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What is Libra OS? The operating system for the AI workforce, explained

· 3 min read
Libra OS Team
Libra OS Team
Nebula Nova Inc.

The simplest way to think about Libra OS: a desktop operating system is the base layer that runs your applications; Libra OS is the base layer that runs, coordinates, and supervises a team of AI employees. One system where they share knowledge, hand work to each other, and complete real business processes end to end — inside your walls.

Not one model — many, working in parallel​

Libra OS is model-agnostic by design. Instead of depending on a single AI vendor, it routes work across multiple models side by side — a planner-class model for the hard calls, a fast lightweight model for high-volume steps, a synthesis model for the final grounded answer. Each tier is just a model id behind any OpenAI-compatible endpoint: bring your own keys, point a tier at a locally-run model, or mix providers per tier.

Model ids carry a vendor prefix so the gateway knows where to route each request — anthropic/… for an Anthropic model, gemini/… for a Google model. (Locally-run Ollama is the one exception and needs no prefix.) The full routing story is in Model settings.

A team, not a chatbot​

Libra OS ships with eight supervised digital employees, and you can define your own in YAML. The point isn't one assistant that answers questions — it's specialized roles collaborating on one system: one employee drafting the document, another researching, another handling the customer thread, all sharing the same knowledge base and the same audit trail.

Every employee works under the same supervision bound: output is source-cited, work that escalates past an employee's scope goes to a human, and nothing ships without review. That last part is what makes it safe to hand real work over — you can see exactly what steps each employee took and which tools it used.

Yours, wherever it runs​

Libra OS comes in two editions of the same product: Libra OS Cloud, hosted and sign-up-and-go, and Libra OS Self-Hosted — your cloud or fully air-gapped on-prem, where customer data, model traffic, and knowledge stores never leave your environment. Same engine, same employees, same API; an employee built in one runs in the other.

Build on it: the SDK​

The official Python devkit, libraos-sdk (1.0.2), is live on PyPI. It speaks the same protocol as Anthropic's Managed Agents, so Anthropic SDK callers can point at a Libra OS deployment with a drop-in client — see Creating an agent.

One streaming caveat worth knowing when you build observability: while server-side tools run, the SSE stream emits no discrete content-block events. The tool results appear in the final MessageResponse.content[] as server_tool_use blocks. If you're rendering live activity, poll message or job state for those phases — or use a non-streaming call — rather than waiting on stream events that never come.

In one sentence​

Libra OS gives a team of AI employees a shared work desk — and gives you the supervision, citations, and audit trail to confidently hand them real work.

Start with the getting-started guide, or read how the two editions compare.