Introduction

The ADB (Agent Databank) is a registry of multi-agent AI safety experiments and results.

Motivation

Multi-agent research is expensive, and fundamentally observes emergent behavior. Nothing agentic is deterministic: you can’t draw reliable conclusions from a single run.

It should be easy to re-run a published result, pause it halfway through, change something, and triplicate both branches. However, today, everyone configures a slightly-different setup, and even when published research includes code, it commonly under-specifies the environment and experimental setup.

The ADB curates a registry of well-specified experiments, makes it easy to run them, and collates the results.

Roadmap

The current platform is an MVP, and is only meant to be run locally.

Planned features (in somewhat priority order):

• automatically depositing runs to HuggingFace
• website running publically, indexing runs on HuggingFace
• claude skill for implementing an experiment

How this guide is organized

Top to bottom, by how deep you’re going:

• Using the platform — running experiments and browsing the results. Start at Getting started; the first loop takes a few minutes with any model credential (or runs keyless against a mock).
• Experiment catalog — what you can run today, with exact commands.
• Reference — the CLI surface and the on-disk layout.