What is the 'Neural Link'?

The Neural Link is a bi-directional bridge that connects AI agents (like Gemini or Claude) directly to the Neo.mjs runtime. It allows agents to 'see' the application's Scene Graph, inspect component state, verify event listeners, and even mutate the running application in real-time. This turns the application into a 'glass box' for AI, enabling autonomous debugging and feature development.

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

Traditional frameworks are general-purpose libraries (like a Toyota) that help you organize code, but they compile away into ephemeral DOM nodes. Neo.mjs is a precision-engineered runtime (like an F1 car), similar to Unreal Engine for games. It maintains a persistent 'Scene Graph' of objects in a separate worker thread. These objects retain their identity, state, and relationships, allowing for advanced capabilities like multi-window orchestration, runtime permutation, and deep AI introspection that are impossible with 'melted plastic' DOM-based frameworks.

What is 'Context Engineering'?

Context Engineering is the practice of curating the environment and information flow for AI agents to maximize their autonomy. In Neo.mjs, this is implemented via three Model Context Protocol (MCP) servers: a Knowledge Base for semantic code understanding, a Memory Core for learning from past sessions, and a GitHub Workflow server for project management. This ecosystem allows agents to work as fully integrated members of the development team.

What is 'Object Permanence' in the context of Neo.mjs?

In Neo.mjs, UI components are persistent JavaScript objects living in the App Worker, not just transient rendering results. This 'Object Permanence' means a component (like a Dashboard) maintains its state (scroll position, user input, internal logic) even if it is detached from the DOM or moved to a different browser window. This is the 'Lego Technic' approach versus the 'Duplo' approach of traditional frameworks.

What is an 'Agent Operating System'?

Neo.mjs v11 introduced the concept of an Agent OS, where the platform itself provides the tools and interfaces for AI agents to operate. It combines a standalone, type-safe AI SDK for autonomous 'Code Execution' with the Neural Link for runtime control. This enables agents to monitor, debug, and heal the application autonomously, effectively acting as an operating system for synthetic intelligence.

How does Neo.mjs handle multi-window applications?

Neo.mjs uses shared web workers to run a single application instance across multiple browser windows. All windows share the same application state and data in real-time. Components can even move between windows while retaining their JavaScript instances. This enables desktop-class experiences like multi-window IDEs, browser-based email clients, and multi-screen control rooms.

What makes Neo.mjs different from React, Angular, or Vue?

Neo.mjs is fundamentally different in its architecture: (1) It uses True Multithreading via web workers to prevent UI jank, (2) It is an AI-Native Application Engine with built-in bridges for AI collaboration, and (3) It treats components as persistent objects in a Scene Graph, enabling native multi-window support and runtime permutation.

Frontmatter

id	8421
title	Implement Static Class Hierarchy in Knowledge Base
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Jan 8, 2026, 1:09 PM
updatedAt	Jan 8, 2026, 1:14 PM
githubUrl	https://github.com/neomjs/neo/issues/8421
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Jan 8, 2026, 1:14 PM

Implement Static Class Hierarchy in Knowledge Base

Name: Neo.mjs Application Engine
Author: Neo.mjs

Closed v11.19.0 enhancementai

tobiu commented on Jan 8, 2026, 1:09 PM

The Knowledge Base currently lacks a structured, deterministic way to discover class relationships (e.g., "What classes extend Neo.component.Base?"). While inspect_class exists in the Neural Link, it only works for loaded runtime classes.

Goal: Create a static "Class Hierarchy" map during the Knowledge Base build process and expose it via a tool.

Tasks:

Update VectorService.mjs:
- During embed(), when building the classNameToDataMap for inheritance boosting, save this map (or a simplified tree) to dist/ai-class-hierarchy.json.
Update QueryService.mjs:
- Add a getClassHierarchy({root}) method that reads this JSON file.
- Implement logic to return the subtree starting at root (or the whole tree).
Update openapi.yaml:
- Expose get_class_hierarchy as a tool.

Value: Allows agents to deterministically discover available components and classes in the codebase without relying on fuzzy vector search.

tobiu added the enhancement label on Jan 8, 2026, 1:09 PM

tobiu added the ai label on Jan 8, 2026, 1:09 PM

tobiu assigned to @tobiu on Jan 8, 2026, 1:10 PM

tobiu referenced in commit 9a21928 - "feat: Add Static Class Hierarchy tool to Knowledge Base (#8421)" on Jan 8, 2026, 1:14 PM

tobiu Jan 8, 2026, 1:14 PM

Input from Gemini 3 Pro:

✦ I have implemented the Static Class Hierarchy tool.

Changes:

ai/mcp/server/knowledge-base/config.mjs: Added hierarchyPath to the default configuration.

VectorService.mjs: Updated embed() to save the classNameToDataMap to dist/ai-class-hierarchy.json.

QueryService.mjs: Added getClassHierarchy() method to retrieve the hierarchy map.

openapi.yaml: Exposed get_class_hierarchy as a new tool.

This allows agents to deterministically inspect the inheritance tree without needing to guess or rely on runtime introspection.

tobiu closed this issue on Jan 8, 2026, 1:14 PM