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	9677
title	Epic Sub: Enhance Neo.collection.Base with Secondary Lookup Indices
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Apr 4, 2026, 2:54 AM
updatedAt	Apr 4, 2026, 3:03 AM
githubUrl	https://github.com/neomjs/neo/issues/9677
author	tobiu
commentsCount	2
parentIssue	9673
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Apr 4, 2026, 3:02 AM

Epic Sub: Enhance Neo.collection.Base with Secondary Lookup Indices

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

Closedenhancementai

tobiu commented on Apr 4, 2026, 2:54 AM

Problem

The Neo.ai.graph.Database currently relies on linear iteration O(E) over the entire edges store to execute getAdjacentNodes() or cascade removeNode(). For large GraphRAG topologies, this guarantees catastrophic performance degradation as the topology grows, since every relationship hop requires scanning every edge in existence.

Proposed Solution

Enhance Neo.collection.Base (or Neo.data.Store) to natively support declarative, reactive secondary map indices.

Introduce a configuration like indices: [{ property: 'source' }, { property: 'target' }].
The collection should internally maintain dedicated Map structures alongside the primary items array and key map.
Mutations (add, remove, update, clear) must automatically maintain these secondary maps.
This unlocks O(1) lookups (e.g., this.edges.indices.source.get(nodeId)) which massively accelerates any graph traversals or relational filtering operations across the Neo.mjs ecosystem.

tobiu added the enhancement label on Apr 4, 2026, 2:54 AM

tobiu added the ai label on Apr 4, 2026, 2:54 AM

tobiu referenced in commit 92d5c3e - "feat: Implement Native Graph Store with O(1) indices (#9677)" on Apr 4, 2026, 3:01 AM

tobiu assigned to @tobiu on Apr 4, 2026, 3:01 AM

tobiu Apr 4, 2026, 3:02 AM

Native Graph Store implementation successfully completed. O(1) multi-map queries integrated via splice interception.

tobiu closed this issue on Apr 4, 2026, 3:02 AM

tobiu Apr 4, 2026, 3:03 AM

Input from Antigravity (Gemini):

✦ ### Architecture Outline for Native Graph Store Implementation

This implementation mitigates linear O(E) search degradation for high-density topographical traversals inside the GraphRAG framework.

Neo.ai.graph.Store: Created as an extension of Neo.data.Store for zero-dependency native mapping without mutating core frameworks.

Secondary Declarative Indices: Added reactive indices_ mapping to construct scalable, multi-property mapping lookups.

splice() Pipeline Orchestration: Intercepted structural mutations via splice() directly to safely generate fast synchronous Map updates in standard associative dictionaries internally without generating bloated event dispatch overhead.

getByIndex() Query Engine: Implements O(1) retrievals resolving standard array extraction utilizing fast Map extraction over graph relationships.

Decoupling Neo.ai.graph.Database: Eradicated the iterative topology searches in favor of instantaneously mapping edge lookups [{ property: 'source' }, { property: 'target' }] dynamically configured during the edge store assignment.

tobiu added parent issue #9673 on Apr 4, 2026, 3:03 AM