Active Graph: Event-Sourced Runtime for Agentic Systems

ActiveGraph is an event-sourced reactive graph runtime designed to build long-running, auditable AI agents. Unlike traditional frameworks that focus on the model call, this system treats an immutable event log as the primary source of truth, where the working state is a deterministic projection of that log. Behaviors react to changes in the shared graph and emit new events, allowing independent components to coordinate without direct communication. This architecture enables powerful features such as deterministic replay, which reproduces any run from its history, and cheap forking for testing counterfactual scenarios without re-executing previous steps. The project evolved from the BabyAGI lineage to provide a "continuity layer" that maintains a coherent model of beliefs, goals, and history over time. Ultimately, ActiveGraph shifts agent identity from transient model outputs to a traceable, experiential record of every action and modification.