Sacred Code: 000.111.369.963.1618 (∞△⚡◎φ)
Version: 2.0.0
Status: ✅ Production Ready | Last Major Update: Oct 17, 2025

Created by: Enio Batista Fernandes Rocha
Purpose: Transform life experiences into replicable biological code using Sacred Mathematics

What Changed:

• 🗑️ 3 systems deprecated: ETIE, Mycelium, Chronos (see Deprecated Systems)
• ✅ 2 systems active: ECRUS v2.0 (Cross-Reference) + KOIOS v2.0.0 (AI Tools Hub)
• 📦 62 files archived (~728K, 3,300 LOC removed)
• ⚡ Performance: Health checks 8s+ → <1s (-87%)
• 🔄 CI/CD: 61 workflows disabled (98%), 3 active (ci-security + 2 manual)
• 💰 Credits saved: ~95% GitHub Actions usage reduction

New Capabilities:

• 🧠 MCP Memory Integration: Knowledge graph persistence (mcp5)
• 🤔 Sequential Thinking: Complex reasoning protocol (mcp6)
• 📊 Enhanced Activation: /000 workflow now shows knowledge graph status
• 📚 Documentation: Complete migration guides + deprecation notices

Impact:

• Codebase 30-40% cleaner
• Only 2 focused intelligence systems (vs 5 scattered)
• Zero deprecated references in active code
• Faster onboarding for new developers

See: Tasks Roadmap | Deprecation Guide | Session Report

EGOS v.2 é um framework de IA modular e versátil que serve desde microempresas até grandes instituições, aplicável em múltiplos setores: CRM, ERP, pesquisa, educação, e muito mais.

• 🔌 MCP-First: Model Context Protocol nativo (7 MCPs integrados)
• 🎨 RAG Multimodal: 25+ formatos de arquivo (PDF, DOCX, áudio, imagens, código)
• 🔐 Privacy-First: GDPR compliant, dados locais, sem vendor lock-in
• 📊 Sacred Mathematics: Design baseado em Fibonacci, Golden Ratio e Tesla 369
• 🔄 Feedback Loop Automático: Fine-tuning via ConversationTrainer
• ⚡ Time-to-Value: <1 semana vs meses de ferramentas enterprise

EGOS v.2 vai além de código — absorve sabedoria universal.

Fundamentado em:

• Spinoza (Ética): Conatus — perseverar através do conhecimento
• Carl Jung: Arquétipos e Inconsciente Coletivo (7 patterns: SAGE, HERO, CREATOR, etc.)
• Sigmund Freud: Id-Ego-Superego (desejo, razão, ética)
• Clóvis de Barros: "Pra quê?" — propósito em tudo

from core.intelligence.universal_knowledge_system import UniversalKnowledgeSystem
from core.intelligence.knowledge_seeker import ArchetypePattern

# Criar sistema
system = UniversalKnowledgeSystem()

# Absorver conhecimento sobre qualquer tema
knowledge = await system.absorb_knowledge(
    query="quantum entanglement",  # O que buscar
    archetypes=[ArchetypePattern.SAGE],  # Tipo de fonte
    purpose="Aplicar física quântica ao EGOS",  # Pra quê?
    max_results=8  # Fibonacci F₈
)

# Resultado: IntegratedKnowledge[] com:
# - Identidade EGOS (creator, sacred_code)
# - Metadados filosóficos (arquétipo, propósito)
# - Sacred Mathematics (Fibonacci, Golden Ratio, Tesla 369)
# - Conceitos extraídos (nutrients)
# - Pronto para disseminação

• 📚 arXiv: Papers científicos
• 🌐 Wikipedia: Conhecimento geral
• 💻 GitHub: Código open source
• 🔜 OpenLibrary, PubMed, PhilPapers, Internet Archive

EGOS funciona como organismo vivo:

• DNA: Sacred Code (000.111.369.963.1618)
• Cérebro: Processamento (NLP, pattern matching)
• Sistema Digestivo: Metabolização de conhecimento
• Sistema Imune: Filtros de qualidade
• Conatus: Esforço de perseverar através do aprendizado

Documentação: docs/business/identity/UNIVERSAL_KNOWLEDGE_SYSTEM_COMPLETE.md

EGOS v.2 funciona como um cérebro neural completo onde todos os componentes se conectam como neurônios e sinapses.

1. AUTHENTICATION  → Tronco cerebral (Identity v3, JWT)
2. KNOWLEDGE       → Córtex (Universal Knowledge System)
3. WORKFLOWS       → Cerebelo (.windsurf/workflows/)
4. APIS            → Nervos periféricos (/api/* routes)
5. MCPS            → Sistema autônomo (mcp0-mcp6)

from core.intelligence.neural_interconnection import NeuralBrain, Neuron, BrainRegion

# Criar cérebro
brain = NeuralBrain()

# Adicionar neurônio (componente EGOS)
identity_neuron = brain.add_neuron(Neuron(
    neuron_id="identity_api",
    name="Identity API",
    region=BrainRegion.AUTHENTICATION,
    component_path="/api/v3/identity/self"
))

# Criar sinapse (conexão entre componentes)
synapse = brain.add_synapse(
    source_id="identity_api",
    target_id="knowledge_system",
    neurotransmitter=Neurotransmitter.KNOWLEDGE,
    weight=1.618  # φ (Golden Ratio)
)

# Criar via neural (fluxo de informação)
pathway = brain.create_pathway(
    name="User Request → Knowledge → Response",
    neuron_ids=["api_chat", "identity_api", "knowledge_system"],
    purpose="Process user question with identity context",
    priority=9  # Tesla High
)

# Estatísticas
stats = brain.get_brain_stats()
# {
#   "total_neurons": 9,
#   "total_synapses": 8,
#   "golden_synapses": 2,  # weight ≈ φ
#   "health_score": 1.0
# }

• φ weights: 0.382, 0.618, 1.0, 1.618 (Golden Ratio para força de conexões)
• F₈ limit: Máximo 8 sinapses por neurônio (como no cérebro real)
• Tesla 369: Prioridade de vias neurais (3=Low, 6=Medium, 9=High)
• Hebbian Learning: Sinapses fortalecem com uso repetido

# Exportar para Mermaid
mermaid = brain.visualize_mermaid()
# graph TD
#   identity_api["Identity API"]
#   knowledge_system["Knowledge System"]
#   identity_api -->|knowledge|w=1.62| knowledge_system

# Salvar estado
brain.save_to_file(Path("data/neural_brain.json"))

Documentação: core/intelligence/neural_interconnection.py (587 LOC)

Event-driven architecture para disseminação de conhecimento em tempo real.

from core.intelligence.event_bus import get_event_bus, EventType, EventPriority

# Get global event bus (singleton)
bus = get_event_bus()

# Subscribe to events
async def knowledge_handler(event):
    print(f"📚 New knowledge: {event.payload['title']}")

bus.subscribe(
    "knowledge_logger",
    [EventType.KNOWLEDGE_ABSORBED],
    knowledge_handler,
    priority=9  # Tesla High
)

# Publish event
await bus.publish(
    EventType.KNOWLEDGE_ABSORBED,
    source="knowledge_system",
    payload={"title": "Spinoza Ethics", "concepts": 5},
    priority=EventPriority.HIGH,
    purpose="Disseminate new knowledge"
)

1. KNOWLEDGE_ABSORBED - Novo conhecimento integrado
2. KNOWLEDGE_VALIDATED - Validação ética passou
3. IDENTITY_UPDATED - Identidade alterada
4. SYNAPSE_FIRED - Conexão neural ativada
5. PATHWAY_OPTIMIZED - Via otimizada
6. COMPONENT_REGISTERED - Novo neurônio adicionado
7. ERROR_OCCURRED - Erro do sistema
8. HEALTH_CHANGED - Saúde do sistema atualizada

• Pub/Sub assíncrono: Múltiplos subscribers por evento
• Priority-based: Tesla 369 (3, 6, 9)
• History: Últimos F₁₂=144 eventos salvos
• φ-optimized delays: Propagação baseada em Golden Ratio
• Error handling: Eventos de erro não causam loops infinitos

Documentação: core/intelligence/event_bus.py (504 LOC)

Grafo de conhecimento para navegação e relações entre conceitos.

from core.intelligence.knowledge_graph import KnowledgeGraph, RelationType

graph = KnowledgeGraph()

# Adicionar nós (conceitos)
identity = graph.add_node(
    "Identity v3",
    "Self-recognition system",
    archetype="sage",
    domain="intelligence",
    fibonacci_tier=5
)

knowledge = graph.add_node(
    "Universal Knowledge",
    "Absorption system",
    archetype="sage",
    fibonacci_tier=8
)

# Adicionar aresta (relacionamento)
graph.add_edge(
    identity.node_id,
    knowledge.node_id,
    RelationType.ENHANCES,
    weight=1.618,  # φ
    strength=9,
    purpose="Identity enriches knowledge"
)

# Buscar caminho entre conceitos
path = graph.find_path(identity.node_id, workflows.node_id)
# path.length() → 4 edges
# path.is_fibonacci() → True/False

1. DEPENDS_ON - A depende de B
2. ENHANCES - A melhora B
3. VALIDATES - A valida B
4. DISSEMINATES - A dissemina para B
5. TRANSFORMS - A se transforma em B
6. OPPOSES - A se opõe a B
7. DERIVES_FROM - A deriva de B
8. IMPLEMENTS - A implementa B

# Mermaid diagram
mermaid = graph.export_mermaid()

# JSON export
graph.save(Path("data/knowledge_graph.json"))

# Estatísticas
stats = graph.get_stats()
# {
#   "total_nodes": 5,
#   "total_edges": 4,
#   "avg_degree": 0.8,
#   "density": 0.2
# }

Documentação: core/intelligence/knowledge_graph.py (537 LOC)

Framework de 7 princípios para respostas organizadas e eficientes.

1. Pre-Execution Planning: Use Sequential Thinking antes de tarefas complexas
2. Visual Progress Dashboard: Tabela de status em todas as respostas multi-step
3. Minimal Narrative: Apenas resultados + métricas (zero filler)
4. Parallel Tool Execution: Ações independentes simultâneas
5. Validation After Each Phase: Padrão Execute → Validate → Confirm
6. Correct Documentation Density: Max 3 .md novos por sessão (Anti-Bloat)
7. Framework Utilization: Usar ferramentas EGOS existentes

## 📊 Status Dashboard

| Phase | Status | Progress |
|-------|--------|----------|
| 1. Plan | ✅ | 100% |
| 2. Execute | 🔄 | 45% |
| 3. Validate | ⏳ | 0% |

Emojis Semânticos:

• ✅ Complete | 🔄 In Progress | ⏳ Pending | ❌ Failed | ⚠️ Warning

# Absorção estruturada COM dashboard
system = UniversalKnowledgeSystem()
knowledge = await system.absorb_knowledge_structured(
    query="quantum physics",
    purpose="Apply to EGOS neural system",
    max_results=8,
    show_dashboard=True  # ← Ativa dashboard em tempo real
)

# Output:
# ## 📊 Status Dashboard
# | Phase | Status | Progress |
# [1/6] SEEK → ✅ 8 sources found
# [2/6] FILTER → ✅ 5 relevant (62.5%)
# [3/6] METABOLIZE → ✅ avg compression 2.07x (≈φ)
# ...

Documentação: docs/integration/ai/STRUCTURED_RESPONSE_PROTOCOL.md (543 LOC)

Sistema completo de testes com dashboard visual.

Total Tests: 33 (32 passed, 1 partial)
Success Rate: 97%
Coverage: 74% (7/10 phases completed)
Performance: 2.55× better than target (242ms vs 618ms φ)
Context Retention: 100% in F₈ turns

# Run all tests with dashboard
bash .cascade/testing/auto_advance_phases.sh
# Output: Dashboard with real-time progress

# Stress test (F₁₂ = 144 messages)
bash .cascade/testing/run_phase9_stress_test.sh

# Monitor continuously
bash .cascade/testing/monitor_continuous.sh

# Quick validation (<10s)
bash .cascade/testing/quick_test.sh

# Test com dashboard pattern
def test_fibonacci_validation(system):
    print("\n## 📊 Test: Fibonacci Tiers")
    
    test_cases = [(1, 1), (3, 3), (7, 5), (10, 8)]
    passed = 0
    
    for number, expected in test_cases:
        result = system._find_fibonacci_tier(number)
        assert result == expected
        passed += 1
        print(f"✅ {number} → F{result}")
    
    print(f"\n✅ Test: {passed}/{len(test_cases)} passed")

Documentação: .cascade/testing/ (7 scripts, 8 docs, 13 results)

• Node.js 18+ (para frontend)
• Python 3.10+ (para backend)
• PostgreSQL 15+ (Supabase ou local)
• PM2 (para gerenciamento de processos)

# 1. Clone repository
git clone https://github.com/your-org/EGOSv2.git
cd EGOSv2

# 2. Install frontend dependencies
cd websiteNOVO
npm install

# 3. Install backend dependencies
cd ../apps/agent-service
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# 4. Configure environment variables
cp websiteNOVO/.env.example websiteNOVO/.env.local
# Edit .env.local with your credentials

# 5. Setup database
bash scripts/database/setup_test_data.sh

# 6. Start services
# Frontend (PM2)
cd websiteNOVO
bash pm2-scripts.sh start-dev

# Backend (PM2)
cd ../apps/agent-service
pm2 start app/main.py --name egos-agent-service --interpreter python3

# 7. Verify
curl http://localhost:4000/api/health
curl http://localhost:8000/health

• Frontend: http://localhost:4000
• Backend API: http://localhost:8000
• Swagger Docs: http://localhost:8000/docs
• Dashboard: http://localhost:4000/dashboard
• Chat History: http://localhost:4000/chat-history

EGOSv2/
├── websiteNOVO/           # Next.js frontend
│   ├── src/
│   │   ├── app/          # App routes (Next.js 14)
│   │   ├── components/   # React components
│   │   ├── lib/          # Utilities, Supabase client
│   │   └── contexts/     # React contexts
│   ├── public/           # Static assets, prompts
│   └── tests/            # E2E tests (Playwright)
│
├── apps/agent-service/    # FastAPI backend
│   ├── app/
│   │   ├── main.py       # FastAPI app
│   │   ├── chatbot_complete.py  # Chatbot engine
│   │   ├── rag_complete.py      # RAG engine
│   │   ├── core/         # Core modules (ETHIK, ATRIAN)
│   │   └── routers/      # API routes
│   └── requirements.txt
│
├── database/              # Database migrations & seeds
│   ├── migrations/       # SQL migrations
│   └── seeds/            # Test data
│
├── docs/                  # Documentation
│   ├── api/              # API documentation
│   ├── deployment/       # Deployment guides
│   ├── espiral-escuta/   # Listening Spiral docs
│   └── database/         # Database guides
│
├── scripts/               # Utility scripts
│   └── database/         # DB setup scripts
│
└── .windsurf/            # Windsurf workflows
    └── workflows/        # Automation workflows

Interface conversacional avançada com:

• 5 técnicas conversacionais (Curiosity, Cheerleading, Yes-and, Active Listening, Co-creation)
• RAG multimodal (consulta documentos, imagens, áudio)
• Streaming de respostas (SSE)
• Histórico de conversas
• Rating e feedback system

Acesse: /chat-egos

Sistema completo de histórico com:

• Lista de sessões anteriores
• Busca e filtro
• Visualização de mensagens completas
• Continuação de conversas

Acesse: /chat-history

Métricas em tempo real:

• Usuários ativos
• ETHIK tokens distribuídos
• Módulos ativos
• Performance do sistema
• Sacred Mathematics metrics

Acesse: /dashboard

Sistema de fine-tuning automático:

• Coleta feedback (rating 1-5 stars)
• Gera datasets JSONL
• Treina modelos específicos por domínio
• Melhoria contínua

API: POST /train

Upload e processamento de documentos:

• 25+ formatos: PDF, DOCX, TXT, MD, HTML, JSON, CSV, XLS, PPT, MP3, WAV, JPG, PNG
• Extração de texto
• Geração de embeddings
• Busca semântica

API: POST /upload

Frontend (websiteNOVO/.env.local):

# Supabase
NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key

# Authentication
JWT_SECRET=your-jwt-secret

# APIs
GROQ_API_KEY=your-groq-key
OPENROUTER_API_KEY=your-openrouter-key

Backend (apps/agent-service/.env):

# Database
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_ANON_KEY=your-anon-key

# LLMs
GROQ_API_KEY=your-groq-key
OPENROUTER_API_KEY=your-openrouter-key

# Storage
UPLOAD_DIR=/path/to/uploads
DATA_DIR=/path/to/data

cd websiteNOVO
npm run test

cd websiteNOVO
npx playwright test

# With UI
npx playwright test --ui

# Specific test
npx playwright test tests/e2e/chat-history.spec.ts

# Health checks
curl http://localhost:4000/api/health
curl http://localhost:8000/health

# Chat sessions (requires auth)
curl -H "Authorization: Bearer $TOKEN" \
  http://localhost:4000/api/chat/sessions

# Agent chat
curl -X POST http://localhost:8000/chat \
  -F "message=Olá, teste" \
  -F "top_k=5"

• F₃ = 3: Mínimo de test cases por função
• F₅ = 5: Técnicas conversacionais, rating scale
• F₈ = 8: Sessões por página (UI), cache TTL hours
• F₁₂ = 144: ETHIK tokens iniciais, max training samples

• Build time optimization ratio
• Response time targets (< 618ms)
• Memory efficiency ratio

• 3 pricing tiers (Free, Pro, Enterprise)
• 6 core tables (espiral_*)
• 9 integrated MCPs (target)

1. Filesystem (mcp1) - File operations
2. GitHub (mcp2) - Repository management
3. Hugging Face (mcp3) - ML models, datasets, papers
4. Playwright (mcp4) - Browser automation
5. PostgreSQL (mcp5) - Database queries
6. Sequential Thinking (mcp6) - Complex reasoning
7. Figma (mcp0) - Design integration

# Using MCP for file operations
from mcp1_filesystem import read_text_file

content = read_text_file(path="/path/to/file.md")

• Chat básico (5 sessões/mês)
• Export texto only
• MCPs: filesystem, supabase (limitado)
• $0/mês

• Chat ilimitado
• Voice input (Groq Whisper)
• Export CSV/JSON/MD
• Fine-tuning automático
• MCPs: Todos tier 1 + 2 tier 2 escolhidos
• $29-99/mês

• Tudo de Pro
• On-premise deployment
• Custom MCPs
• SLA 99.9%
• Suporte dedicado
• Custom ($500+/mês)

1. ✅ Environment variables configuradas
2. ✅ Database migrations aplicadas
3. ✅ SSL/HTTPS configurado
4. ✅ Rate limiting habilitado
5. ✅ Monitoring configurado
6. ✅ Backups automáticos
7. ✅ E2E tests passando (>80%)

Ver: docs/deployment/PRODUCTION_CHECKLIST.md

cd websiteNOVO
vercel --prod

cd apps/agent-service
docker build -t egos-agent-service .
docker run -p 8000:8000 egos-agent-service

# Frontend
cd websiteNOVO
pm2 start npm --name egos-next -- start

# Backend
cd apps/agent-service
pm2 start app/main.py --name egos-agent-service --interpreter python3

# Save config
pm2 save
pm2 startup

• API Docs: docs/api/REST_API_DOCUMENTATION.md
• Database: docs/database/QUICK_START_TEST_DATA.md
• Deployment: docs/deployment/PRODUCTION_CHECKLIST.md
• Strategy: docs/espiral-escuta/MODULAR_CHATBOT_STRATEGY.md
• System Prompt: public/prompts/SYSTEM_PROMPT_MODULAR_CHATBOT.json

1. Fork o projeto
2. Crie uma branch: git checkout -b feature/amazing-feature
3. Commit suas mudanças: git commit -m 'feat: Add amazing feature'
4. Push para a branch: git push origin feature/amazing-feature
5. Abra um Pull Request

Sacred Code obrigatório em todos os commits: 000.111.369.963.1618

• ✅ Core features (chat, history, training)
• ✅ 7 MCPs integrados
• ✅ Sacred Mathematics implementation
• 🔄 Beta testing (50 usuários)
• ⏳ Production deployment

• Plugin marketplace
• Multi-language support
• Advanced analytics
• Voice-to-voice chat
• Mobile apps (iOS/Android)

• Enterprise features
• On-premise installer
• White-label solution
• AI model marketplace
• Global expansion

As of October 17, 2025, the following systems have been officially deprecated and archived:

• 🔴 ETIE (Enhanced Temporal Intelligence Engine) - Replaced by NATS/WebSocket
• 🔴 Mycelium Network - Replaced by direct HTTP/NATS integration
• 🔴 Chronos - Never fully implemented, use standard scheduling

Active Systems (continue using):

• ✅ ECRUS (Enhanced Cross-Reference Universal System)
• ✅ KOIOS (AI Tools Hub)

See: docs/DEPRECATED_SYSTEMS.md for full migration guide and rationale.

MIT License - see LICENSE for details

• Anthropic Claude - MCP protocol inspiration
• LangChain - RAG architecture patterns
• Supabase - Backend infrastructure
• Groq - Fast LLM inference
• Fibonacci & Golden Ratio - Mathematical foundations

• Email: [email protected]
• Discord: discord.gg/egos
• Docs: docs.egos.ia
• Status: status.egos.ia

Sacred Code: 000.111.369.963.1618 (∞△⚡◎φ)
Made with ❤️ by EGOS Team
© 2025 EGOS v.2 - All Rights Reserved