neomjs/neo: README-based editorial guide
A guide grounded in the README, repository metadata, and license for installing and checking neomjs/neo.
Project scope
neomjs/neo describes itself in the README as "Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active Hybrid GraphRAG, DreamService, and self-healing loops.". This article keeps to facts that can be checked in the repository. Stars, forks, and promotional badges are signals of attention, not proof of quality. Under "A self-evolving software organism", the README says: Neo.mjs is a professional, end-to-end AI engineering team that lives in its own open-source repository.. That establishes the project's stated boundary, not a production test.
Suitable use cases
The README's "A self-evolving software organism" section gives a useful starting point for deciding whether the project fits: The Body (/src/) , the production multi-threaded application engine: App Worker, VDom Worker, Data Worker, Canvas Worker, SharedWorker, JSON blueprints, object permanence, and zero-build native ES modules.. If that problem is not yours, popularity is a poor reason to adopt it. Project names, commands, and component names are kept as written so a reader can return to the primary source without guessing at terminology. Another checkable README item is: The Brain (/ai/) , the Agent OS: Memory Core, Knowledge Base, Native Edge Graph, A2A coordination, GitHub workflow automation, DreamService, and the named human + AI maintainer institution.. It can shape a first test, but it does not replace testing in the intended environment.
How it works
The operating model is spread across sections such as "A self-evolving software organism". The source evidence includes: Through the Neural Link possession interface, the swarm does not just read code; it inhabits live applications , inspecting semantic runtime state, mutating UI and data in real time, turning conversational UIs from chat panels into agents. This article does not turn missing architecture, performance, or security details into claims. A real deployment still needs a look at the repository layout, configuration files, and release history.