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	9828
title	Documentation: Create Whitebox E2E Testing Guide (Neural Link)
state	Closed
labels	[]
assignees	tobiu
createdAt	Apr 9, 2026, 5:49 PM
updatedAt	Apr 9, 2026, 5:53 PM
githubUrl	https://github.com/neomjs/neo/issues/9828
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Apr 9, 2026, 5:53 PM

Documentation: Create Whitebox E2E Testing Guide (Neural Link)

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

Closed[]

tobiu commented on Apr 9, 2026, 5:49 PM

Background

Recent sessions have successfully established the foundational whitebox E2E testing infrastructure using Playwright and the new Neural Link bridge. We have transitioned from basic component testing to complex "Level 3" virtualized grid architecture testing (GridContainer, ControlsContainer, cross-component state synchronization).

While we have comprehensive guides for unit, component, and benchmarking, there is currently no documentation on Whitebox E2E Testing.

Objective

Create the first iteration of the Whitebox E2E Testing Guide under learn/guides/testing/WhiteboxE2E.md and hook it into the learning application (learn/tree.json).

Scope

Explain the Why: Briefly contrast traditional blackbox E2E (flaky, unreliable) with Neural Link Whitebox E2E (query component instances, read state directly, simulate exact native DOM events).
Setup: Document how to hook up an application (neo-config.json -> "useAiClient": true).
Core API / Primitives: Example of NeuralLink_ComponentService.queryComponent and how to read properties (like value), specifically handling differences in return depth.
Example Walkthrough: Reference the newly stabilized GridBigDataNL.spec.mjs test to show an end-to-end example of connecting, finding a ComboBox, verifying state, and disconnecting.

tobiu referenced in commit ad7a13a - "docs: Add Whitebox E2E testing guide (#9828)" on Apr 9, 2026, 5:53 PM

tobiu closed this issue on Apr 9, 2026, 5:53 PM

tobiu assigned to @tobiu on Apr 9, 2026, 5:53 PM

tobiu Apr 9, 2026, 5:53 PM

Input from Antigravity (Gemini 3.1 Pro):

✦ I have successfully authored the Whitebox E2E testing guide, hooked it into learn/tree.json, and linked to it dynamically from learn/guides/testing/Overview.md. The code changes to document this baseline functionality are committed and pushed. I'm assigning this to myself and closing the ticket.