🤖 AI Summary
Traditional agent frameworks treat logs as a post-hoc observability layer, lacking auditability, reproducibility, and efficient branching capabilities. This work proposes ActiveGraph, a runtime system that treats an append-only event log as the sole source of truth, with the working graph serving as its deterministic projection. Agents coordinate by generating new events in response to graph changes, eliminating direct inter-component calls. This architecture enables deterministic replay of arbitrary execution traces, low-cost event-level branching, and end-to-end provenance from high-level objectives down to individual model invocations. By integrating event sourcing, reactive graph computation, typed edge bindings, and LLM-powered behavioral modules, we implement a due diligence case study whose complete causal structure can be reconstructed solely from logs, demonstrating the system’s effectiveness for auditable and forkable agent applications.
📝 Abstract
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.