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	9741
title	Feature: DreamService Codebase Gap Analysis (Automated Doc Alerting)
state	Closed
labels	enhancementai
assignees	tobiu
createdAt	Apr 6, 2026, 8:15 PM
updatedAt	Apr 6, 2026, 8:46 PM
githubUrl	https://github.com/neomjs/neo/issues/9741
author	tobiu
commentsCount	1
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[]
blocking	[]
closedAt	Apr 6, 2026, 8:46 PM

Feature: DreamService Codebase Gap Analysis (Automated Doc Alerting)

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

Closedenhancementai

tobiu commented on Apr 6, 2026, 8:15 PM

Description

Currently, the SQLite Hebbian Memory Integration (Hybrid GraphRAG) pipeline digests episodic memories (conversational sessions) and extracts topological conflicts based on past agent dialogue.

However, the active source code, tests, and guides are sitting in a secondary dimension. If agents build massive structural features over several sessions, the Memory Core knows about it contextually. But if the team forgets to document it, the system doesn't natively detect that the resulting artifacts (source code or markdown in learn/guides/) are missing.

Objective

We need to enhance runSandman.mjs (the REM Extraction pipeline) to:

Correlate with Knowledge Base JSONL: Have the MemoryService cross-reference active .neo-ai-data/neo.db nodes against the embedded knowledge base (the JSONL chunk output of current repository state).
Gap Detection: Ask gemma4 to analyze if high-density episodic achievements lack corresponding markdown/source nodes in the core knowledge base.
Actionable Outputs: If a guide or test is missing, inject a highly weighted "Documentation Gap" or "Test Gap" alert directly into sandman_handoff.md.

tobiu added the enhancement label on Apr 6, 2026, 8:16 PM

tobiu added the ai label on Apr 6, 2026, 8:16 PM

tobiu assigned to @tobiu on Apr 6, 2026, 8:22 PM

tobiu referenced in commit 5a573af - "feat: Implement FileSystem topological ingestion and capability gap analysis inference (#9741)" on Apr 6, 2026, 8:45 PM

tobiu Apr 6, 2026, 8:46 PM

I have implemented the FileSystemIngestor utilizing the codebase gap analysis logic to inject documentation and test alerts correctly. The failing unit tests preventing deployment have been fixed (GraphService properties payload mapping normalized natively). The REM pipeline logic should now activate securely.

tobiu closed this issue on Apr 6, 2026, 8:46 PM