we've been building agents around the llm, starting with conversations, adding tools, giving rules, logging everything, and storing a form o…

we've been building agents around the llm, starting with conversations, adding tools, giving rules, logging everything, and storing a form of it as retrievable state Active Graphs flips this. what if we treated the logs as the agent, which include rules provided (and changed), tools given/used, and conversations. behaviors (including llm calls) sit on top of this, react to changes, and then create changes back into the state. this is more like how we work. we don't have workflows, just a whole bunch of rule based behaviors that sometimes chain. we are also not defined by our reasoning capability, but by our beliefs and experiences that shaped them. i'm excited to open source Active Graph: an event-sourced reactive graph runtime for long-running, agents 🔄🧠 events/logs projects a graph. reactive behaviors react and affect the graph. fork-and-diff agent runs. no A2A, no workflows, no DAG site: http://activegraph.ai docs: htt…

Source: Yohei Nakajima (X) | 2026-05-20