MultiAgentOS by lukekevinmclaughlin-oss
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Long-form notes from the team

Local-first AI, multi-agent patterns, BYO LLMs.

Articles on the ideas behind MultiAgentOS — local LLMs, multi-agent orchestration, tool authority leases, and the case for privacy-first AI tooling. RSS if you prefer a reader.

  1. Concepts

    Multi-agent AI explained — what it actually is, and when it's useful

    The honest version: a multi-agent system is just “multiple LLM calls coordinated by a controller.” Here's what that looks like in practice, when it beats single-prompt approaches, and when it's overkill.

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  2. Cost

    Local LLMs vs. cloud APIs — the 5-year cost reality

    For most agentic workloads, a $1,500 GPU pays for itself in 6–18 months versus OpenAI / Anthropic API spend. Here's the math, the break-even points, and the workloads where it doesn't apply.

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  3. vs

    Cursor vs. MultiAgentOS for privacy-sensitive teams

    Cursor is a great editor; it just sends your code to OpenAI / Anthropic. For teams where that's a non-starter (legal, healthcare, finance, gov, defence), here's a side-by-side and a migration playbook.

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  4. How-to

    How to run AI agents locally with Ollama — a practical 30-minute setup

    From a clean machine to a multi-agent setup that actually does work — installing Ollama, picking a tool-capable model, wiring it into MultiAgentOS, and running your first end-to-end task.

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