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	9049
title	Docs: Knowledge Base Enhancement for DevRank Services
state	Closed
labels	documentationai
assignees	tobiu
createdAt	Feb 7, 2026, 11:43 PM
updatedAt	Feb 7, 2026, 11:51 PM
githubUrl	https://github.com/neomjs/neo/issues/9049
author	tobiu
commentsCount	1
parentIssue	8930
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 7, 2026, 11:51 PM

Docs: Knowledge Base Enhancement for DevRank Services

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

Closed v12.0.0 documentationai

tobiu commented on Feb 7, 2026, 11:43 PM

Apply the Knowledge Base Enhancement Strategy to the DevRank Backend Services.

Goal: Improve the discoverability and maintainability of the DevRank backend code by adding comprehensive, intent-driven JSDoc comments to all classes and methods in apps/devrank/services/.

Scope:

Manager.mjs
Spider.mjs
Updater.mjs
Cleanup.mjs
Storage.mjs
GitHub.mjs
config.mjs

Requirements:

Class Comments: Add @summary, detailed architectural description, and @see links. Explain why the service exists and how it fits into the data lifecycle.
Method Comments: Ensure all methods have JSDoc with:
- @param and @returns tags.
- Intent-driven descriptions (e.g., "Enforces data hygiene by..." instead of "Runs cleanup").
Searchability: Include keywords like "discovery", "enrichment", "pruning", "rate limit" in the comments to aid the Knowledge Base vector search.

References:

.github/AGENTS_STARTUP.md (Knowledge Base Enhancement Strategy)

tobiu added the documentation label on Feb 7, 2026, 11:43 PM

tobiu added the ai label on Feb 7, 2026, 11:43 PM

tobiu added parent issue #8930 on Feb 7, 2026, 11:43 PM

tobiu assigned to @tobiu on Feb 7, 2026, 11:46 PM

tobiu referenced in commit 49d674b - "docs: Knowledge Base Enhancement for DevRank Services (#9049)" on Feb 7, 2026, 11:50 PM

tobiu Feb 7, 2026, 11:50 PM

Input from Gemini 3 Pro:

✦ Enhanced the DevRank Backend Services with comprehensive JSDoc comments to improve Knowledge Base discoverability.

Updates

Cleanup.mjs: Documented pruning strategies, whitelist protection, and resurrection logic.

Config.mjs: Explained the configuration proxy pattern.

GitHub.mjs: Detailed the dual-protocol (GraphQL/REST) approach and authentication hierarchy.

Manager.mjs: Documented the CLI lifecycle, hygiene orchestration, and smart scheduling.

Spider.mjs: Explained the Random Walk strategies (Deep Slicing, Temporal, Keyword) and "Filter Bubble" mitigation.

Storage.mjs: Described the persistence model, atomic writes, and data normalization.

Updater.mjs: Documented the enrichment workflow, bot handling, and active pruning triggers.

Code committed to dev branch.

tobiu closed this issue on Feb 7, 2026, 11:51 PM