Most agent frameworks are built around the language model: a conversation loop comes first, then tools, then rules, and finally a logging layer bolted on for observability, with state persisted as retrievable "memory." We describe ActiveGraph, a runtime that inverts this arrangement. The append-only event log is the source of truth; the working graph is a deterministic projection of that log; and behaviors--ordinary functions, classes, LLM-backed routines, or logic attached to typed edges--react to changes in the graph and emit new events. No component instructs another; coordination happens entirely through the shared graph. This single design decision yields three properties that retrieval-and-summarization memory systems do not provide: deterministic replay of any run from its log, cheap forking that branches a run at any event without re-executing the shared prefix, and end-to-end lineage from a high-level goal down to the individual model call that produced each artifact. We present the architecture, a determinism contract that makes replay sound, and a worked diligence example whose full causal structure is reconstructable from the log alone. We discuss--without claiming to demonstrate--why this substrate is unusually well suited to self-improving agents, and how it extends the BabyAGI lineage and prior graph-memory research.

翻译：大多数智能体框架围绕语言模型构建：先设计会话循环，再接入工具，随后制定规则，最后附加日志层用于可观测性，并将状态持久化为可检索的"记忆"。本文提出ActiveGraph——一种颠覆该架构的运行时系统。仅追加的事件日志是唯一事实来源；工作图是日志的确定性投影；行为（普通函数、类、基于大语言模型的例程，或绑定到类型化边的逻辑）通过响应图状态变更并生成新事件完成协作。所有组件通过共享图实现完全去中心化协调，无需直接指令传递。这一核心设计决策催生了检索-摘要式记忆系统无法实现的三个特性：基于日志的任意运行确定性重放、无需重放共享前缀即可在任意事件处创建低成本分叉、以及从高层目标到产生每个工件之独立模型调用的端到端溯源。本文阐述了系统架构、确保重放正确性的确定性契约，以及通过日志即可重构完整因果结构的尽职调查实例。我们讨论（而非证明）了该架构对自改进智能体的特殊适配性，及其对BabyAGI研究脉络与先前图记忆研究工作的拓展。