DBA Brain

What it does

DBA Brain collects health metrics from your SQL Server, Oracle, PostgreSQL and MySQL instances, grades them against policies you write, runs your scheduled SQL, takes backups and proves them by restoring them, checks objectives against real measurement history, and tells you about it, on a schedule, without an agent on any monitored machine.

It sends nothing anywhere by itself. No telemetry, no usage reporting, no update check. It connects to the databases and hosts you list, and, only if you turn it on, to a chat service. Nothing else.

What it is not

• Not an AI agent. It is what an agent operates. Nothing here talks to a model or decides anything on its own.
• Not a dashboard product. The primary interface is a CLI; the primary output is a record in a database you own.
• Installs nothing on your databases. No agent, no extension, no stored procedure. It logs in with a read-only account and asks questions.

What it produces

Real report pages from a live estate, with every name replaced by a stable fake one. Click an image to open it full size.

How it works

One run, end to end

Whoever starts it (the daemon, a person, a chat command or an AI agent), a run takes the same path. The app hands one JSON request to transport, which starts the shared operations CLI; that CLI reaches the database or host and answers with one JSON envelope. Results land in the runtime store, and reports and alerts are built from there.

Every command answers in the same envelope, whether the work succeeded or not:

{
  "success": true,
  "operation": "restore-full",
  "message": "Restored SALESDB_STG to 2026-08-07 01:40:00.",
  "error": null,
  "data": { },
  "metrics": { "duration_ms": 41230 }
}

From a measurement to an alert

A backup is proven by restoring it

Why: cheap for an AI agent, fine without one

The command a person runs is byte-for-byte the command an agent runs, so there is one code path to audit, and every run is logged the same way whoever started it.

  WITHOUT a tool                        WITH DBA Brain
  agent -> raw SQL -> agent             agent -> DBA Brain -> database

  "check this database"                 "check this database"
    -> SELECT ... dm_os_wait_stats        -> one JSON request
    <- every row, into the context
    -> SELECT ... sys.databases           <- one graded envelope:
    <- every row, into the context             41 checks, 40 OK,
    -> ... once per check, per run             1 WARNING: appdb
    <- all of it, healthy or not               log backup 31h old

The saving is not compression. It is not sending what nobody needed to read. And no model is required: remove the agent and the daemon, schedules, reports and alerts all still run.

15 components

Ten apps, each doing one job with its own CLI and never importing another app, and five shared layers that every app stands on. The list is closed, and every component has exactly one reference doc.

Who may call whom

• common may not be imported: it is only ever run as a CLI.
• lib may not run a CLI: it is only ever imported.
• No app imports another app. No shared layer imports an app.
• Each rule has a guard test beside it, so the diagram describes what is true, not just what was intended.

Apps

Shared layers

Install

Python 3.12+. Each database driver is an extra, so you install only what you run.

python -m venv .venv
.venv/bin/pip install 'dbabrain[postgres]'

Published on PyPI via trusted publishing.

Five-minute try-out with a throwaway PostgreSQL container:

cd examples/postgres-quickstart
docker compose up -d
python -m db_ops.db.cli      --config config.json init
python -m db_ops.metrics.cli --config config.json collect --dry-run
python -m db_ops.metrics.cli --config config.json collect
python -m db_ops.metrics.cli --config config.json report

Docs and source

• dbabrain on PyPI (download stats)
• Source on GitHub: README, docs, examples
• Architecture and first run
• Changelog
• Security policy: how to report a vulnerability
• License: Apache License 2.0

Contact: donations and investment

DBA Brain is free, open source, and maintained by one person. If you would like to donate, sponsor the project, or talk about investment or partnership, please get in touch directly:

For bugs and feature requests, please use GitHub issues. For security problems, follow the security policy rather than email.