Open Source

MIT licensed

AEGIS

A self-hosted, flow-first personal AI orchestration platform

A small fleet of named agents run scheduled and event-driven Temporal workflows over your own data — tasks, email, money, knowledge, homelab alerts — and ask you for a decision only when they actually need one. Local-LLM-first, open source, and built to run on your own hardware.

View on GitHubRead the field notes

What it is

A careful backstage system, not a louder assistant

Every week there is another agent demo that promises to do everything. AEGIS is a smaller, stranger bet: that software can learn the shape of one person's life well enough to interrupt less. It watches the boring things — your tasks, your inbox, your money, your notes, the alerts from the machines in your house — and it only reaches for you when a decision is actually yours to make.

It is built to be forked and configured for your own life, not signed up for. There is no hosted version and no multi-tenant anything. You point it at your own accounts and your own models, and it runs on your own hardware. The value is not a product you switch on; it is a set of patterns — durable workflows, human-approval gates, personality-scoped tools, local-first inference — that you can run yourself.

The AEGIS admin panel — a decision-first Overview screen showing pending decisions, alerts, agents and uptime
The Overview: what needs you right now, and how the system is doing.

How it works

A few ideas, applied consistently

None of these is novel on its own. The interesting part is what happens when you hold to them everywhere — one primitive for every decision, one durable engine for every job.

One primitive: interactions

Every time AEGIS needs you, it is the same shape — a row in Postgres, a card in your chat app, and a durable workflow waiting on your tap. Approvals, choices, drafts to review, plain acknowledgements: one mechanism, not a dozen.

Durable and flow-first

Every job is a Temporal workflow. Schedules, retries and multi-day waits survive restarts and deploys, and a change to a schedule takes effect without a redeploy.

A small fleet of agents

Four named agents — Sebas for tasks, Raphael for research, Maou for money, Pandora’s Actor for infrastructure — each a permission boundary with its own tools, model tier and voice.

Behaviour is data, not code

Agents key on capability tags, not identity. Rename them, re-scope them, or add your own from an admin page — without touching a line of Python.

GTD at the centre

Todoist is the canonical task store, mirrored every five minutes. Work-streams are labels, the inbox gets clarified automatically, and the system asks before it acts.

Local-LLM-first

A LiteLLM proxy resolves fast / balanced / smart tiers to whatever models you point it at, reaching for a hosted model like Claude only when a job wants more horsepower.

Your knowledge as infrastructure

A native Postgres + pgvector retrieval store the agents can search and cite, seeded from your own URLs, uploads and folders. No separate service to run.

Runs on your own hardware

FastAPI, Postgres and Temporal on a small Docker Swarm at home — not a cloud region, not a SaaS. You bring your own credentials and your own models.

The AEGIS admin panel — the agent fleet: Sebas, Raphael, Maou and Pandora's Actor
The fleet: four agents, each a permission boundary with a name.
TemporalFastAPIPostgres + pgvectorLiteLLMReactDocker Swarm

The story so far

Field notes from building it

AEGIS was two rewrites in before any of this, and private for most of its life. These are notes from the build — and from the ten days of turning something that worked for me into something you can fork.

AEGIS Is Open Source

Three months ago I wrote about my personal AI orchestration system and ended with a section titled 'Why It's Not Open Source Yet.' That's fixed. AEGIS is on GitHub, MIT-licensed — here's what it actually took to get there.

Behavior Is Data, Not Code

The biggest refactor of the AEGIS open-sourcing sprint: removing every branch on an agent's identity so behavior lives in the database as capability tags — resolved at runtime, edited from a UI, and safe even when a tag has no owner.

A To-Do Is a Tweet: Social Publishing With Approval Cards

Scheduling a social post doesn't need a new UI. A to-do already has copy, a time, and labels — so in AEGIS, a Todoist task with a publish label is a scheduled post, and nothing goes out until a card in chat gets a human tap.

Bring Your Own Cloud: The Infrastructure Registry

For a self-hosted system that's meant to be forked, there's exactly one honest way to handle infrastructure credentials: the user brings their own, the system stores them encrypted, and nothing in the code assumes a vendor. Here's how AEGIS got there — and what it cost me to cut my own vendors out.

Labels, Not Projects: Rethinking GTD

The day Next and Someday stopped being Todoist projects and became labels — and why that small modelling decision is what made AEGIS's GTD layer, and its bidirectional sync, actually work.

Day One of Opening the Box

AEGIS is going open source, and today the first public commits landed. Day one of turning a private system into a shippable one: twenty-seven migrations squashed into a baseline, credentials evicted from the build, and an admin panel redesigned around decisions.

When the Bot Learns to Stay Quiet

AEGIS watches my homelab and GitHub, but the interesting part is what happens between an alert firing and me hearing about it. Most of the time, the correct answer is nothing.

One Primitive for Every Interruption

The design decision in AEGIS I'm proudest of isn't an agent or a model — it's a single interactions primitive. How one Postgres table, five kinds of card, and a Temporal workflow replaced every per-domain approval pattern.

Meet AEGIS: My Weird Little Operating Layer

Every week there’s another agent demo, another workflow canvas, another pitch about software running software for us. OpenClaw, browser agents, Zapier, n8n, Claude Code, MCP s

Take it apart, or have one built

The code is open — star it, fork it, or tell me what breaks. And if you want a careful, durable automation system like this built for your own business, that is the kind of work I do.

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