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EJ
EJ

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I built a tiny AI company that runs on my laptop (multi-agent application)

#agents #ai #programming #opensource

Holons Demo

Lately I’ve been thinking about what “work” might look like in a world of AI agents.

I was talking with my wife about it — maybe in the future, instead of replacing people one by one, it becomes one person managing a small team of AI workers.

But tools like Claude Code or OpenClaw felt way too technical for her.

So I started wondering:

what if this actually felt like working with a team?

Not writing code.

Not wiring workflows.

But hiring, managing, and collaborating.

So I built Holons.

What it looks like

Holons is a desktop-first system (Tauri + Flask + React) where:

  • agents have roles and identities
  • a “lead” agent assigns tasks and builds workflows
  • multiple agents can collaborate in group chats
  • everything runs locally (or self-hosted)

You can say something like:

“Create a pitch for a B2B AI accountant”

And it will:

  • propose a workflow
  • assign tasks to different agents
  • estimate cost
  • and let you run the whole thing

What surprised me

The hardest part wasn’t the agents.

It was visibility.

Once you have multiple agents running, you need to understand:

  • what they’re doing
  • how much they cost
  • how they behave over time

So one design decision I made early:

Every LLM call writes to a unified ledger

Each call tracks:

  • model + provider
  • tokens (prompt / completion)
  • cost
  • latency
  • agent + user

That single table powers:

  • cost dashboards
  • usage quotas
  • reports
  • audit logs

This turned out to be way more important than I expected.

Some technical notes

  • Multi-provider support: Bedrock, OpenAI, Anthropic, Gemini, MiniMax
  • Per-agent model binding (different agents can use different models)
  • pgvector-based RAG + external knowledge integrations
  • MCP-style tool integration
  • IM channels (Telegram / Slack / LINE)

Still early

This is very much an experiment.

I’m not sure yet if this is the “right” abstraction for multi-agent systems,
but it already feels different from traditional workflows.

More like managing a team,

less like calling an API.

Links

Curious how others are thinking about multi-agent systems.

Are people actually using them in real workflows,
or are we still figuring out the right model?

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