A minimalist, universal JavaScript runtime is the perfect fit for this. By
keeping the core engine strictly decoupled from the host environmentmeaning it
has no native DOM or Node.js filesystem bindings out of the boxyou ensure it
can run anywhere, from a browser-based virtual environment to a backend
microservice.

To achieve this "just enough" philosophy, we can design the runtime as a
simple scheduler with a yield/resume mechanic. Instead of using complex
async/await chains that can get messy across different environments, the
execution loop simply halts when it hits a blocking opcode (like INP or a data
fetch) and hands control back to the host process.

Here is a blueprint for a lightweight, modular Semantic Virtual Machine.

1. The Core Engine

This core class handles memory, the program counter (PC), label indexing, and
the execution loop. It knows absolutely nothing about what the commands
actually do; it only knows how to route them to registered handlers.

class SemanticVM { constructor() { this.memory = {}; // Memory handles
this.program = []; // Array of parsed token arrays this.labels = {}; // Map of
LABEL_NAME -> PC index this.pc = 0; // Program Counter this.state = 'IDLE'; //
IDLE, RUNNING, YIELDED, HALTED // The modular registry for commands
this.opcodes = {}; } // Register a new command module registerOpcode(opcode,
handlerFunction) { this.opcodes[opcode.toUpperCase()] = handlerFunction; } //
Parse the flat text into tokens and index the labels load(scriptText) {
this.program = []; this.labels = {}; const lines = scriptText.split('
'); for (let i = 0; i < lines.length; i++) { let line = lines[i].trim(); if
(!line || line.startsWith('#')) continue; // Basic regex to split by space but
keep quoted strings together let tokens = line.match(/(?:[^\s"]+|"[^"]*")+/g).m
ap(t => t.replace(/(^"|"$)/g, '')); if (tokens[0] === 'LBL') {
this.labels[tokens[1]] = this.program.length; } else { this.program.push(tokens
); } } this.pc = 0; } // The main execution scheduler run() { this.state =
'RUNNING'; while (this.pc < this.program.length && this.state === 'RUNNING') {
const tokens = this.program[this.pc]; const opcode = tokens[0]; const args =
tokens.slice(1); if (this.opcodes[opcode]) { // The handler receives the VM
context and the arguments this.opcodes[opcode](this, args); } else { throw new
Error(`Execution fault: Unknown Opcode '${opcode}' at PC ${this.pc}`); } //
Only increment if the opcode didn't yield or jump if (this.state === 'RUNNING'
&& tokens === this.program[this.pc]) { this.pc++; } } if (this.pc >=
this.program.length && this.state !== 'YIELDED') { this.state = 'HALTED'; } }
// Used by the host environment to push data back into the VM
resume(targetVariable, value) { if (targetVariable) { this.memory[targetVariabl
e] = value; } this.state = 'RUNNING'; this.pc++; // Move past the yielding
instruction this.run(); } } 

2. Registering the Instruction Set

Because the engine is entirely modular, you inject the standard library from
the outside. This allows you to easily swap out how OUT behaves depending on
whether you are rendering to a browser canvas, a remote WebSocket viewer, or a
Node terminal.

const vm = new SemanticVM(); // --- STANDARD MEMORY LOGIC ---
vm.registerOpcode('VAR', (ctx, args) => { const [name, type, val] = args;
ctx.memory[name] = (type === 'NUM') ? parseFloat(val) : val; });
vm.registerOpcode('SET', (ctx, args) => { const [name, val] = args;
ctx.memory[name] = val; // In a strict system, you'd check type matching here
}); // --- STANDARD CONTROL FLOW --- vm.registerOpcode('JMP', (ctx, args) => {
ctx.pc = ctx.labels[args[0]]; }); vm.registerOpcode('JMP_IF', (ctx, args) => {
const [varName, condition, val, targetLabel] = args; let isTrue = false; if
(condition === 'IS') isTrue = (ctx.memory[varName] === val); if (condition ===
'NOT') isTrue = (ctx.memory[varName] !== val); if (isTrue) ctx.pc =
ctx.labels[targetLabel]; }); // --- I/O AND YIELD MECHANICS ---
vm.registerOpcode('OUT', (ctx, args) => { // In Node, this might be
console.log. In browser, an HTML append. console.log(`[VM OUTPUT]:
${args[0]}`); }); vm.registerOpcode('INP', (ctx, args) => { const [targetVar]
= args; ctx.state = 'YIELDED'; // Alert the host environment that we need
input console.log(`[VM YIELD]: Waiting for input to populate '${targetVar}'...`
); }); 

3. Execution Example

Here is how the host environment interacts with the running bytecode.

const FSO_SCRIPT = ` VAR COUNTER NUM 0 OUT "Starting the system..." LBL
LOOP_START INP USER_COMMAND JMP_IF USER_COMMAND IS "EXIT" END_PROGRAM OUT "You
typed something!" JMP LOOP_START LBL END_PROGRAM OUT "Shutting down." `; //
Load and run the program vm.load(FSO_SCRIPT); vm.run(); // The VM will run
until the first INP command and then stop. // The host app can resume it later
(e.g., triggered by a UI button click or API response) setTimeout(() => {
vm.resume('USER_COMMAND', 'HELLO'); }, 1000); // And resume it again to
trigger the exit condition setTimeout(() => { vm.resume('USER_COMMAND',
'EXIT'); }, 2000); 

Architectural Benefits of this Approach

Zero Dependency: It requires no external modules or polyfills. It is pure ES6.

Instant Serialization: Because vm.memory is just a flat POJO (Plain Old
JavaScript Object) and vm.pc is an integer, saving the exact execution state
mid-run is as simple as JSON.stringify({ memory: vm.memory, pc: vm.pc }).

Extensible I/O: Adding an opcode like FETCH_DATA is trivial. The handler just
sets ctx.state = 'YIELDED' and fires off a native fetch() or a Cloudflare
Worker request, calling vm.resume() when the data returns.

How do you envision handling the translation step? Should the local LLM
generate this FSO script entirely as a raw string, or do you see a benefit in
having the LLM output it wrapped in a structured envelope (like a Markdown
block or a lightweight JSON wrapper) to ensure the parser knows exactly where
the executable payload begins and ends?