AICortex

A private, self-hosted brain for your LLM — on your own NAS.

In 30 seconds: AICortex is one container on your own hardware that every AI you use connects to.

• It remembers who you are, how you work and what you're building — and hands that to Claude, ChatGPT or a local model at the start of every chat, on desktop and phone.
• It reaches your stuff — services, devices, mail, calendars, files — with the secrets in an encrypted vault the model never sees.
• It keeps your schedule: tell your AI *"tomorrow at 8: summarise yesterday's notes"*. AICortex stores the job and at 8 wakes your Claude (or asks ChatGPT, an API key or a local Ollama) to do it.

Try it in 5 minutes · Run it on your NAS · More screenshots · Connect your AI

AICortex is a self-hosted MCP server that turns your NAS into a personal LLM connector: a persistent "brain" your assistant loads at the start of every session. Any MCP-capable LLM — Claude, ChatGPT, or a fully local model — gains a durable identity and real reach into your own services and devices, while running inside your network. Non-MCP tools (n8n, LangChain, your own scripts) reach the *same* brain through a native REST API with scoped per-user keys — and humans get a built-in admin WebUI at /ui for the vault, skills, users and audit log.

The model stays in its provider's cloud (or runs locally). Your memory, skills and secrets stay on your NAS. The assistant talks to AICortex over an HTTPS connector; the server uses your local credentials internally and never hands them to the model. New capabilities are added as data — a skill, a service config, a secret — with no redeploy.

Highlights

🧠 A brain that travels with you

• One short cortex_start call — who you are, how you work, and the current project's notes, open tasks and last session (a few hundred characters; cortex_catalog has the full list)
• Self-learning, typed memory with a review queue, so it grows without polluting itself
• A skill router — search your skills, load only what's needed, author new ones at runtime
• Playbooks — multi-stage work where a person approves each stage's artifact (e.g. Anthropic's AI-native SDLC: intent.md → spec.md → plan.md …), bundled as a template — see docs/playbooks.md
• The *same* brain across models, desktop and mobile — one account, one state

🔌 Real reach — every integration is just data

• Email (SMTP send + IMAP read), calendars (CalDAV), HTTP services, MQTT & FTP/FTPS devices, WebDAV cloud, SSH/SFTP
• IPP printing & eSCL scanning (straight into Paperless-ngx) on LAN multifunction devices
• Event-driven: inbound webhooks (POST /hooks/ , secret/HMAC-verified) turn external events into inbox items; outbound webhooks notify
• An MCP gateway to use other MCP servers' tools — register any integration with one call, no code, no redeploy

🌐 Beyond MCP — drive it over plain HTTP

• A native REST API: every tool as POST /api/v1/tools/ with an auto-generated OpenAPI 3.1 spec — plug AICortex into n8n, LangChain, an OpenAI-compatible client or a shell script, no MCP client required
• Per-user API keys: scoped (default-deny), hashed at rest, rate-limited, optional expiry — and a key runs the *same* per-user areas as an OIDC session, never admin by default

🖥 Built-in admin WebUI — no terminal needed

• Every signed-in person manages their own notes, scheduled jobs and playbook runs (and approves playbook stages) at `/ui`; admins also get the vault (secret *names* only — values are write-only), skills, users & roles, services & devices editing and the audit log — language-switchable DE/EN
• Same SSO as the connector (OIDC + PKCE against *your* IdP, e.g. Pocket ID), CSRF-guarded, fully self-contained (strict CSP, no CDNs, no build step) — served by the same container, no extra service
• Every page takes the *same policy path* as the MCP tools, so the browser never gets more than the person's AI
• See it in screenshots

👥 Team, autonomy & continuity

• A presence-aware multi-agent board: live presence, capability-routed task *pull*, context-preserving handoff
• Cross-LLM session handoff — resume exactly where another model or device left off
• Cron-as-data scheduling with per-user act-as: a scheduled job runs confined to *its owner's* area, via a short-lived per-job token — never a standing god-token
• Runners carry the jobs out — AICortex wakes your Claude (a Claude Code cloud task on your plan) with the due job, or asks your ChatGPT plan, an API key or a local Ollama; each person sets up their own in the WebUI and gives each job its runner, no extra container (how)

🔒 Secure & multi-tenant

• Your own OIDC login and roles (admin / user / viewer) — one switch, AUTH_ENFORCE
• **Per-user isolation *and* capability areas: private memory + vault per person, plus default-deny** service/skill allow-lists an admin assigns — so not everyone may use every tool
• An encrypted secret vault, an SSRF egress guard, and capability checks that fail closed — end-to-end on your hardware, including a local model (Ollama)

How it works

LLM app — any MCP client (desktop / mobile)  ·  one or many agents

│ custom connector / MCP server (HTTPS, from the model's cloud) ▼ Reverse proxy (Zoraxy / Caddy / nginx / Traefik …) │ ▼ AICortex (this container, on your NAS) │ uses local files & secrets ▼ Memory · Skills · HTTP services · MQTT & FTP devices · WebDAV cloud · Workspace files · SSH/SFTP · SMTP + IMAP email · IPP printing · eSCL scanning · MCP gateway · Inbox/Tasks · Sessions · Cron · Secret vault (every outbound call passes the SSRF egress guard)

How AICortex differs

The "LLM brain" space has some great projects — AICortex deliberately sits in a different spot:

• [GBrain](https://github.com/garrytan/gbrain) is a *memory engine* — markdown-first, Postgres/PGLite-backed, with a self-wiring knowledge graph and hybrid/vector search. Deep retrieval, one concern.
• [CortexPrism](https://cortexprism.io/) is a full *agent operating system* — its own runtime, ~30 LLM providers, a plugin marketplace, sandboxed code execution and multi-user teams. Batteries included, heavyweight.
• AICortex is a single, lightweight *MCP connector*: one container that gives any client a persistent brain — memory and skills, devices, sessions, secrets and scheduling — where new integrations are added as data, no redeploy. And unlike most brain projects it isn't MCP-only: the same tools are exposed over a native REST API with an auto-generated OpenAPI spec, so non-MCP clients (n8n, LangChain, a script) plug in too. No database, no graph, no marketplace; you bring your own client.

Rule of thumb: want a dedicated memory/graph engine → GBrain; want a batteries-included agent OS → CortexPrism; want a minimal self-hosted connector that plugs your existing LLM app into your own stuff → AICortex.

Try it in 5 minutes

Just want to look around? Run it on your own computer — no NAS, domain or login needed, only Docker:

echo "STORAGE_ENCRYPTION_KEY=$(openssl rand -base64 32 | tr '+/' '-_')" > aicortex.env

docker run -d --name aicortex -p 127.0.0.1:8787:8787 --env-file aicortex.env \ -e ALLOW_INSECURE=1 -v aicortex-data:/data ghcr.io/ikarusmk/aicortex:latest

1. Open http://localhost:8787/ui — the WebUI, already signed in as admin.
2. Connect an AI that runs on the same computer. Claude Code: claude mcp add --transport http aicortex http://localhost:8787/mcp, then ask it to call cortex_start. Other local clients: docs/clients.md. The Claude and ChatGPT apps connect from their provider's cloud, so they need the real setup.
3. Tell it something about you, or *"tomorrow at 8: summarise yesterday's notes"* — it shows up under Notes and Scheduled jobs. A job runs once you add a runner with an API key (OpenRouter's free models work); waking Claude needs your own login, so that, too, is the real setup.

⚠️ No login in this mode. ALLOW_INSECURE=1 switches authentication off: whoever

reaches the port is admin. Keep the port on 127.0.0.1 as above, keep real secrets

out, and never run it like this on a server. For everyday use, follow the

Quick start with your own login.

Done looking? docker rm -f aicortex && docker volume rm aicortex-data && rm aicortex.env removes it with all its data.

Requirements

• A NAS or server running Docker (Compose v2).
• A reverse proxy serving the container over public HTTPS (cloud-hosted LLM clients connect from their provider's cloud, so the endpoint must be reachable from the internet; a purely local client can reach it on the LAN).
• A domain/subdomain pointing at your proxy.
• An MCP-capable client that supports custom connectors / MCP servers (Claude, ChatGPT, or any MCP client).

Quick start

🤖 Installing with an AI coding agent? Point it at

[INSTALL_FOR_AGENTS.md](INSTALL_FOR_AGENTS.md) — it walks the agent through the

whole install and verification, handing the human-only steps (DNS, proxy, OIDC) back

to you.

git clone git@github.com:IkarusMK/AICortex.git

cd AICortex cp .env.example .env # adjust PUID / PGID / HOST_PORT / TZ docker compose up -d --build

The MCP endpoint is served at http:// :8787/mcp.

Prebuilt image (no local build): a multi-arch image is published to GHCR by CI.

In docker-compose.yml, comment out build: ., uncomment

image: ghcr.io/ikarusmk/aicortex:latest, then docker compose pull && docker compose up -d.

Updating from 1.x: pull the new image and restart with the same data/ folder and

.env — there is no migration step. Connected apps stay signed in; memory, skills,

vault, services, tasks, sessions and API keys carry over. What changes is listed in

CHANGELOG.md.

Expose it & add the connector:

1. Point a subdomain (e.g. agent.example.com) at your reverse proxy.
2. Proxy that host to http:// :8787 over HTTPS — the upstream is plain HTTP, so do *not* enable "TLS to upstream". If your proxy geo