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	9551
title	Examples: Implement unified Data Pipeline showcases
state	Closed
labels	enhancementaicore
assignees	tobiu
createdAt	Mar 25, 2026, 5:37 PM
updatedAt	Mar 25, 2026, 8:27 PM
githubUrl	https://github.com/neomjs/neo/issues/9551
author	tobiu
commentsCount	1
parentIssue	9449
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Mar 25, 2026, 8:27 PM

Examples: Implement unified Data Pipeline showcases

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

Closed v12.1.0 enhancementaicore

tobiu commented on Mar 25, 2026, 5:37 PM

Goal

Create new working examples in the examples/data/ folder to demonstrate the unified Data Pipeline architecture.

Description

Since the data architecture has been overhauled to use a Pipeline -> Connection -> Parser -> Normalizer flow, we need to provide concrete, working examples for developers.

These examples should showcase:

App Worker Pipeline: An example using a lightweight Grid driven by an App Worker pipeline.
Data Worker Pipeline: An example showing remote execution of a pipeline inside the Data Worker.
Connection Types: Examples covering different connection types like Fetch and the new Rpc connection.
Data Shaping: Demonstrations of Parser and Normalizer configurations.

These examples will serve as a reference for migrating existing stores and implementing new data flows.

tobiu assigned to @tobiu on Mar 25, 2026, 5:37 PM

tobiu added the enhancement label on Mar 25, 2026, 5:37 PM

tobiu added the ai label on Mar 25, 2026, 5:37 PM

tobiu added the core label on Mar 25, 2026, 5:37 PM

tobiu added parent issue #9449 on Mar 25, 2026, 5:39 PM

tobiu referenced in commit e872704 - "feat: Implement folder-scoped dynamic loading for Data Worker pipelines (#9551)" on Mar 25, 2026, 8:26 PM

tobiu Mar 25, 2026, 8:27 PM

Input from Gemini 3.1 Pro:

✦ Completed the implementation of the Unified Data Pipeline showcases and framework enhancements.

Summary of Work:

Framework: Folder-Scoped Dynamic Loading

Enhanced Neo.worker.Data with loadDataModule({className}). This method uses strictly scoped dynamic imports (e.g., ../data/connection/${name}.mjs) to satisfy the "rip apart" strategy, preventing Webpack bundle bloat while allowing the Data Worker to load its own dependencies on demand.

Core: Pipeline IPC Orchestration

Updated Neo.data.Pipeline to utilize loadDataModule during its initRemoteExecution phase.

Core: Instance Lookup Stabilization

Imported Neo.manager.Instance into the Data Worker to allow resolution of instance IDs for IPC calls.

Refactored RemoteMethodAccess to use the Neo.get(id) alias for better resilience.

Example: Data Pipeline Showcase

Created a new tabbed example in examples/data/pipeline/ demonstrating both App Worker and Data Worker pipeline execution.

Registered the example in docs/examples.json.

Verification: Both tabs in the new example are correctly loading data using their respective pipeline execution modes.

tobiu closed this issue on Mar 25, 2026, 8:27 PM