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title: ActiveGraph: an event-sourced agent runtime where the...
description: Yohei Nakajima (creator of BabyAGI) introduced ActiveGraph, an experimental open-source agent runtime built around an immutable, typed event log rather than an...
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> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

# ActiveGraph: an event-sourced agent runtime where the log defines the agent

**[Collections](https://daily.dev/sources/collections)** · 2 min read · 1 upvotes · 0 comments

## Summary

Yohei Nakajima (creator of BabyAGI) introduced ActiveGraph, an experimental open-source agent runtime built around an immutable, typed event log rather than an LLM. All agent state flows through a shared append-only log that projects a graph state, with behaviors reacting to graph changes and emitting new events — inspired by blackboard architecture and Kafka-style micro-workers. This design enables rollbacks, replays, forks, policy-gated self-modification, and modular behavior packs. Nakajima tested the approach with experiments including using the log as memory (benchmarked on LongMemEval), a self-improving loop where the agent proposes and validates its own patches, and a Pokémon card deck optimizer that tracked failed experiments. The core thesis is that agent identity should be grounded in an experiential log rather than LLM priors alone.

## Content

Yohei Nakajima (creator of BabyAGI) gave a talk at AI Engineer on ActiveGraph, his experimental open-source agent runtime. The core idea is simple but worth sitting with: instead of building agent identity around an LLM, you build it around an immutable event log.

## What it is

ActiveGraph is an event-sourced graph runtime. All state changes flow through a shared, typed event log that projects a graph state. Behaviors react to graph changes and emit new events. The design draws from blackboard architecture and Kafka-style micro-workers - agents don't communicate directly with each other, they write to and read from the log.

The log isn't just infrastructure. It *is* the agent.

## What you get from this

Building on an immutable log unlocks some genuinely useful properties:

- **Rollbacks and replays** - you can rewind to any prior state
- **Forks** - branch execution and explore alternatives
- **Policy-gated self-modification** - the agent can propose patches to itself, validate them, and only accept changes that pass
- **Modular packs** - object schemas and behaviors are bundled into composable units

## Experiments

Nakajima ran a few experiments to test the approach:

- **Log as memory** - tested on LongMemEval, using the event log as the agent's memory store
- **Self-improving loop** - the agent proposes and validates patches before applying them to itself
- **Pokémon card deck optimizer** - tracked failed experiments over time, which is a surprisingly good stress test for long-running state management

## The thesis

If you're working on long-running agent problems, you'll probably end up at immutable event logs eventually. Nakajima's argument is that agent identity and capability should be grounded in an experiential record, not just whatever priors the LLM was trained with.

The talk covers the concept, code examples, and the surprises that came up along the way. Worth watching if you're thinking seriously about durable agents.

---

Tags: [#llm](https://daily.dev/tags/llm), [#architecture](https://daily.dev/tags/architecture), [#ai-agents](https://daily.dev/tags/ai-agents)

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