https://github.com/srutz/cagent
Cagent is a coding-agent … a TypeScript implementation of an autonomous agent framework that can interact with language models, execute tools, and perform tasks based on user input.
It is free, opensource and MIT Licensed, so you can do with it as you like.
It designed to be simple and modular. Also it is meant to be a starting point for including it into your own projects. Think of shadcn approach and copy it into your projects, just like I do.
Running it like this will show you how tools actually work from an LLM-Perpective:
cagent --verbose list files in current dir
📑 TABLE OF CONTENTS
Executive Summary
Core Architecture (5-Layer Compiler Pattern)
System Specifications
Implementation Guide
Code Examples & Schemas
Deployment Instructions
Integration for Game Engines
Advanced Features
✅ Structured Narrative Data (Scene, Tone, Intent)
✅ Mechanical Specifications (Stat Blocks, Formulas, Balance)
✅ World State (Persistent Global Variables)
✅ Visual Assets (AI-Ready Image Prompts)
✅ Exportable JSON (Direct Database/Engine Integration)
Key Innovation: Instead of treating AI output as final prose, we treat it as source code that must be compiled into deterministic game systems.
Inputs: User text, scene description, conflict statement Outputs:
Scene Summary (3-sentence description)
Tone Tags (Dark, Heroic, Mystical, etc.)
Conflict Identification (Player vs Enemy, Exploration, Dialogue, etc.)
Story Direction (Next logical plot beat)
Example Processing:
Code
INPUT: “The knight raised his shield as the giant beast charged.”
OUTPUT:
Scene: Battlefield encounter with charging colossus
Tone: Dark Heroic, High Tension
Conflict: Heavy Knight (Defensive) vs Colossal Beast (Offensive)
Direction: Combat Simulation Required
Layer 2: CHARACTER GENERATION SYSTEM
Purpose: Map narrative characters to RPG-compliant stat objects.
Processing:
Extract character archetype (Knight, Mage, Rogue, etc.)
Assign class and subclass from predefined hierarchy
Generate or retrieve attributes (STR, INT, DEX, VIT, etc.)
Assign aura type (Element, Ki, Ether, Divine, Soul)
Calculate derived stats (HP, Mana, Armor, etc.)
Output Schema:
JSON
{
“character”: {
“id”: “char_001”,
“name”: “Valdrin”,
“class”: “Heavy Knight”,
“subclass”: “Ironclad”,
“level”: 12,
“aura_type”: “Earth Ether”,
“attributes”: {
“strength”: 18,
“vitality”: 20,
“dexterity”: 8,
“intelligence”: 6,
“wisdom”: 14,
“charisma”: 10
},
“derived_stats”: {
“max_hp”: 240,
“max_aura”: 150,
“armor_rating”: 22,
“speed”: 4
}
}
}
Layer 3: MECHANICS LAB (DETERMINISTIC LOGIC)
Purpose: Convert narrative actions into mathematical game formulas.
Critical Rule: NO VAGUE DESCRIPTIONS. All effects must be quantifiable.
Processing Pipeline:
Parse action verb (Attack, Defend, Cast, etc.)
Identify ability source (Class, Subclass, Equipment, Passive)
Calculate damage/effect using established formulas
Apply modifiers (Critical, Status, Scaling)
Generate cooldown and resource cost
Example Ability Compilation:
Code
INPUT: “The knight strikes with heavy force”
COMPILED ABILITY:
Name: Crushing Blow
Type: Physical Attack
Damage Formula: (STR * 1.5) + (Weapon_Base * 0.8) + 2d8
Cost: 20 Aura
Cooldown: 2 turns
Status Effect: None
Scaling: +0.1 per level
EXAMPLE OUTPUT (Level 12 Knight):
Base Damage: (18 * 1.5) + (35 * 0.8) + 2d8
= 27 + 28 + 2d8
= 55 + 2d8 (Average: 64 damage)
Damage Formula Standard:
Code
Physical_Damage = (Attacker_STR * Ability_Scaling) + Weapon_Base + d_roll ± Status_Modifiers
Magic_Damage = (Attacker_INT * Ability_Scaling) + Catalyst_Base + d_roll ± Resistance
Healing = (Attacker_WIS * Spell_Scaling) + Base_Heal_Amount
Layer 4: WORLD FORGE (PERSISTENT STATE)
Purpose: Generate and maintain a persistent world graph.
State Tracking:
Current Region / Biome
Active Factions & Reputation
Environmental Hazards
NPC Locations
World Difficulty Scaling (1-10)
Timeline Events
World State Schema:
JSON
{
“world_state”: {
“version”: “2.0”,
“timestamp”: 1704067200,
“current_region”: “Ashfall Valley”,
“active_biome”: “Volcanic Highlands”,
“difficulty_scaling”: 7,
“factions”: {
“Silver_Conclave”: {
“stance_toward_player”: “Hostile”,
“power_level”: 8,
“control_percentage”: 45
},
“Obsidian_Horde”: {
“stance_toward_player”: “Neutral”,
“power_level”: 6,
“control_percentage”: 30
}
},
“active_threats”: [
{
“threat_id”: “dragon_001”,
“type”: “Colossal Beast”,
“location”: “Molten Peak”,
“threat_level”: 9
}
]
}
}
Layer 5: VISION RENDER (VISUAL PROMPTS)
Purpose: Generate high-fidelity AI image prompts compatible with DALL-E 3, Midjourney, Stable Diffusion.
Prompt Template:
Code
[Character/Scene Description]
[Setting & Lighting]
[Style & Medium]
[Emotional Tone]
[Cinematic Elements]
[Technical Quality Modifiers]
Example Output:
Code
Heavy armored knight in slate-blue plate armor standing in a burning battlefield,
glowing molten sword raised, intense cinematic lighting with volumetric smoke,
epic composition, dark heroic fantasy art style, 8k resolution,
detailed armor with runic inscriptions, dynamic action pose,
orange and blue color grading, dramatic shadows
Aura Scaling:
Base_Aura = 100 + (Level * 10)
Aura_Regen = (WIS / 2) + (Level / 4) per turn
Ability_Cost = Base_Cost * (1 + Status_Modifier)
3.2 Class & Subclass Hierarchy
Code
HEAVY KNIGHT
├─ Ironclad (Defense-focused)
│ ├─ Fortress Form
│ ├─ Shield Slam
│ ├─ Absorb Wall
│ ├─ Counter Mass
│ ├─ Plating Pulse
│ └─ Siege Posture
└─ Dreadnought (Offense-focused)
├─ Momentum Charge
├─ Earthfall Blow
├─ Bone Breaker
├─ Impact Field
├─ Ruin Drive
└─ Colossus Mode
RANGER
├─ Marksman (Precision)
└─ Beastmaster (Pet Support)
MAGE
├─ Pyromancer (Fire)
└─ Frostweaver (Ice)
ROGUE
├─ Assassin (Burst Damage)
└─ Trickster (Control)
MONK
├─ Mystic Fist (Chi-Based)
└─ Void Walker (Dimensional)
WARDEN
├─ Sentinel (Guardian)
└─ Oath-Keeper (Bonded)
3.3 Scaling Formulas
Code
HP_Scaling: Base_HP + (Level * VIT * 1.5)
Damage_Scaling: Base_Damage + (Level * 0.5)
Resistance_Scaling: Base_Resistance + (Level * 0.25)
Ability_Cooldown: Base_Cooldown / (1 + Haste_Modifier)
Code
Example: /init “Dark High-Fantasy” 7 “Soul-Ember Aura System”
System Response:
✓ Loaded: Grimdark Fantasy Ruleset
✓ Loaded: Level 7 Enemy Scaling
✓ Loaded: Soul-Ember Resource System
✓ Global State: Initialized
✓ Ready for Input
4.2 Command Structure
Code
/init [Theme] – Initialize world
/inspect [Entity] – Deep-dive stat block
/combat [A] vs [B] – Simulate encounter
/map [Region] – Generate regional overview
/export [Format] – Export data (json/csv/xml)
/log – Summarize session state
/branch [Number] – Execute story path
/balance-check – Verify mechanical balance
/faction-report – Show faction standings
/world-update – Refresh world state
4.3 Full Output Template
Code
🖋️ NARRATIVE BREAKDOWN
Scene: [3-sentence description]
Tone: [Tone tags]
Conflict: [Primary conflict]
Direction: [Next story beat]
👤 CHARACTER LAYER
Name: [Character name]
Class/Subclass: [Class] / [Subclass]
Level: [Current level]
Aura Type: [Aura element]
Key Stat: [Primary attribute and value]
Stats Grid: [All attributes in table format]
⚔️ SYSTEM MECHANICS
Primary Ability: [Ability name]
Type: [Passive/Active/Triggered]
Damage Formula: [Mathematical formula]
Resource Cost: [Aura/Mana cost]
Cooldown: [Turn cooldown]
Status Effects: [Applied effects]
Scaling: [Per-level progression]
Secondary Abilities: [List with formulas]
🗺️ WORLD DATA
Region: [Current region]
Biome: [Biome type]
Faction Influence: [Dominant factions]
Threat Level: [1-10 difficulty]
Environmental Hazards: [Active dangers]
NPC Locations: [Key NPCs and positions]
🎨 VISUAL PROMPT
[High-fidelity DALL-E 3 compatible image prompt]
📟 DATA EXPORT (JSON)
[Complete JSON schema of all above data]
🌳 BRANCHING PATHS
[1] DEEPEN MECHANICS - Expand ability pool [2] WORLD EVOLUTION - Show regional consequences [3] NARRATIVE PROGRESSION - Next story beat [4] COMBAT SIMULATION - Roll for action [5] CHARACTER EXPANSION - Add backstory [6] DIALOGUE TREE - NPC interaction
Code
---
## 5. CODE EXAMPLES & SCHEMAS
### 5.1 Core Agent Initialization (JavaScript)
javascript
class MythOSCompiler {
constructor(config = {}) {
this.config = {
maxContextWindow: config.maxContextWindow || 8000,
difficulty: config.difficulty || 5,
theme: config.theme || ‘High Fantasy’,
auraSystem: config.auraSystem || ‘Elemental’,
…config
};
this.globalState = {
worldVersion: '2.0',
currentRegion: null,
activeFactions: {},
characters: {},
timeline: [],
difficulty: this.config.difficulty
};
this.layers = {
narrative: new NarrativeEngine(),
character: new CharacterGenerator(),
mechanics: new MechanicsLab(),
world: new WorldForge(this.globalState),
visual: new VisualPromptEngine()
};
}
async compile(userInput) {
const compiled = {};
// Layer 1: Extract Narrative
compiled.narrative = await this.layers.narrative.parse(userInput);
// Layer 2: Generate Character Data
compiled.character = await this.layers.character.generate(
compiled.narrative.archetype,
compiled.narrative.keywords
);
// Layer 3: Calculate Mechanics
compiled.mechanics = await this.layers.mechanics.calculate(
compiled.character,
compiled.narrative.actionType
);
// Layer 4: Update World State
compiled.world = await this.layers.world.update(
compiled.narrative,
compiled.mechanics
);
// Layer 5: Generate Visuals
compiled.visual = await this.layers.visual.render(
compiled.narrative,
compiled.character,
compiled.world
);
// Export JSON
compiled.json = this.exportJSON(compiled);
return this.formatOutput(compiled);
}
exportJSON(compiled) {
return {
version: ‘2.0’,
timestamp: Date.now(),
metadata: {
theme: this.config.theme,
difficulty: this.config.difficulty,
auraSystem: this.config.auraSystem
},
layers: {
narrative: compiled.narrative,
character: compiled.character,
mechanics: compiled.mechanics,
world: compiled.world
},
globalState: this.globalState
};
}
formatOutput(compiled) {
return `
🖋️ NARRATIVE BREAKDOWN
${this.formatNarrative(compiled.narrative)}
👤 CHARACTER LAYER
${this.formatCharacter(compiled.character)}
⚔️ SYSTEM MECHANICS
${this.formatMechanics(compiled.mechanics)}
🗺️ WORLD DATA
${this.formatWorld(compiled.world)}
🎨 VISUAL PROMPT
${compiled.visual.prompt}
📟 DATA EXPORT
`json
${JSON.stringify(compiled.json, null, 2)}
`
🌳 BRANCHING PATHS
${this.generateBranches()}
`;
}
// … Additional formatting methods
}
5.2 Character Generation Schema
JavaScript
class CharacterGenerator {
async generate(archetype, keywords) {
const baseStats = this.getArchetypeStats(archetype);
return {
id: this.generateID(),
archetype,
class: this.mapArchetypeToClass(archetype),
subclass: this.selectSubclass(keywords),
level: this.determineLevelFromContext(),
auraType: this.selectAuraType(archetype),
baseAttributes: {
strength: baseStats.STR,
vitality: baseStats.VIT,
dexterity: baseStats.DEX,
intelligence: baseStats.INT,
wisdom: baseStats.WIS,
charisma: baseStats.CHA
},
derivedStats: {
maxHP: this.calculateHP(baseStats),
maxAura: this.calculateAura(baseStats),
armorRating: this.calculateArmor(baseStats),
speed: baseStats.DEX / 4,
damageMultiplier: baseStats.STR / 10
},
abilities: this.generateAbilities(archetype),
equipment: this.generateEquipment(archetype),
passives: this.generatePassives(archetype),
lore: {
backstory: this.generateBackstory(archetype),
faction: this.determineFaction(keywords),
alignment: this.determineAlignment(keywords)
}
};
}
calculateHP(stats) {
return 100 + (stats.VIT * 8) + (stats.Level * stats.VIT);
}
calculateAura(stats) {
return 50 + (stats.Level * 5) + (stats.INT * 2);
}
}
5.3 Mechanics Lab (Damage Calculation)
JavaScript
class MechanicsLab {
calculateDamage(attacker, ability, defender = null) {
const baseDamage = this.parseFormula(ability.damageFormula, attacker);
const diceRoll = this.rollDice(ability.dicePool);
const modifiers = this.calculateModifiers(attacker, ability, defender);
const totalDamage = baseDamage + diceRoll + modifiers;
return {
baseDamage,
diceRoll,
modifiers,
totalDamage,
isCritical: this.rollForCritical(attacker),
statusEffects: ability.statusEffects || []
};
}
parseFormula(formula, attacker) {
// Example formula: “(STR * 1.5) + (Weapon * 0.8) + 10”
return formula
.replace(/STR/g, attacker.strength)
.replace(/INT/g, attacker.intelligence)
.replace(/DEX/g, attacker.dexterity)
.replace(/VIT/g, attacker.vitality)
.replace(/WIS/g, attacker.wisdom)
.replace(/Weapon/g, attacker.weaponPower)
// Then evaluate safely
.split(‘)’).reduce((acc, val) => {
// Safe evaluation with constraints
return acc;
});
}
rollDice(dicePool) {
// Parse “2d8+1” format
const [dice, modifier] = dicePool.split(‘+’);
const [count, sides] = dice.split(‘d’);
let total = 0;
for (let i = 0; i < count; i++) {
total += Math.floor(Math.random() * sides) + 1;
}
return total + (modifier ? parseInt(modifier) : 0);
}
calculateModifiers(attacker, ability, defender) {
let total = 0;
// Scaling modifier
if (ability.scaling) {
total += attacker.level * ability.scaling.perLevel;
}
// Weakness/Resistance
if (defender && ability.damageType) {
const resistance = defender.resistances[ability.damageType] || 0;
total -= total * (resistance / 100);
}
// Status effects on attacker
if (attacker.statusEffects) {
attacker.statusEffects.forEach(effect => {
if (effect.damageModifier) {
total *= (1 + effect.damageModifier);
}
});
}
return total;
}
rollForCritical(attacker) {
const critChance = (attacker.dexterity / 100) * 10;
return Math.random() * 100 < critChance;
}
}
5.4 World Forge (State Management)
JavaScript
class WorldForge {
constructor(initialState) {
this.state = initialState;
this.history = [];
}
async update(narrativeLayer, mechanicsLayer) {
const change = {
timestamp: Date.now(),
narrative: narrativeLayer,
mechanics: mechanicsLayer,
factionImpact: this.calculateFactionImpact(narrativeLayer),
difficultyShift: this.calculateDifficultyShift(mechanicsLayer)
};
this.history.push(change);
this.applyChange(change);
return {
currentRegion: this.state.currentRegion,
activeBiome: this.state.activeBiome,
factionStandings: this.state.activeFactions,
threatLevel: this.state.threatLevel,
environmentalHazards: this.state.hazards,
npcs: this.state.npcLocations
};
}
calculateFactionImpact(narrativeLayer) {
// If player defeats enemy aligned with Faction A
// Faction A loses power, Faction B gains relations
const impacts = {};
if (narrativeLayer.defeated) {
const defeatedFaction = this.getFaction(narrativeLayer.defeated);
if (defeatedFaction) {
impacts[defeatedFaction] = -5; // Power loss
}
}
return impacts;
}
calculateDifficultyShift(mechanicsLayer) {
// After player wins, increase nearby threats by 1-2 levels
if (mechanicsLayer.playerVictory) {
return Math.min(10, this.state.difficulty + 1);
}
return this.state.difficulty;
}
applyChange(change) {
// Update faction standings
Object.entries(change.factionImpact).forEach(([faction, impact]) => {
if (this.state.activeFactions[faction]) {
this.state.activeFactions[faction].powerLevel += impact;
}
});
// Update difficulty
this.state.difficulty = change.difficultyShift;
// Record timeline event
this.state.timeline.push({
timestamp: change.timestamp,
event: change.narrative.summary
});
}
}
5.5 Visual Prompt Engine
JavaScript
class VisualPromptEngine {
async render(narrative, character, world) {
const components = {
subject: this.renderSubject(character),
setting: this.renderSetting(world),
action: this.renderAction(narrative),
mood: this.renderMood(narrative),
technical: this.renderTechnical(world.difficulty)
};
return {
prompt: this.assemblePrompt(components),
tags: this.generateTags(character, world),
style: this.selectStyle(narrative.tone)
};
}
renderSubject(character) {
return ${character.archetype} in detailed ${character.armorType} armor, ;
glowing ${character.auraType} effects, dramatic pose
}
renderSetting(world) {
return ${world.currentRegion} at ${world.timeOfDay}, ;
${world.weather} conditions, ${world.lighting}
}
renderAction(narrative) {
return narrative.actionDescription || ‘standing in battle-ready stance’;
}
renderMood(narrative) {
const moodMap = {
‘dark’: ‘ominous shadows, desaturated colors, grim atmosphere’,
‘heroic’: ‘golden lighting, dramatic composition, triumphant pose’,
‘mystical’: ‘ethereal glow, magical particles, surreal environment’,
‘tense’: ‘sharp focus, high contrast, dynamic motion blur’
};
return moodMap[narrative.tone] || ‘cinematic lighting’;
}
renderTechnical(difficulty) {
const qualityLevels = {
1: ‘4k resolution’,
5: ‘8k resolution, high detail’,
10: ‘8k resolution, ultra high detail, photorealistic’
};
return qualityLevels[Math.ceil(difficulty / 2)] || ‘8k resolution’;
}
assemblePrompt(components) {
return
${components.subject}, ${components.action},
${components.setting},
${components.mood},
${components.technical},
professional fantasy art, cinematic masterpiece
}
generateTags(character, world) {
return [
character.class,
character.auraType,
world.currentRegion,
world.biome,
‘fantasy’,
‘cinematic’
];
}
}
Directory structure
project/
├── core/
│ ├── myth-os-compiler.js
│ ├── narrative-engine.js
│ ├── character-generator.js
│ ├── mechanics-lab.js
│ ├── world-forge.js
│ └── visual-prompt-engine.js
├── database/
│ ├── schemas.js
│ ├── classes.json
│ ├── abilities.json
│ └── world-data.json
├── api/
│ ├── routes.js
│ └── middleware.js
├── server.js
└── .env
6.2 Express Server Setup
JavaScript
// server.js
const express = require(‘express’);
const cors = require(‘cors’);
const MythOSCompiler = require(‘./core/myth-os-compiler’);
const app = express();
app.use(express.json());
app.use(cors());
const compiler = new MythOSCompiler({
difficulty: 5,
theme: ‘Dark High-Fantasy’,
auraSystem: ‘Elemental’
});
app.post(‘/api/compile’, async (req, res) => {
try {
const { input, command } = req.body;
if (command === '/init') {
compiler.initialize(input);
return res.json({ status: 'Initialized', globalState: compiler.globalState });
}
const output = await compiler.compile(input);
res.json(output);
} catch (error) {
res.status(500).json({ error: error.message });
}
});
app.get(‘/api/export’, (req, res) => {
const json = compiler.exportJSON(compiler.lastCompiled);
res.setHeader(‘Content-Type’, ‘application/json’);
res.setHeader(‘Content-Disposition’, ‘attachment; filename=world-export.json’);
res.send(JSON.stringify(json, null, 2));
});
app.listen(3000, () => console.log(‘MYTH-OS running on port 3000’));
6.3 Frontend Integration (HTML/React)
HTML
<!DOCTYPE html>
MYTH-OS Agent Interface
<br>
* { margin: 0; padding: 0; box-sizing: border-box; }<br>
body { font-family: 'Cinzel', serif; background: #070604; color: #f0e0c0; }</p>
<div class=”highlight”><pre class=”highlight plaintext”><code>.container {
max-width: 1400px;
margin: 0 auto;
padding: 2rem;
}
.command-input {
display: flex;
gap: 1rem;
margin-bottom: 2rem;
}
.command-input input {
flex: 1;
padding: 0.8rem;
background: #1a1208;
border: 2px solid #c8960a;
color: #f0e0c0;
font-family: inherit;
border-radius: 2px;
}
.command-input button {
padding: 0.8rem 1.5rem;
background: linear-gradient(135deg, #c8960a, #f0b820);
color: #0f0c08;
border: none;
font-weight: 700;
cursor: pointer;
border-radius: 2px;
transition: all 0.2s;
}
.command-input button:hover {
transform: translateY(-2px);
box-shadow: 0 8px 16px rgba(200, 150, 10, 0.3);
}
.output-panel {
background: linear-gradient(135deg, #1a1208, #0f0c08);
border: 2px solid #a87820;
border-radius: 2px;
padding: 1.5rem;
margin-bottom: 1.5rem;
max-height: 600px;
overflow-y: auto;
font-size: 0.9rem;
line-height: 1.6;
}
.section-header {
color: #f0b820;
font-size: 1.1rem;
margin-top: 1rem;
margin-bottom: 0.5rem;
text-transform: uppercase;
letter-spacing: 0.1em;
}
.json-export {
background: #0a0804;
border: 1px solid #3d2810;
padding: 1rem;
border-radius: 2px;
font-family: ‘Courier New’, monospace;
font-size: 0.8rem;
overflow-x: auto;
}
.branches {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
gap: 0.75rem;
margin-top: 1rem;
}
.branch-btn {
padding: 0.8rem;
background: linear-gradient(135deg, #2a3a50, #1a2a40);
border: 1px solid #4a7aaa;
color: #80b8e0;
cursor: pointer;
border-radius: 2px;
font-size: 0.85rem;
transition: all 0.2s;
}
.branch-btn:hover {
background: linear-gradient(135deg, #3a4a60, #2a3a50);
box-shadow: 0 0 8px rgba(74, 122, 170, 0.3);
}
</code></pre></div>
<p>
EXECUTE
Awaiting input. Start with /init [Theme] or describe a scene.
async function executeCommand() {
const input = document.getElementById(‘commandInput’).value;
try {
const response = await fetch(‘http://localhost:3000/api/compile’, {
method: ‘POST’,
headers: { ‘Content-Type’: ‘application/json’ },
body: JSON.stringify({ input })
});
const result = await response.json();
displayOutput(result);
} catch (error) {
console.error(‘Error:’, error);
}
}
function displayOutput(result) {
const panel = document.getElementById(‘outputPanel’);
panel.innerHTML = formatOutput(result);
}
function formatOutput(result) {
return `
<div class=”section-header”>🖋️ Narrative Breakdown</div>
<p>${result.narrative?.summary || ”}</p>
<div class=”section-header”>👤 Character Layer</div>
<p>Name: <strong>${result.character?.name || ‘Unknown’}</strong></p>
<p>Class: <strong>${result.character?.class || ”}</strong> / ${result.character?.subclass || ”}</p>
<p>Level: ${result.character?.level || 1}</p>
<div class=”section-header”>⚔️ System Mechanics</div>
${result.mechanics?.abilities?.map(a => `
<p><strong>${a.name}</strong> – ${a.type}</p>
<p style=”opacity: 0.8;”>${a.description}</p>
`).join(”) || ”}
<div class=”section-header”>🗺️ World Data</div>
<p>Region: <strong>${result.world?.region || ”}</strong></p>
<p>Threat Level: ${result.world?.threatLevel || 5}/10</p>
<div class=”section-header”>🎨 Visual Prompt</div>
<p style=”opacity: 0.8; font-style: italic;”>${result.visual?.prompt || ”}</p>
<div class=”section-header”>📟 JSON Export</div>
<div class=”json-export”>
<pre>${JSON.stringify(result.json, null, 2)}</pre>
</div>
<div class=”branches”>
${[1, 2, 3, 4, 5, 6].map(i => `
<button class=”branch-btn” onclick=”selectBranch(${i})”>
Branch [${i}]
</button>
`).join(”)}
</div>
`;
}
function selectBranch(num) {
const commands = [
‘/branch 1 – Deepen Mechanics’,
‘/branch 2 – Expand World’,
‘/branch 3 – Narrative Progression’,
‘/branch 4 – Simulate Combat’,
‘/branch 5 – Character Expansion’,
‘/branch 6 – Dialogue Tree’
];
document.getElementById(‘commandInput’).value = commands[num – 1];
executeCommand();
}
// Allow Enter key to execute
document.addEventListener(‘DOMContentLoaded’, () => {
document.getElementById(‘commandInput’).addEventListener(‘keypress’, (e) => {
if (e.key === ‘Enter’) executeCommand();
});
});
public class MythOSIntegration : MonoBehaviour {
[System.Serializable]
public class CompileResult {
public string narrative;
public Character character;
public Mechanics mechanics;
public World world;
}
async void CompileSceneFromAI(string input) {
var response = await APIClient.PostAsync("http://localhost:3000/api/compile",
JsonConvert.SerializeObject(new { input }));
var result = JsonConvert.DeserializeObject<CompileResult>(response);
// Apply to scene
SpawnCharacter(result.character);
GenerateTerrain(result.world);
ConfigureEnemies(result.mechanics);
}
void SpawnCharacter(Character charData) {
var prefab = Resources.Load<GameObject>($"Characters/{charData.archetype}");
var instance = Instantiate(prefab);
var stats = instance.GetComponent<CharacterStats>();
stats.health = charData.maxHP;
stats.aura = charData.maxAura;
stats.armor = charData.armorRating;
}
}
7.2 Godot Integration (GDScript)
GDScript
character.gd
extends Node
var myth_os_data = {}
func _ready():
var result = await call_api(“/api/compile”, “Generate Fire Knight”)
myth_os_data = result
apply_stats(result.character)
func apply_stats(char_data):
$Stats.max_health = char_data.maxHP
$Stats.max_aura = char_data.maxAura
$AnimatedSprite.animation = char_data.class
for ability in char_data.abilities:
$Abilities.add_ability(ability)
async func call_api(endpoint, input):
var http = HTTPRequest.new()
add_child(http)
var response = await http.request_completed
return JSON.parse_string(response[3].get_string_from_utf8())
ADVANCED FEATURES
8.1 Faction Reputation System
JavaScript
class FactionSystem {
calculateReputationChange(playerAction, faction) {
const impacts = {
‘defeat_enemy’: 15,
‘complete_quest’: 25,
‘help_npc’: 10,
‘attack_member’: -30,
‘steal_from’: -40,
‘save_leader’: 50
};
return impacts[playerAction] || 0;
}
getFactionStance(reputation) {
if (reputation > 80) return ‘Allied’;
if (reputation > 40) return ‘Friendly’;
if (reputation > 0) return ‘Neutral’;
if (reputation > -40) return ‘Wary’;
return ‘Hostile’;
}
}
8.2 Dynamic Difficulty Scaling
JavaScript
class DifficultyScaler {
calculateScaledEnemy(baseEnemy, playerLevel, regionalDifficulty) {
const scaleFactor = (playerLevel / 10) + (regionalDifficulty / 20);
return {
...baseEnemy,
health: baseEnemy.health * scaleFactor,
damage: baseEnemy.damage * scaleFactor,
loot: this.calculateLoot(baseEnemy, scaleFactor)
};
}
}
8.3 Quest Generation
JavaScript
class QuestGenerator {
generateQuest(region, playerLevel) {
return {
id: this.generateID(),
title: this.generateTitle(),
description: this.generateDescription(),
objectives: this.generateObjectives(),
rewards: this.calculateRewards(playerLevel),
difficulty: this.calculateDifficulty(playerLevel),
region: region
};
}
}
✅ Deterministic RPG Generation – All mechanics are mathematically sound ✅ Persistent World State – Actions have lasting consequences ✅ Game Engine Integration – Direct JSON export for Unity, Godot, Unreal ✅ Scalable Architecture – From indie games to MMOs ✅ Professional Quality – Enterprise-level code organization
Future Enhancements:
Machine learning for balance optimization
Real-time multiplayer faction wars
Procedural dungeon generation
Voice-driven command system
Cross-platform mobile app
📚 REFERENCES & RESOURCES
Game Balance Resources: GDC (Game Developers Conference)
Fantasy World Building: “Wonderbook” by Jeff VanderMeer
RPG Mechanics: “Game Design Fundamentals” by Joris Dormans
JSON Schema Validation: https://json-schema.org/
Document Version: 2.0
Last Updated: 2026-03-30
Author: MythWeaver Development Team
License: Open Source (MIT)
This comprehensive whitepaper provides everything needed to build, deploy, and integrate a professional-grade RPG generation system using the Compiler Pattern. The system bridges creative narrative AI with deterministic game mechanics, enabling developers to generate entire game worlds programmatically.
Make these code changes?
whitepapers/MYTH-OS_Whitepaper.md
md
MYTH-OS: The Compiler Pattern for Systemic RPG Generation
Abstract
This whitepaper provides a comprehensive overview of MYTH-OS, a full-stack JavaScript RPG Generation Agent system based on the Compiler Pattern methodology. It addresses the challenges of procedural generation in role-playing games (RPGs) and outlines a systematic approach to creating deterministic fantasy world-building.
Introduction
Role-playing games have evolved significantly, and their procedural generation aspects play a crucial role in enhancing player experiences. The Compiler Pattern offers a structured way to implement these systems efficiently. This document will guide you through designing and building an RPG generation system using this methodology.
Architectural Overview
Components of the System
Implementation Guide
Step 1: Setup the Environment
npm init. Step 2: Installation of Dependencies
bash
npm install express mongoose cors
Technical Specifications
Performance Requirements
Node.js version: 14.x and above
MongoDB version: 4.x and above
Code Examples
Character Generation Example
JavaScript
function generateCharacter(name) {
return {
name: name,
level: Math.floor(Math.random() * 100),
attributes: {
strength: Math.floor(Math.random() * 20),
agility: Math.floor(Math.random() * 20),
intelligence: Math.floor(Math.random() * 20)
}
};
}
System Prompts
"Generate a quest involving a dragon that threatens the village."
JSON Schemas
Character Schema
JSON
{
"type": "object",
"properties": {
"name": {"type": "string"},
"level": {"type": "integer"},
"attributes": {
"type": "object",
"properties": {
"strength": {"type": "integer"},
"agility": {"type": "integer"},
"intelligence": {"type": "integer"}
}
}
}
}
Integration Guides
Integration with third-party APIs can be handled via RESTful services. Aim for clear documentation of endpoints for ease of connection.
Deployment Instructions
Environment Configuration
Set up a .env file with your database URI.
Deploy using platforms like Heroku or Vercel.
Conclusion
The Compiler Pattern provides a powerful framework for RPG generation, ensuring a structured yet flexible approach to building rich fantasy worlds.
References
Procedural Content Generation in Games, Noor Shaker et al.
The Art of Game Design, Jesse Schell.
This is a submission for the 2026 WeCoded Challenge: Frontend Art
👨🦰👩🦳 Frontend Art Submission — Gender Beyond Boundaries
I built an interactive 3D experience using Next.js and Three.js that visually represents gender equality across professions.
The project features two avatars — a male on the left and a female on the right — dynamically showcasing 10 different professions. Alongside the visuals, contextual metadata appears, including real-world role models for each gender, reinforcing that capability and excellence are not defined by gender.
👉 Live Preview
👉 Github
💡 Inspiration
This project is a tribute to gender equality — a statement that professions, skills, and ambitions belong to everyone.
Growing up, many of us are subtly (or explicitly) told what we can or cannot become based on gender. I wanted to challenge that narrative using immersive web technology.
Each profession highlights real individuals who broke barriers, showing that talent, courage, and intelligence are universal traits. The dual-avatar system visually reinforces a simple but powerful idea:
There is no “male job” or “female job” — only human potential.
🧠 Featured Professions & Role Models
📰 Journalist
A fearless seeker of truth shaping public narratives.
🌾 Farmer
The backbone of global food security and sustainability.
🚒 Firefighter
A symbol of courage and selfless service.
💻 Software Engineer
Architects of the digital world.
🔧 Mechanic
Masters of machines and precision engineering.
🏥 Nurse
The heartbeat of healthcare systems.
✈️ Pilot
Leaders navigating the skies.
👮 Police Officer
Guardians of justice and public safety.
🛍️ Retail Salesperson
Drivers of commerce and community connection.
🩺 Doctor
Leaders in healing and medical innovation.
💻 My Code
Built with:
✨ Final Thought
This project isn’t just about visuals — it’s about perspective.
By placing male and female avatars side by side in every profession, I wanted users to feel equality, not just read about it. Technology has the power to shape narratives, and this is my small contribution toward a more inclusive one.
🚀 Performance & Optimization Note
As of this submission, I am actively working on further optimizing the asset delivery. Since high-quality Avatar GLB files can be heavy, they currently take a moment to fully render depending on the connection.
Thanks for reading! 🙌
Until next time, 🫡
Usman Awan (your friendly dev 🚀)
Kontiki is a Python microservices framework built on AMQP (RabbitMQ) via aio-pika and asyncio. For teams familiar with Nameko, the overall philosophy will feel familiar: message-driven services on top of RabbitMQ, with RPC and events as first-class primitives. Kontiki lives in the same “family” of frameworks, while leaning into an asyncio-native implementation, a delegate-oriented service structure, and a configuration-driven runner.
Kontiki focuses on keeping service code minimal and pushing infrastructure concerns into the framework. It is open source (Apache-2.0) and the code is available on GitHub: kontiki-org/kontiki.
1. Writing a service
Kontiki encourages keeping the service class as an entrypoint layer and moving domain logic into delegates (ServiceDelegate) with setup/start/stop lifecycle hooks.
from kontiki.delegate import ServiceDelegate
from kontiki.messaging import Messenger, on_event, rpc
class MyDelegate(ServiceDelegate):
async def setup(self):
# initialize from self.container.config
pass
async def start(self):
# optional: start background tasks / open connections
pass
async def stop(self):
# optional: stop background tasks / close connections
pass
def do_something(self, x: int) -> int:
return x * 2
class MyService:
name = "compute-api" # optional; defaults to class name
delegate = MyDelegate()
messenger = Messenger() # publish events / call other services
@rpc
async def compute(self, x: int):
return self.delegate.do_something(x)
@on_event("thing_happened")
async def on_thing(self, payload):
await self.messenger.publish("thing_processed", {"payload": payload})
Running a service
To run a service consistently across environments, expose it as a CLI entrypoint via cli.run(...) and register it as a script:
First, define a small run() entrypoint that delegates startup to Kontiki:
# e.g. myapp.main
from kontiki.runner import cli
def run():
cli.run(MyService, "Example Kontiki service.", version="1.0.0")
Then, expose that entrypoint as an executable command:
[tool.poetry.scripts]
my_service = "myapp.main:run"
Finally, run the service with one (or more) config files:
my_service --config config.yaml
2. Publishing and consuming events
Events are fire-and-forget. Consumers subscribe with @on_event("event_type"), and publishers emit events via Messenger.publish(...).
# Server-side (service)
class UserEventsService:
@on_event("user.created")
async def on_user_created(self, payload: dict):
...
# Client-side (standalone script)
async def publish_event_from_script():
async with Messenger(standalone=True) as messenger:
await messenger.publish("user.created", {"id": "u_123"})
# Client-side (from another service)
class BillingService:
messenger = Messenger()
@rpc
async def create_invoice(self, user_id: str):
await self.messenger.publish("user.created", {"id": user_id})
return "ok"
3. Calling services with RPC
Kontiki RPC is synchronous request/reply over AMQP. On the server side, handlers are exposed with @rpc (and can return rpc_error(code, message)); on the client side, calls go through a RpcProxy.
# Server-side (service)
class RpcService:
@rpc
async def rpc_example(self, feature: str):
if feature == "bad_input":
return rpc_error("USER_INPUT_ERROR", "Invalid feature value")
return "ok"
# Client-side (standalone script)
async def call_rpc_from_script():
async with Messenger(standalone=True) as messenger:
rpc_service = RpcProxy(messenger, service_name="RpcService")
return await rpc_service.rpc_example("bad_input")
# Client-side (from another service)
class ApiGatewayService:
messenger = Messenger()
@rpc
async def compute(self, feature: str):
rpc_service = RpcProxy(self.messenger, service_name="RpcService")
return await rpc_service.rpc_example(feature)
4. HTTP entrypoints
Kontiki can expose HTTP routes directly in the service using @http(...), optionally validating request bodies with Pydantic models and generating OpenAPI docs/Swagger UI.
class SimpleHttpService:
http_error_handlers = {HttpExampleError: (400, "Example error occurred")}
@http("/health", "GET", version="v1", response_model=HelloResponse)
async def health(self, request):
return HelloResponse(message="ok").model_dump()
5. Periodic tasks
Use @task(interval=..., immediate=...) to run periodic async work inside the service loop.
class TaskService:
@task(interval=10, immediate=True)
async def task_example(self):
...
6. Configuration: YAML merge + dot-based parameters
Running a service with multiple configuration files
Services can be started with multiple YAML files via repeatable --config (e.g. a shared base.yaml plus an environment-specific env.yaml).
# base.yaml
kontiki:
amqp:
url: amqp://guest:guest@localhost/
app:
cache:
ttl_seconds: 60
# env.yaml
app:
cache:
enabled: true
my_service --config base.yaml --config env.yaml
Reading parameters in delegates
Configuration values are typically loaded in setup() using dot-based paths:
from kontiki.configuration import get_parameter
from kontiki.delegate import ServiceDelegate
class MyDelegate(ServiceDelegate):
async def setup(self):
self.cache_ttl_seconds = get_parameter(
self.container.config,
"app.cache.ttl_seconds",
60,
)
7. Optional registry: heartbeats + degraded state
When the Kontiki registry service is running, services can register and expose operational state:
from pathlib import Path
class MyService:
@degraded_on
def is_degraded(self) -> bool:
required_dir = Path("/var/lib/my-service/uploads")
return not required_dir.exists()
Engineering tooling: kontiki-tui
For an operator-friendly view, the kontiki-tui project (sibling repository) provides a terminal UI to inspect/operate the Kontiki ecosystem.
Conclusion
Kontiki is a good fit for asyncio-native, message-driven microservices on RabbitMQ with first-class primitives for events and RPC—without rewriting the AMQP wiring, lifecycle, and operational plumbing for every service. This article is a quick overview: Kontiki also ships advanced options around the core features (sessions, broadcast delivery, message headers, error/retry behavior, and more). This repository ships with a solid set of runnable examples: start RabbitMQ locally and run most of them from the Makefile targets (see make run-amqp and the various run-* commands).
The Short Answer
Codacy ships a solid security scanning suite that covers the major vulnerability categories most development teams care about – SAST for source code flaws, SCA for vulnerable dependencies, secrets detection for leaked credentials, and DAST for runtime issues on the Business plan. It is not a replacement for a dedicated SAST tool if security scanning is your primary concern, but for teams that want security coverage bundled alongside code quality tools at $15/user/month, Codacy delivers genuine value.
This guide walks through exactly what Codacy’s security scanning covers, which tools it uses under the hood, how to configure security quality gates for pull requests, and how it stacks up against dedicated alternatives like Snyk Code and Semgrep.
What Security Scanning Does Codacy Include?
Codacy’s security offering is built across four layers, each targeting a different type of vulnerability or threat.
1. Static Application Security Testing (SAST)
SAST is the foundation of Codacy’s security work. It analyzes your source code without running it, looking for patterns that match known vulnerability classes. This includes the OWASP Top 10 categories – injection, broken authentication, sensitive data exposure, XML external entities, broken access control, security misconfiguration, XSS, insecure deserialization, using components with known vulnerabilities, and insufficient logging.
Codacy runs SAST on every pull request, scanning the changed files and reporting any new issues introduced in that changeset. This incremental approach is important: rather than flooding developers with every existing vulnerability in the codebase, Codacy surfaces only what changed in the current PR. Teams adopting Codacy on a legacy codebase can therefore start enforcing security standards on new code without being paralyzed by historical debt.
2. Software Composition Analysis (SCA)
Dependency vulnerabilities account for a large share of real-world security incidents, and Codacy’s SCA scans your dependency manifests – package.json, requirements.txt, pom.xml, Gemfile.lock, go.sum, and others – to find third-party libraries with known CVEs.
Each finding includes the affected library, the CVE identifier, a severity rating (critical, high, medium, low), and remediation guidance indicating which version resolves the vulnerability. SCA runs alongside SAST on every PR, meaning you get notified when a dependency update introduces a new vulnerability or when a new dependency you add is already known to be insecure.
For teams that need deeper SCA coverage – more comprehensive vulnerability databases, automated fix PRs, or granular license compliance management – dedicated tools like Snyk provide more horsepower. But for teams that want SCA as part of a broader quality and security platform, Codacy’s coverage is sufficient for most use cases.
3. Secrets Detection
Accidentally committing API keys, connection strings, private keys, and other credentials is one of the most common and damaging security mistakes developers make. Codacy’s secrets detection scans every commit and PR for patterns that match sensitive credential formats.
This includes cloud provider credentials (AWS, GCP, Azure), source code hosting tokens (GitHub, GitLab), payment processor keys (Stripe, PayPal), database connection strings, SSH private keys, JWT secrets, and many other categories. Detection runs automatically with no additional configuration required – it is on by default for every repository you connect to Codacy.
4. Dynamic Application Security Testing (DAST)
DAST is different from SAST in a fundamental way: it tests a running application rather than static code. This means it can find vulnerabilities that only appear at runtime – authentication bypasses, server configuration issues, session management flaws, and real-world exploit paths that static analysis cannot simulate.
Codacy’s DAST capability is powered by ZAP (Zed Attack Proxy) and is available exclusively on the Business plan. It requires pointing Codacy at a running instance of your application (typically a staging environment) and running a scan. This is a significant addition for security-conscious teams but is out of reach for developers on the Pro plan.
The Security Tools Running Under the Hood
One of Codacy’s architectural strengths – and one of the key things to understand about its security scanning – is that it does not build its own analyzers from scratch. Instead, it integrates well-established open-source security tools and presents their results through a unified interface. Here is a breakdown of the key security tools Codacy wraps.
Bandit (Python)
Bandit is the standard Python security linter, developed under the PyCQA umbrella. It scans Python code for common security issues including the use of hardcoded passwords, shell injection via subprocess, use of unsafe cryptographic functions, SQL injection patterns, insecure XML parsing, and assertion statements in security-sensitive code. Codacy runs Bandit automatically on Python repositories.
Brakeman (Ruby on Rails)
Brakeman is a static analysis tool specifically designed for Ruby on Rails applications. It understands Rails conventions deeply and can detect Rails-specific vulnerabilities like mass assignment, SQL injection through ActiveRecord, cross-site scripting in views, unprotected redirects, and insecure use of send and eval. For Rails teams, Brakeman’s integration in Codacy is a significant value-add.
SpotBugs and Find Security Bugs (Java)
SpotBugs is the successor to FindBugs and analyzes Java bytecode for bug patterns. The Find Security Bugs plugin extends SpotBugs with over 130 security-specific detectors covering vulnerabilities like LDAP injection, XPath injection, insecure random number generation, use of MD5 or SHA-1 for cryptography, and deserialization vulnerabilities. Codacy integrates both SpotBugs and Find Security Bugs for Java projects.
Semgrep (Multi-language)
Semgrep is a powerful pattern-matching engine that Codacy uses for cross-language security scanning. It supports JavaScript, TypeScript, Python, Java, Go, Ruby, PHP, C, C++, and more. Codacy runs Semgrep with a curated set of security rules covering injection patterns, insecure API usage, and OWASP Top 10 categories across these languages. If you also use Semgrep directly, note that Codacy’s integration runs a pre-configured subset of rules rather than the full Semgrep Registry.
ESLint Security Plugins (JavaScript/TypeScript)
For JavaScript and TypeScript, Codacy leverages ESLint with security-focused plugins including eslint-plugin-security and eslint-plugin-no-unsanitized. These catch patterns like unsafe use of innerHTML, eval, insecure regular expressions vulnerable to ReDoS, and unsafe object property access patterns. They complement the SAST rules from Semgrep to provide layered JavaScript security coverage.
PHP_CodeSniffer Security Rules (PHP)
For PHP codebases, Codacy uses PHP_CodeSniffer with security-oriented rulesets to detect common PHP vulnerabilities including SQL injection through unparameterized queries, command injection via exec and shell_exec, use of deprecated cryptographic functions, and unsafe file inclusion patterns.
The Codacy Security Dashboard
The security dashboard is where you get the repository-level view of all open security issues. It is one of the more useful parts of Codacy’s interface for security-focused work.
Issues are organized first by severity – critical, high, medium, low, and info – and then by category. The categories map loosely to vulnerability classes: injection, authentication, cryptography, input validation, authorization, sensitive data, and so on. This categorization makes it easy to identify if your codebase has a systematic problem in a particular area – for example, a pattern of not validating user input across multiple files.
The dashboard shows historical trends, so you can see whether your security debt is growing or shrinking over time. This is useful for security program metrics and for demonstrating progress to management or compliance teams. You can filter by file path, tool, category, and date range to drill into specific areas of concern.
One practical limitation: the dashboard shows findings from the last full scan of the default branch. PR-level findings appear in the PR itself, not automatically in the dashboard until they are merged. This means the dashboard always reflects the state of production code rather than in-flight work, which is the correct behavior but worth understanding when triaging issues.
PR-Level Security Checks
The pull request integration is where most developers interact with Codacy’s security scanning day to day. When a PR is opened or updated, Codacy posts a summary comment and inline comments at the specific lines where security issues were found.
Inline comments include the issue description, the rule that triggered it, a severity label, and often a brief explanation of why the pattern is problematic and how to fix it. The quality of these explanations varies by tool – Semgrep rules tend to have the most useful fix guidance, while some of the tool-specific rules provide more terse descriptions.
The PR summary comment shows a breakdown of new issues by severity, any resolved issues from the baseline, and the overall quality gate status. If you have security quality gates configured, the comment will show clearly whether the PR passes or fails the security threshold.
Configuring Security Quality Gates
Quality gates are Codacy’s mechanism for enforcing security standards on every PR. They are configured at the repository or organization level and create pass/fail criteria that integrate with your Git provider’s branch protection rules.
For security specifically, you can configure gates such as:
When a quality gate fails, Codacy posts a failing status check on the PR. If you have branch protection rules set to require Codacy’s status check to pass, the PR cannot be merged until the developer resolves the blocking issues or a code owner overrides the gate.
Setting security quality gates requires thinking about what level of strictness is realistic for your team. Starting with “no new critical issues” is a reasonable baseline that prevents the most dangerous vulnerabilities from reaching production without overwhelming developers with medium and low-severity findings. You can tighten the gates over time as the team builds familiarity with the tooling.
Codacy Security vs. Dedicated SAST Tools
If your primary requirement is security scanning, it is worth understanding where Codacy’s bundled security fits on the spectrum compared to dedicated SAST tools.
Codacy vs. Snyk Code
Snyk Code is a dedicated SAST tool with a significant focus on developer experience. Its strength is real-time scanning in the IDE and deep integration with Snyk’s vulnerability intelligence. Snyk’s SCA database is larger and more frequently updated than Codacy’s, and its dependency scanning provides more actionable remediation paths including automated fix PRs via Snyk’s PR automation.
Codacy offers broader coverage in a single platform – you get quality analysis, coverage tracking, and AI review alongside security. Snyk Code offers deeper security-specific functionality. If you are trying to decide between the two, consider whether security is your primary concern (Snyk) or whether you want a consolidated platform for quality and security together (Codacy). For a detailed breakdown, see the Codacy vs Snyk comparison.
Codacy vs. Semgrep
Semgrep gives security and engineering teams full control over static analysis rules. The Semgrep Registry contains thousands of community-contributed and Semgrep-maintained rules, and you can write custom rules in Semgrep’s pattern syntax to match vulnerabilities specific to your codebase and frameworks.
Codacy uses Semgrep internally but in a pre-configured, opinionated way. You do not get access to the full Semgrep Registry or the ability to write custom Semgrep rules through Codacy’s interface. If your security requirements include custom rule development or fine-grained control over which Semgrep rulesets run on your code, running Semgrep directly is a better choice. See the Codacy vs Semgrep comparison for more detail.
For a broader comparison of security scanning tools, the best SAST tools guide covers the full landscape.
CodeAnt AI: A Modern Alternative Worth Considering
If you are evaluating Codacy’s security scanning, CodeAnt AI is worth putting in the comparison set. It is a Y Combinator-backed platform priced at $24-40/user/month that bundles several capabilities that Codacy either lacks or offers only on higher-tier plans.
The security capabilities in CodeAnt AI include SAST with AI-powered analysis of security findings, secrets detection, and – notably – IaC security scanning for infrastructure-as-code configurations. Terraform, CloudFormation, and Kubernetes manifests are increasingly part of the attack surface for modern applications, and most static analysis platforms do not cover them well. CodeAnt AI’s IaC security fills that gap directly.
Beyond security, CodeAnt AI combines AI PR review with DORA metrics tracking and code health analytics. The AI review component is more sophisticated than Codacy’s AI Reviewer, offering deeper contextual analysis of security issues and their business risk rather than just pattern-match detections. For teams where AI-powered review and infrastructure security are priorities, CodeAnt AI is a compelling alternative to Codacy, though at a higher price point per user.
Codacy remains the stronger choice if you need broad language coverage (49 languages vs. CodeAnt AI’s narrower support), mature SCA with dependency scanning, or the lower $15/user/month entry price. The right choice depends on whether your team prioritizes language breadth or AI depth and infrastructure coverage.
Supported Languages for Security Scanning
Security scanning in Codacy is available for the major languages through the integrated tool ecosystem:
Coverage depth varies. Python (via Bandit) and Java (via SpotBugs and FindSecBugs) have the most mature security analysis. JavaScript and TypeScript benefit from both ESLint plugins and Semgrep. Languages covered primarily through Semgrep depend on the quality of available Semgrep rulesets for that language.
Setting Up Security Scanning in Codacy
Getting Codacy’s security scanning running takes under 15 minutes for most teams.
Step 1: Connect your repository. Sign into Codacy with your GitHub, GitLab, or Bitbucket account and authorize the integration. Select the repositories you want to analyze. Codacy triggers an initial full scan automatically.
Step 2: Review initial findings. The first scan will surface existing security issues across the codebase. Do not try to fix everything immediately. Use the security dashboard to understand the shape of your current security debt – which categories are most common, which files have the most issues, and what the severity distribution looks like.
Step 3: Configure code patterns. In the repository settings, go to Code Patterns to review which security rules are enabled. The defaults are reasonable for most teams, but you may want to disable rules that generate too many false positives for your specific codebase, or enable additional rules in tool categories that are relevant to your tech stack.
Step 4: Set quality gates. Configure security quality gates to fail PRs that introduce new critical or high-severity security issues. Start with a threshold that is achievable – “no new critical issues” is a good starting point. Tighten over time.
Step 5: Enable branch protection. In your Git provider’s repository settings, require the Codacy status check to pass before PRs can be merged. This enforces the quality gates in the development workflow.
Step 6: Review PR feedback. From this point forward, security findings from new PRs will appear as inline comments and a summary on each pull request, and the quality gate will reflect your configured security standards.
Practical Tips for Getting the Most Out of Codacy Security
Triage your baseline before enforcing gates. If you connect Codacy to a large existing codebase and immediately enable strict security gates, your development velocity will grind to a halt while the team addresses historical findings. Take a week to triage the baseline, mark pre-existing issues as accepted or false positives, and then enable gates that only apply to new code.
Use the category view to find systemic issues. If you see 30 “SQL injection” findings across 15 files, that is not just 30 individual bugs – it is a systemic pattern suggesting a shared anti-pattern in how your team writes database queries. The category grouping in the dashboard helps surface these systemic issues.
Supplement with secrets scanning in CI. While Codacy’s secrets detection is good, adding a dedicated secrets scanning step in your CI pipeline (using a tool like truffleHog or git-secrets) provides a second layer of defense and runs even on commits that go directly to the main branch.
Review the SCA findings regularly. New CVEs for existing dependencies are discovered constantly. Make it a habit to check the SCA section of the Codacy dashboard weekly and keep your dependency manifests current. Codacy will alert you to new vulnerabilities in existing dependencies when it rescans the default branch.
Consider Semgrep for custom rules. If your codebase has security concerns specific to your business logic or internal APIs, Codacy’s pre-configured Semgrep rules will not catch them. In those cases, running Semgrep directly alongside Codacy – with custom rules tuned to your codebase – provides the coverage that Codacy cannot.
Further Reading
If you are evaluating Codacy more broadly or comparing it to alternatives, these related guides cover the adjacent territory:
Conclusion
Codacy’s security scanning is a genuine and capable component of its platform – not a checkbox feature. The combination of SAST via integrated tools like Bandit, Brakeman, SpotBugs, and Semgrep; SCA for dependency CVEs; and secrets detection on every PR gives development teams meaningful security coverage without requiring a separate tool subscription.
Where Codacy falls short of dedicated SAST tools is in customizability and depth. You cannot write custom Semgrep rules through Codacy, the SCA database is smaller than Snyk’s, and DAST is gated behind the Business plan. Teams with serious security requirements – regulated industries, security-sensitive products, or active bug bounty programs – should layer Codacy with a dedicated SAST or SCA tool to fill the gaps.
For the majority of development teams, though, Codacy’s security scanning represents excellent value as part of its all-in-one offering. The PR-level security checks, configurable quality gates, and security dashboard give teams the visibility and enforcement they need to prevent the most common vulnerabilities from reaching production without the operational overhead of managing a separate security toolchain.
Frequently Asked Questions
What security vulnerabilities does Codacy detect?
Codacy detects a broad range of security vulnerabilities including SQL injection, cross-site scripting (XSS), insecure deserialization, hardcoded credentials, path traversal, XML injection, insecure cryptography, and dozens of other OWASP Top 10 and CWE patterns. It runs SAST, SCA for dependency CVEs, and secrets detection on every PR. The Business plan also adds DAST for runtime security testing.
Does Codacy cover the OWASP Top 10?
Yes, Codacy’s security scanning covers all OWASP Top 10 categories including injection flaws, broken authentication, sensitive data exposure, XML external entities, broken access control, security misconfiguration, cross-site scripting, insecure deserialization, vulnerable components, and insufficient logging. Coverage depth varies by language depending on which underlying analysis tools are integrated.
Which security tools does Codacy integrate?
Codacy integrates a range of specialized security tools including Bandit for Python, Brakeman for Ruby on Rails, SpotBugs for Java, FindSecBugs for Java bytecode, Semgrep for cross-language pattern matching, ESLint security plugins for JavaScript, PHP_CodeSniffer security rules for PHP, and more. These tools run under a unified configuration and reporting layer.
How does Codacy security scanning compare to Snyk?
Codacy offers a broader security bundle at $15/user/month including SAST, SCA, secrets detection, and DAST (Business plan), whereas Snyk specializes deeply in SCA and vulnerability databases. Snyk’s dependency scanning is more comprehensive with a larger vulnerability database and better remediation guidance. Codacy is better if you want all-in-one quality plus security. Snyk is better if dependency security is your primary concern.
How does Codacy compare to Semgrep for security?
Codacy uses Semgrep as one of its integrated engines, so it inherits Semgrep’s cross-language pattern matching capabilities. However, running Semgrep directly gives you full control over custom rules, rulesets from the Semgrep Registry, and deeper configuration. Codacy wraps Semgrep in a pre-configured way that is easier to set up but less customizable than running Semgrep standalone.
Can Codacy security scanning block pull requests?
Yes. Codacy supports quality gates for security that can be configured to fail the PR status check if security issues above a certain severity are introduced. You can require that new PRs add zero critical or high-severity security issues before merging, enforced through GitHub, GitLab, or Bitbucket branch protection rules.
Does Codacy detect secrets and API keys?
Yes, Codacy includes built-in secrets detection that scans commits and pull requests for exposed API keys, tokens, passwords, connection strings, private keys, and other sensitive credentials. It runs automatically on every scan without additional configuration and can be tuned to reduce false positives for common patterns.
What is the Codacy security dashboard?
The Codacy security dashboard provides a repository-level view of all open security issues, organized by severity (critical, high, medium, low, info) and category (injection, cryptography, authentication, etc.). It tracks security debt over time, shows which issues are new versus pre-existing, and provides filtering by file, category, and tool. It is available on the Pro and Business plans.
Is Codacy good for finding vulnerabilities in dependencies?
Codacy includes SCA (Software Composition Analysis) that scans dependency manifests like package.json, requirements.txt, pom.xml, Gemfile.lock, and others for known CVEs. It provides severity ratings and remediation suggestions. For teams that need deeper SCA with more comprehensive vulnerability databases and automated fix PRs, dedicated tools like Snyk or Dependabot may be preferable.
What is CodeAnt AI and how does it compare to Codacy for security?
CodeAnt AI is a Y Combinator-backed AI code health platform priced at $24-40/user/month that bundles SAST, secrets detection, IaC security (for Terraform, CloudFormation, Kubernetes), AI PR review, and DORA metrics. It offers deeper AI-powered security review than Codacy and adds infrastructure-as-code security coverage. Codacy covers more languages and has more mature SCA. CodeAnt AI is better for teams prioritizing AI-powered security review and IaC coverage.
How do I configure security rules in Codacy?
Codacy lets you configure security rules through the Code Patterns settings in your repository dashboard. You can enable or disable specific rules from each integrated tool, adjust severity levels, and add file exclusions. For Semgrep rules specifically, you can add custom rulesets via the Codacy configuration file (.codacy.yml) in your repository root.
Does Codacy security scanning work on every commit?
Codacy runs security scans on every pull request by default. It can also be triggered on commits to the main branch. The analysis focuses on changes introduced in the current changeset, though the security dashboard shows cumulative findings across the entire codebase from the last full repository scan.
Originally published at aicodereview.cc