By Altronis | Published 8 Apr 2026 | Updated 25 Oct 2026

What is OpenClaw?

Corrected on 25 October 2026. An earlier version of this post said I built OpenClaw. I did not: it is an open-source project from the OpenClaw Foundation. What I built is my setup around it.

OpenClaw is the personal AI operator I run on a small machine in my home office. OpenClaw itself is an open-source project from the OpenClaw Foundation, under the MIT licence; I did not build it. What I built is my setup around it: the scheduled jobs, the skills and the agents that hand work to each other. I am writing this so I have something to point at the next time someone asks.

What it actually is

A small box at home running a few long-lived processes:

Scheduled jobs that wake up on a timer: around fifteen in April 2026, when I first wrote this.
Open-source models running locally on a Strix Halo workstation - Qwen 122B for text and Qwen-VL for images.
Free-tier inference from NVIDIA NIM (GLM 4.7, Kimi K2) for the jobs that need a hosted model.
A small team of specialised agents that hand work to each other - one for research, one for coding, one for review, one for orchestration.

The jobs do narrow, repetitive things: drafting morning email replies, watching a few stock positions for technical entry zones, posting market notes when prices move, running a weekly goal review, flagging when one of the jobs has silently broken.

What it costs to run

Effectively zero. Almost everything runs on free NVIDIA NIM, on local inference, or on a small VPS I already had. The jobs themselves are shell scripts, cron entries, and code I have been rewriting.

What it has taught me

Free models do most of the work fine. I had been over-specifying every job with frontier models. Swapping the bulk of them to free NIM tier and local Qwen took the bill from small-but-real to nothing, with no quality drop on the kind of tasks I was running.

Specialised agents beat one general agent. One agent doing nine kinds of job loses context fast. Splitting into a researcher, a reviewer, a coder, and an orchestrator handing work to each other works better. Team design matters more than model choice.

Most operational work is observability, not AI. The hardest failures are silent ones - a job that quietly stopped because an environment variable was not being passed through to a subprocess, a token that expired in the middle of the night, an agent that confidently produced a citation that did not exist. None of these throw errors. All of them need monitoring built from outside the agent. Most of my engineering time goes into noticing when work is not happening, not into the agents themselves.

How this connects to Altronis

When a client asks me to design an agentic AI system for their internal operations, I am not reasoning from theory. I am reasoning from a small version of one I have been running on my own time. The systems Altronis builds for clients are larger and more deeply integrated, but the principles are the same: schedule what you can, observe everything from outside, give each agent a narrow role, and do not pay for frontier reasoning when a small model will do.

If you are figuring out where agentic AI fits in your operations - and where it does not - that is the conversation I am interested in having.

Frequently asked

What is OpenClaw and how does it differ from Claude or other agents?

OpenClaw is an open-source personal AI agent from the OpenClaw Foundation, under the MIT licence. We run a fork of it as an autonomous operator: it runs continuously on our own machine, executes scheduled tasks, keeps a memory of context, and orchestrates other AI tools. Claude is a single-conversation assistant; in this setup OpenClaw is the loop that wraps Claude, Goose, local LLMs and, as of May 2026, about 50 skills into a 24/7 worker.

Can OpenClaw be deployed at an enterprise?

OpenClaw is open source, so any team can run it. The setup described here is built for one operator on one machine. A team deployment would need multi-tenant memory isolation, role-based skill access and audit-log export.

What is the relationship between OpenClaw and Lyra?

OpenClaw is the operator layer (the brain that decides what to do). Lyra is the application layer (the CRM+ERP the operator drives). They are designed to compose: OpenClaw decides 'invoice this customer'; Lyra is the system the operator clicks.