I built a self-hosted AI agent that markets itself. Here's how.

#ai #opensource #selfhosted #node

Two months ago my co-founder and I got tired of paying for AI tools
that live on someone else's server, phone home constantly, and charge
per seat.

So we built our own.

Today we're open sourcing it. It's called Daemora.

What it actually does

You install it with one command:

npm install -g daemora
daemora setup
daemora start

Then you message your Telegram or Discord bot like a teammate.

This week I asked it to:

Find the top 10 skipping ropes on Amazon, analyse them, create a Google Doc and email it to someone
Research catnip, write a report, save it to my machine and send it to me on Telegram
Fix a bug in a GitHub repo, open a PR, run the tests and health check the service every minute

All three worked. While I made coffee.

The part that surprised us most

We thought the hard part would be the LLM integration.

It wasn't.

The hard part was security. When you give an AI agent access
to your actual machine, you have to think about things like:

Preventing it from dumping your environment variables
Stopping it from reading files outside its sandbox
Stripping secrets from subprocess environments so they can't be leaked via executeCommand
Blocking network egress if a response contains a known secret value

We ended up with a 14-layer security model. AES-256-GCM
encrypted vault, filesystem sandbox, subprocess isolation,
egress guard, prompt injection tagging. You can run
daemora doctor for a full scored security audit.

The thing we're most proud of

Three-layer memory.

Daemora has semantic, episodic, and procedural memory with
automatic extraction, composite-scored recall, and confidence
decay. It learns from every task without you manually saving
anything. Unified session across all channels - you can start
a task on Telegram and follow up on Discord.

It also has smart loop detection. If the agent starts burning
tokens in repetitive patterns - exact repeat, ping-pong,
semantic repeat, or polling loops - it detects and stops
automatically. This one saved us a lot of money during
development.

The meta part

This week I gave Daemora a prompt to market itself.

Set up watchers for competitor mentions. Draft Reddit posts.
Monitor GitHub for star spikes. Send me a Telegram report
every Friday with growth metrics. Generate short-form videos
via the Media Studio crew and post them automatically.

It's now running its own growth campaign. Autonomously.
On my laptop.

That felt like the moment we knew it was real.

What's inside

52 built-in tools - files, web, shell, email, scheduling, vision, media, multi-agent delegation
20 messaging channels - Telegram, Slack, Discord, WhatsApp, email, Signal, Teams and more
7 AI providers including Ollama - works fully local, no API key needed
Native MCP integration - GitHub, Notion, Linear, Postgres in one command
Media Studio crew - generate AI images, videos, and music. Edit videos with Remotion - add captions, transitions, effects programmatically
Provider failover - automatic retry with exponential backoff. If a provider goes down, switches automatically
Three-layer memory - semantic, episodic, procedural. Learns from every task, no manual saving needed
60 built-in skills - coding, research, email, GitHub, Slack, Google Workspace, macOS automation and more

The stack if you're curious

Runtime: Node.js 20+ - ES modules, no build step
AI SDK: Vercel AI SDK - model-agnostic, 25+ providers
Storage: SQLite + file-based (Markdown, JSONL)
Scheduling: croner - production-grade cron
Vault: Node.js crypto built-in - AES-256-GCM + scrypt
Voice: Twilio + OpenAI Realtime STT + ElevenLabs TTS
Video: Remotion - React-based programmatic video editing

No Docker required. No binary dependencies for the vault.
Runs as a system daemon on macOS, Linux, or Windows WSL.