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	8484
title	Optimize uploadKnowledgeBase script: Early Exit and Robust Cleanup
state	Closed
labels	enhancementaibuild
assignees	tobiu
createdAt	Jan 9, 2026, 10:18 PM
updatedAt	Jan 9, 2026, 10:22 PM
githubUrl	https://github.com/neomjs/neo/issues/8484
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Jan 9, 2026, 10:22 PM

Optimize uploadKnowledgeBase script: Early Exit and Robust Cleanup

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

Closed v11.20.0 enhancementaibuild

tobiu commented on Jan 9, 2026, 10:18 PM

buildScripts/uploadKnowledgeBase.mjs needs two improvements:

Cleanup Logic: The current finally block might not execute if the process exits early via process.exit(1) inside the catch block. We should ensure the zip file is always cleaned up, even on failure. The process.exit(1) calls should likely be removed or moved to the end, or we should use a more robust cleanup mechanism (like listening for exit events or just restructuring the flow).
Execution Order: The script currently creates the large zip file before checking if the GitHub release exists. This is inefficient. We should check for the existence of the release tag on GitHub first. If the tag is missing, we should abort immediately without spending time and disk I/O creating the zip.

Proposed Flow:

Check if chroma-neo-knowledge-base exists locally.
Check if the target release tag exists on GitHub.
Zip the knowledge base.
Upload to GitHub.
Cleanup (delete zip).

tobiu added the enhancement label on Jan 9, 2026, 10:18 PM

tobiu added the ai label on Jan 9, 2026, 10:18 PM

tobiu added the build label on Jan 9, 2026, 10:18 PM

tobiu assigned to @tobiu on Jan 9, 2026, 10:19 PM

tobiu referenced in commit 1135f12 - "refactor: Optimize uploadKnowledgeBase flow and cleanup (#8484)" on Jan 9, 2026, 10:20 PM

tobiu referenced in commit 7ac2ba2 - "fix: Ensure zip cleanup runs by deferring process.exit (#8484)" on Jan 9, 2026, 10:22 PM

tobiu Jan 9, 2026, 10:22 PM

Input from Gemini 3 Pro:

✦ I have applied a critical fix to the script logic. process.exit(1) inside the catch block would terminate the process immediately, bypassing the finally block and leaving the zip file behind.

I replaced this with an exitCode variable that is checked at the very end of the function, ensuring that the cleanup logic in finally always executes before the process terminates.

Fix applied and pushed to dev.

tobiu closed this issue on Jan 9, 2026, 10:22 PM