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CODEC-CORTEX
CODEC-CORTEX
Universal Communication Protocol for LLM/SLM Agents
v0.6.0 · MPL-2.0 · License · Specification

OverviewHow It WorksRoadmapQuick StartDocumentationProject Structure🇪🇸 Español


📋 Overview

CODEC-CORTEX is a compression protocol for agent knowledge.

Just as H.264 compresses video frames into a bitstream for efficient transmission, CODEC-CORTEX compresses agent state — context, lessons, objectives, working memory — into a dense sigil format that LLMs and SLMs can transmit, store, and reconstruct with minimal token overhead.

Metric Prose (plain text) CODEC-CORTEX Compression
Session state ~250 tokens ~28 tokens ~8×
Lesson (LNG) ~80 tokens ~12 tokens ~6×
Knowledge (KNW) ~120 tokens ~20 tokens ~6×
Full project brain ~3,500 tokens ~450 tokens ~7×

But the real compression is semantic. The learning engine automatically distills multiple specific lessons (LNG) into general knowledge (KNW) — a second-order compression that compounds across sessions.

           SES (Sessions)                          ~250 tokens
              ↓ cortex.learn
           LNG (Lessons)     ~8:1 compression      ~30 tokens
              ↓ elevate
           KNW (Knowledge)   ~5:1 compression       ~6 tokens
                                         ─────────────
                          Total:          ~40:1 semantic compression

🏗️ How It Works

CODEC-CORTEX operates on three independent layers:

┌─────────────────────────────────────────────────────────────┐
│                    CODEC-CORTEX PROTOCOL                     │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Layer 3: Knowledge (Semantic Compression)                  │
│  ────────────────────────────────────────────               │
│  Engine: cortex.learn / elevate                             │
│  Transforms: many LNG → one KNW                             │
│  Purpose: compound learning across sessions                 │
│                                                             │
│  Layer 2: Transport (MCP / File / ACP)                      │
│  ────────────────────────────────────────────               │
│  MCP: real-time encoding/decoding for agents                │
│  File: .cortex persistence on disk                          │
│  ACP: cross-agent delegation                                │
│                                                             │
│  Layer 1: Representation (Sigil Syntax)                     │
│  ────────────────────────────────────────────               │
│  Sigils: FCS, OBJ, WRK, LNG, KNW, SES...                   │
│  Types: attrs, cuerpo, attrs-pos                            │
│  Sections: $0 through $N                                    │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Layer 1 — Representation

The core sigil format. Every agent state — focus, objectives, working memory, lessons, knowledge — is expressed as dense, structured entries. This is the compressed bitstream of the protocol.

$2: FOCUS

FCS:current{what:"Implement auto-numbering", priority:"medium", status:"current", survive:"work"}

$7: LESSONS

LNG:handler_id{type:"process", cause:"BLP-003 execution", lesson:"Always verify file in disk after create"}

Layer 2 — Transport

How the compressed state moves between agents, systems, and humans:

Transport Protocol Purpose Status
File .cortex on disk Persistence, history ✅ Active
MCP Model Context Protocol Real-time agent encoding/decoding 🚧 In design
ACP Agent Communication Protocol Cross-agent task delegation 📋 Future
LSP Language Server Protocol Human editor support 📋 Future

Layer 3 — Knowledge (Semantic Compression)

The engine that makes CODEC-CORTEX more than a format. cortex.learn scans accumulated lessons, identifies patterns, and elevates them into general knowledge. This is where the protocol achieves true compression — not of syntax, but of meaning.


🛣️ Roadmap

Phase 1: File CODEC — ✅ Active
┌─────────────────────────────────────────────────────────────┐
│ • Parser (core + v2)    • Validator (E023-E034)             │
│ • HCORTEX renderer      • Learning engine (LNG → KNW)      │
│ • CLI (17 commands)     • 695+ tests                       │
│ • Auto-numbering        • .cortex persistence              │
└─────────────────────────────────────────────────────────────┘

Phase 2: Stream CODEC — 🚧 Next
┌─────────────────────────────────────────────────────────────┐
│ cortex.encode(state) → sigils     MCP Server exposure       │
│ cortex.decode(sigils) → state     Real-time agent transport │
│ cortex.learn as MCP tool          Cross-session learning    │
└─────────────────────────────────────────────────────────────┘

Phase 3: Database CODEC — 🔮 Vision
┌─────────────────────────────────────────────────────────────┐
│ Sigil queries (GET KNW:*)         Streaming between agents  │
│ Semantic indexes                  Transactional writes      │
│ Replicated state                  Multi-agent ecosystem     │
└─────────────────────────────────────────────────────────────┘

See the full Roadmap Document for detailed phase breakdown and cycles.


🚀 Quick Start

Installation

pip install codec-cortex
cortex --version

Requires Python ≥ 3.9.

Initialize agent memory

# Create your agent's brain
cp docs/reference/SKILL.cortex brain.cortex

# Verify it
cortex verify brain.cortex

# Write your focus
cortex edit brain.cortex --section 2 --set "what:My current objective"

# Render as human-readable
cortex render brain.cortex --mode readable

Load as universal skill

For agents that support skill loading:

// Load CODEC-CORTEX as your memory protocol
// See skill/cortex/AGENT.md for identity template
// See skill/hcortex/SKILL_HCORTEX.md for full specification

See Quick Start Guide for detailed walkthrough.


📚 Documentation

The project documentation is organized under docs/:

Directory Content
docs/reference/ Stable reference: SKILL specification, roadmap, learning engine
docs/releases/ Delivery reports per version (v0.3.0 → v2.4.0)
docs/verification/ Audit and verification reports
docs/benchmarks/ Performance benchmarks and analysis
`docs/proposals/ Strategic proposals and business plans
docs/archive/ Historical or orphan documents

Key documents:

Document Description
docs/reference/SKILL.md Full CORTEX specification
docs/reference/cortex-codec-roadmap.md Protocol vision and phase roadmap
docs/reference/learning-engine-spec.md Learning engine specification
skill/cortex/SKILL.md Dense CORTEX skill file
skill/hcortex/SKILL_HCORTEX.md Human-readable HCORTEX skill spec

🧩 Project Structure

codec-cortex/
├── cli/                  ← CLI and Python package (parser, validator, renderer)
│   └── src/cortex/       ← Core: parser, hcortex, v2, glossary, crud
├── docs/                 ← All documentation (reference, releases, benchmarks, etc.)
│   ├── reference/        ← Stable reference documents
│   ├── releases/         ← Delivery reports
│   ├── verification/     ← Audit reports
│   ├── benchmarks/       ← Performance benchmarks
│   └── proposals/        ← Strategic vision
├── skill/                ← CORTEX and HCORTEX skill specifications
│   ├── cortex/           ← Dense CORTEX format
│   └── hcortex/          ← Human-readable HCORTEX format
└── benchmarks/           ← Benchmark scripts

📊 Enterprise Readiness

Capability Status Details
Deterministic parser Zero LLM calls for parse/encode/decode/verify
Full validation suite 695+ tests, strict mode
CLI with 28+ commands verify, render, convert, CRUD, doctor, diff, format, diagram, session, learn
Learning engine SES → LNG → KNW elevation pipeline
Runtime sessions Session lifecycle (start → event → consolidate → close)
Global CLI flags --output json, --json, --mode, --yes, --version
MCP server 🚧 Phase 2: Stream CODEC
ACP integration 📋 Phase 2/3
LSP language server 📋 Phase 3

📄 License

Starting with CODEC-CORTEX v0.4.0, the project core is licensed under the Mozilla Public License 2.0 (MPL-2.0).

Previous releases published under the MIT License remain available under their original MIT terms. The license change applies prospectively to v0.4.0 and later releases.

The CODEC-CORTEX name, logo, visual identity and related marks are not licensed under MPL-2.0 and are governed by the project trademark policy.


Designed by Fidel Ernesto Lozada A. · Systems Engineer / MSc. Management Sciences · MPL-2.0


🇪🇸 Español

Versión en español — English version above


📋 Resumen

CODEC-CORTEX es un protocolo de compresión para el conocimiento de agentes de IA.

Así como H.264 comprime frames de video en un flujo de bits para transmisión eficiente, CODEC-CORTEX comprime el estado de un agente — contexto, lecciones, objetivos, memoria de trabajo — en un formato denso de sigilos que los LLMs y SLMs pueden transmitir, almacenar y reconstruir con gasto mínimo de tokens.

Métrica Texto plano CODEC-CORTEX Compresión
Estado de sesión ~250 tokens ~28 tokens ~8×
Lección (LNG) ~80 tokens ~12 tokens ~6×
Conocimiento (KNW) ~120 tokens ~20 tokens ~6×
Cerebro completo ~3,500 tokens ~450 tokens ~7×

La compresión real es semántica. El motor de aprendizaje destila múltiples lecciones específicas (LNG) en conocimiento general (KNW) — una compresión de segundo orden que se acumula entre sesiones.

           SES (Sesiones)                        ~250 tokens
              ↓ cortex.learn
           LNG (Lecciones)    ~8:1 compresión     ~30 tokens
              ↓ elevate
           KNW (Conocimiento) ~5:1 compresión      ~6 tokens
                                         ─────────────
                          Total:     ~40:1 compresión semántica

🏗️ Cómo Funciona

CODEC-CORTEX opera en tres capas independientes:

┌─────────────────────────────────────────────────────────────┐
│                    PROTOCOLO CODEC-CORTEX                    │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Capa 3: Conocimiento (Compresión Semántica)                │
│  ────────────────────────────────────────────               │
│  Motor: cortex.learn / elevate                              │
│  Transforma: muchas LNG → una KNW                           │
│  Propósito: aprendizaje compuesto entre sesiones            │
│                                                             │
│  Capa 2: Transporte (MCP / Archivo / ACP)                   │
│  ────────────────────────────────────────────               │
│  MCP: codificación/decodificación en tiempo real            │
│  Archivo: persistencia .cortex en disco                     │
│  ACP: delegación entre agentes                              │
│                                                             │
│  Capa 1: Representación (Sintaxis de Sigilos)               │
│  ────────────────────────────────────────────               │
│  Sigilos: FCS, OBJ, WRK, LNG, KNW, SES...                  │
│  Tipos: attrs, cuerpo, attrs-pos                            │
│  Secciones: $0 a $N                                         │
│                                                             │
└─────────────────────────────────────────────────────────────┘

🚀 Inicio Rápido

pip install codec-cortex
cortex --version

Requiere Python ≥ 3.9.

# Crear el cerebro de tu agente
cp docs/reference/SKILL.cortex brain.cortex

# Verificarlo
cortex verify brain.cortex

# Escribir tu enfoque
cortex edit brain.cortex --section 2 --set "what:Mi objetivo actual"

# Renderizar como legible
cortex render brain.cortex --mode readable

Comandos del CLI

Comando Descripción
cortex session start Iniciar sesión de trabajo
cortex session status Estado de sesión activa
cortex learn scan Escanear cerebro en busca de candidatos
cortex learn elevate Elevar lecciones a conocimiento
cortex render Renderizar .cortex a HCORTEX legible
cortex verify Validar archivo .cortex
cortex doctor Diagnosticar integridad del workspace
cortex --output json <comando> Salida JSON para integración

📚 Documentación en Español

Documento Descripción
docs/reference/SKILL.md Especificación completa del protocolo CORTEX
docs/reference/cortex-codec-roadmap.md Visión del protocolo y hoja de ruta
docs/reference/learning-engine-spec.md Especificación del motor de aprendizaje

📄 Licencia

A partir de CODEC-CORTEX v0.4.0, el núcleo del proyecto se publica bajo la Mozilla Public License 2.0 (MPL-2.0).

Las versiones anteriores publicadas bajo licencia MIT permanecen disponibles bajo sus términos originales.


Diseñado por Fidel Ernesto Lozada A. · Ingeniero de Sistemas / MSc. Ciencias de Gestión · MPL-2.0

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