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	9334
title	Performance: Optimize Collection `items_` config to use shallow cloning
state	Closed
labels	enhancementaiperformance
assignees	tobiu
createdAt	Feb 27, 2026, 5:34 PM
updatedAt	Feb 27, 2026, 6:13 PM
githubUrl	https://github.com/neomjs/neo/issues/9334
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Feb 27, 2026, 6:13 PM

Performance: Optimize Collection `items_` config to use shallow cloning

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

Closed v12.0.0 enhancementaiperformance

tobiu commented on Feb 27, 2026, 5:34 PM

Problem

The items_ config in Neo.collection.Base was defaulting to clone: 'deep' (the default for reactive configs in Neo.mjs). For large datasets (e.g., 50,000 records in the DevIndex app), setting or initializing items_ triggered a deep clone of the entire array and every single record object within it. This caused a massive performance bottleneck, specifically an ~800ms delay when the allItems collection was created during the first filter operation.

Solution

Updated the items_ config to use a descriptor with clone: 'shallow'. This ensures that only the array structure itself is copied (preserving immutability for the collection array) while the individual record objects maintain their exact references. This drops the items assignment overhead for 50k records from ~779ms down to ~6ms, eliminating the bottleneck while ensuring updates to records propagate correctly between filtered and unfiltered states.

tobiu added the enhancement label on Feb 27, 2026, 5:34 PM

tobiu added the ai label on Feb 27, 2026, 5:34 PM

tobiu added the performance label on Feb 27, 2026, 5:34 PM

tobiu referenced in commit bcd697d - "enhancement: Performance: Optimize Collection items_ config to use shallow cloning (#9334)" on Feb 27, 2026, 5:37 PM

tobiu referenced in commit 06ae779 - "enhancement: Performance: Optimize Collection items_ config to use shallow cloning (#9334)" on Feb 27, 2026, 6:10 PM

tobiu assigned to @tobiu on Feb 27, 2026, 6:11 PM

tobiu Feb 27, 2026, 6:11 PM

Input from Gemini 3.1 Pro:

✦ I have resolved this issue.

We discovered that Neo.cloneMap.Object was being triggered repeatedly for all 50k records and their properties because the items_ config in Neo.collection.Base lacked a clone descriptor, causing it to fall back to the default clone: 'deep' mechanism on assignment.

We fixed this by providing a config descriptor for items_ in src/collection/Base.mjs explicitly setting clone: 'shallow'. This avoids the expensive full depth clone, performing just [...value] to ensure collection immutability while preserving standard object references for the individual items.

We also fixed a synchronization issue where the initial afterSetSourceId hook wasn't updating me.count after cloning the source collection data directly.

A performance regression test was created in test/playwright/unit/app/devindex/StoreFilterProfile.spec.mjs with an explicit assertion to enforce that the initial hydration/filter creation process takes less than 400ms (down from the ~1000ms+ range previously).

The issue is resolved and the relevant unit tests all pass!

tobiu closed this issue on Feb 27, 2026, 6:13 PM

Performance: Optimize Collection items_ config to use shallow cloning

Problem

Solution

Performance: Optimize Collection `items_` config to use shallow cloning