Session，看看当时发生了什么。它不是你明天能用上的工具，但如果你关心AI代理的可审计性和可复现性，这个思路值得关注。
📄 原文摘要(英文)
Modern agent systems often suffer from fragmented runtime state: transcripts, tool effects, memory events, workspace placement, branch provenance, and replay evidence are recorded separately and become difficult to inspect or reproduce. OpenRath addresses this issue with a PyTorch-like programming model for multi-agent, multi-session systems. The analogy concerns the role of a central first-class runtime abstraction, not tensor computation. Its core abstraction is Session, the runtime value passed between agents and workflows. A Session is branchable, inspectable, replayable, backend-aware, and composable. It records conversation chunks, sandbox placement, lineage metadata, token usage, pending work, and tool evidence, while defining where memory interactions enter the runtime record. Since this state is carried by the same value used in program execution, fork, merge, and replay become explicit runtime operations rather than states reconstructed from external traces. OpenRath further defines Sandbox, Tool, Agent, Memory, Workflow, and Selector, with Selector turning control flow into runtime-routed decisions. This report presents the programming model, architecture, audited milestones, and evidence protocol. Its claims are limited to controlled runtime properties, while broad quantitative comparisons, live-provider quality, optional-backend availability, and memory quality are left for follow-on evaluation. The central thesis is that Session provides agent systems with a first-class runtime value for auditable composition.