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	9438
title	TreeStore: Reduce GC pressure and redundant iterations
state	Closed
labels	enhancementaiperformancegrid
assignees	tobiu
createdAt	Mar 11, 2026, 11:09 AM
updatedAt	Mar 11, 2026, 11:14 AM
githubUrl	https://github.com/neomjs/neo/issues/9438
author	tobiu
commentsCount	1
parentIssue	9404
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Mar 11, 2026, 11:14 AM

TreeStore: Reduce GC pressure and redundant iterations

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

Closed v12.1.0 enhancementaiperformancegrid

tobiu commented on Mar 11, 2026, 11:09 AM

Goal

Optimize TreeStore.mjs to minimize Garbage Collection (GC) pressure and avoid redundant operations, specifically targeting large dataset performance.

Context

During operations like filter, doSort, and bulk expands, the TreeStore can trigger thousands of array allocations and function invocations, significantly stressing the GC.

Acceptance Criteria

Refactor updateSiblingStats() to remove the .filter() array allocation, using a two-pass counter instead.
Hoist activeFilters calculation in filter() to avoid duplicate iteration.
Create a reusable private method #rebuildKeysAndCount() that pre-allocates the _keys array instead of using .map(). Use this method in doSort, filter, clearFilters, expandAll, and collapseAll.
Ensure all tests still pass.

tobiu added the enhancement label on Mar 11, 2026, 11:09 AM

tobiu added the ai label on Mar 11, 2026, 11:09 AM

tobiu added the performance label on Mar 11, 2026, 11:09 AM

tobiu added the grid label on Mar 11, 2026, 11:09 AM

tobiu added parent issue #9404 on Mar 11, 2026, 11:09 AM

tobiu assigned to @tobiu on Mar 11, 2026, 11:09 AM

tobiu Mar 11, 2026, 11:13 AM

Input from Gemini 3.1 Pro:

✦ I have applied all the requested optimizations:

Added #rebuildKeysAndCount() to pre-allocate the _keys array, eliminating the .map() overhead and array reallocations in clearFilters, collapseAll, doSort, expandAll, and filter.

Hoisted the activeFilters array evaluation to the top of filter(), avoiding a duplicate iteration.

Completely rewrote updateSiblingStats() to use a two-pass counting and indexing loop, eliminating the .filter() call that was previously creating thousands of short-lived arrays.

All unit tests pass successfully. The commit has been pushed. Closing this optimization sub-ticket.

tobiu referenced in commit b5f367c - "perf(data.TreeStore): Reduce GC pressure and redundant iterations (#9438)" on Mar 11, 2026, 11:13 AM

tobiu closed this issue on Mar 11, 2026, 11:14 AM