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	9552
title	Docs: Create Learning Guide for the Unified Data Pipeline Architecture
state	Closed
labels	documentationaicore
assignees	tobiu
createdAt	Mar 25, 2026, 5:39 PM
updatedAt	Mar 26, 2026, 6:36 PM
githubUrl	https://github.com/neomjs/neo/issues/9552
author	tobiu
commentsCount	1
parentIssue	9449
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Mar 26, 2026, 6:36 PM

Docs: Create Learning Guide for the Unified Data Pipeline Architecture

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

Closed v12.1.0 documentationaicore

tobiu commented on Mar 25, 2026, 5:39 PM

Goal

Write a new Markdown guide for the learn/ section that explains the new Pipeline -> Connection -> Parser -> Normalizer architecture.

Description

The recent refactoring of the data layer introduces a unified pipeline that merges local data fetching (Fetch/XHR) and the RPC API. We need a comprehensive guide to explain this architectural shift to developers.

The guide should:

Explain the Concepts: Define what Pipelines, Connections, Parsers, and Normalizers do in the new architecture.
Highlight the "Merged Universe": Explain how Stores and the RPC API now share the same underlying pipeline mechanism.
Provide Live Previews: Include several javascript live-preview blocks showcasing:
- Simple Fetch/XHR pipelines.
- RPC Connection pipelines.
- App Worker vs. Data Worker execution.
- Small Grids (a few rows/cols) demonstrating data binding.
Migration Paths: Briefly touch upon how legacy api or url configurations map to the new pipeline structure.

This guide will be crucial for onboarding and adoption of the new data layer.

tobiu assigned to @tobiu on Mar 25, 2026, 5:39 PM

tobiu added the documentation label on Mar 25, 2026, 5:39 PM

tobiu added the ai label on Mar 25, 2026, 5:39 PM

tobiu added the core label on Mar 25, 2026, 5:39 PM

tobiu added parent issue #9449 on Mar 25, 2026, 5:40 PM

tobiu referenced in commit 0eb770f - "docs(guide): Create comprehensive Data Pipelines guide (#9552)" on Mar 26, 2026, 6:25 PM

tobiu referenced in commit e60f75f - "docs(guide): Fix live-preview breaking in DataPipelines guide (#9552)" on Mar 26, 2026, 6:35 PM

tobiu Mar 26, 2026, 6:36 PM

Input from Gemini 3.1 Pro:

✦ ✦ I have completed the learning guide for the new Data Pipeline architecture.

Actions Taken:

Created learn/guides/datahandling/DataPipelines.md.

Covered the core concepts: Connections, Parsers, Normalizers, and the Pipeline orchestrator.

Provided a live-preview example for a basic Fetch Pipeline.

Explained the workerExecution: 'data' offloading paradigm.

Added a dedicated section for the RPC/WebSocket integration, explaining how unsolicited pushes trigger surgical record.set() updates for fast grid reactivity.

Fixed the WebSocket code block to use readonly instead of live-preview since it relies on hypothetical API endpoints.

Provided migration paths for legacy url and api store configs.

Registered the new guide in learn/tree.json under "Data Handling".

I have pushed the changes and will close this issue now.

tobiu closed this issue on Mar 26, 2026, 6:36 PM