What is the Neural Link?

The Neural Link is a bi-directional bridge that connects AI agents directly to the Neo.mjs runtime. It lets agents inspect the Scene Graph, component state, event listeners, computed styles, and DOM rectangles, and mutate the running application in real time.

Why is Neo.mjs called an Application Engine instead of a framework?

Neo.mjs maintains persistent application objects in a worker-backed Scene Graph instead of compiling application state away into ephemeral DOM nodes. That architecture enables multi-window orchestration, runtime permutation, and deep AI introspection.

What is Context Engineering?

Context Engineering shapes the information and tool environment around AI agents. Neo.mjs implements it through Knowledge Base, Memory Core, GitHub Workflow, and Neural Link MCP servers for frontier harnesses, plus a File System MCP server for internal Neo.ai.Agent local loops.

What is the Neo.mjs Agent OS?

The Neo.mjs Agent OS is the repository Brain: source code and services for Memory Core, Knowledge Base, Active Hybrid GraphRAG, DreamService, Golden Path synthesis, A2A coordination, and Neural Link tooling.

Frontmatter

id	13157
title	create_component (Slice 2): multi-window targeting + get_component_tree visibility + E2E proof
state	Closed
labels	enhancementaitestingarchitecture
assignees	neo-opus-vega
createdAt	Jun 14, 2026, 2:01 AM
updatedAt	Jun 14, 2026, 9:45 AM
githubUrl	https://github.com/neomjs/neo/issues/13157
author	neo-opus-vega
commentsCount	3
parentIssue	null
subIssues	[]
subIssuesCompleted	0
subIssuesTotal	0
blockedBy	[x] 9846 feat: Implement `create_component` Neural Link Tool
blocking	[]
closedAt	Jun 14, 2026, 9:45 AM

create_component (Slice 2): multi-window targeting + get_component_tree visibility + E2E proof

Closed v13.1.0/archive-v13-1-0-chunk-2 enhancementaitestingarchitecture

neo-opus-vega commented on Jun 14, 2026, 2:01 AM

Context

Slice 2 of #9846 (split during the #13154 review, per @neo-gpt's close-target audit). Slice 1 — the create_component NL tool (schema-validated, write-locked, delegates to call_method as container.add(config)) — shipped in #13154 with unit coverage. This ticket carries the live-runtime + E2E verification of the shipped tool.

Release classification: boardless — Agent OS / Neural Link enhancement, not a v13 release blocker.

The Problem

Slice 1's create_component resolves the target via sessionId (parity with every NL tool) and is unit-tested by mocking the transport. It does NOT yet: (a) verify the created component is queryable / visible afterward, or (b) prove the create end-to-end against a live App Worker.

The Fix

get_component_tree visibility: confirm a component created via create_component appears in get_component_tree / is queryable in the live app.
E2E proof: an e2e that drives a live app, calls create_component, and inspects the result (via the NeuralLink fixture + a real App Worker).
(Multi-window targeting moved to #13185 — see re-scope below.)

Acceptance Criteria (re-scoped per @neo-gpt's #13183 review)

AC2: a component created via create_component appears in get_component_tree results — delivered in #13183.
AC3: an E2E validates create + subsequent inspection against a live App Worker — delivered in #13183.
AC1 (explicit multi-window targeting when useSharedWorkers: true) — moved to #13185 (a direct shared-worker multi-window e2e). It is belt-and-suspenders: the mechanism is sound by composition (create_component delegates to the window-agnostic, id-based call_method; globally-unique ids reuse the App Worker's existing per-window routing). #13157 is now scoped to the delivered AC2 + AC3.

Out of Scope

The tool itself, schema-validation, and the write-locked tier (delivered in #13154).
The direct multi-window e2e (AC1) — #13185.

Implementation note

The NL-service e2e/unit setup must set the neural-link config autoConnect=false before importing ConnectionService (else it spawns a real Bridge at import — see #13154's isolation fix).

Related: #9846, #13154, #13106, #13183, #13185

Origin Session ID: 4cc428e3-cf36-4324-8646-1b96cb23fa4a