What is the Neural Link?

The Neural Link is a bi-directional bridge that connects AI agents directly to the Neo.mjs runtime. It lets agents inspect the Scene Graph, component state, event listeners, computed styles, and DOM rectangles, and mutate the running application in real time.

Why is Neo.mjs called an Application Engine instead of a framework?

Neo.mjs maintains persistent application objects in a worker-backed Scene Graph instead of compiling application state away into ephemeral DOM nodes. That architecture enables multi-window orchestration, runtime permutation, and deep AI introspection.

What is Context Engineering?

Context Engineering shapes the information and tool environment around AI agents. Neo.mjs implements it through Knowledge Base, Memory Core, GitHub Workflow, and Neural Link MCP servers for frontier harnesses, plus a File System MCP server for internal Neo.ai.Agent local loops.

What is the Neo.mjs Agent OS?

The Neo.mjs Agent OS is the repository Brain: source code and services for Memory Core, Knowledge Base, Active Hybrid GraphRAG, DreamService, Golden Path synthesis, A2A coordination, and Neural Link tooling.

Frontmatter

id	10002
title	Universal Macro Overlay DB Topology
state	Closed
labels	enhancementaiarchitecture
assignees	[]
createdAt	Apr 14, 2026, 5:10 PM
updatedAt	Apr 16, 2026, 8:44 PM
githubUrl	https://github.com/neomjs/neo/issues/10002
author	tobiu
commentsCount	1
parentIssue	10014
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
contentTrust
projected
quarantined	0
signals	[]
blockedBy	[x] 10003 Standardize Global Vector Embedding Configuration
blocking	[x] 10006 DreamService: Semantic Edge Injection into Native Graph, [x] 10004 DreamService: Omnidirectional Gap Inference Engine
closedAt	Apr 16, 2026, 8:44 PM

Universal Macro Overlay DB Topology

Closed v13.0.0/archive-v13-0-0-chunk-4 enhancementaiarchitecture

tobiu commented on Apr 14, 2026, 5:10 PM

Objective

Transition from rigid file-path based relationship mapping to an Omnidirectional Semantic Distance topology using global macroscopic vectors.

Tasks

Upgrade the manage_knowledge_base generation pipeline to construct a companion macro collection (e.g., neo-knowledge-base-macro).
Ensure this macro collection stores raw, un-chunked, 1:1 semantic embeddings for all structural artifacts (Guides, Tickets, Source Code, Pull Requests, Tests).
Refactor DreamService.mjs to query graph node IDs against this pre-calculated Macro DB, completely eliminating slow, on-the-fly string compilation and LLM embedding loops from the gap inference pipeline.

tobiu cross-referenced by #10014 on Apr 14, 2026, 6:43 PM

tobiu Apr 16, 2026, 8:44 PM

Superseded by Epic #10030.