feid-mas 0.1.1a1

Framework Experimental para Integracion de Datos (FEID) - Multi-Agent Systems

FEID-MAS: Enterprise-Grade Multi-Agent System Framework

Language / Idioma: English (README) | Español (README_ES)

FEID-MAS (Framework for Enterprise Intelligent Distributed - Multi-Agent System) is a production-ready Python framework for building professional, scalable, and fault-tolerant autonomous agents.

Overview

FEID-MAS provides a solid foundation for creating intelligent agents that can:

• Process high-volume task queues with priority handling and backpressure management
• Tolerate failures through circuit breakers, automatic retries, and exponential backoff
• Scale horizontally with rate limiting, metrics collection, and health probes
• Communicate across protocols (FIPA, KQML, JSON-RPC, SIMPLE)
• Persist events with automatic rotation and external handler callbacks
• Maintain audit trails with structured logging and incident tracking
• Enforce security policies with sandboxing and whitelist/denylist controls
• Monitor performance with real-time metrics, latency tracking, and error rates

Quick Start (2 Minutes)

Installation

pip install -e .

Your First Agent

from feid.agent import AgenteMaestroNASA

class MyAgent(AgenteMaestroNASA):
    def _technical_work(self, task):
        """Implement your business logic here"""
        return f"Processed: {task}"

# Create and use agent
agent = MyAgent("MyAgent")
agent.send_task("Hello World", priority=1)
agent.graceful_shutdown()

Use Factory Profiles (Pre-configured)

from feid.agent import AgenteFactory, AgenteMaestroNASA

# Quick profile: lightweight, testing
agent = AgenteFactory.quick(AgenteMaestroNASA, name="LightAgent")

# Standard profile: balanced, general-purpose
agent = AgenteFactory.standard(AgenteMaestroNASA, name="StandardAgent")

# Industrial profile: maximum features, production
agent = AgenteFactory.industrial(AgenteMaestroNASA, name="ProductionAgent")

Key Features

Feature	Benefit	Use Case
Priority Queue	Control task execution order	Emergency/VIP task handling
Circuit Breaker	Prevent cascading failures	Fault tolerance
Retry Strategy	Automatic recovery	Transient errors
Rate Limiting	Control request flow	API integration
Metrics & Monitoring	Real-time visibility	Production observability
Audit Logging	Complete event trail	Compliance
Multi-Protocol	FIPA, KQML, JSON-RPC	Enterprise integration
Security Sandbox	Prevent malicious tasks	Untrusted input
Health Probes	Kubernetes-compatible	Container orchestration

Agent Profiles

🚀 Quick Profile

• Best for: Testing, prototyping
• Queue: 50 items | Workers: 1 | Retries: 1
• Minimal features

⚡ Standard Profile

• Best for: General-purpose applications
• Queue: 500 items | Workers: 4 | Retries: 3
• Full features (metrics, audit, rate limiting)

🏭 Industrial Profile

• Best for: Production, high-volume
• Queue: 10,000 items | Workers: 8 | Retries: 5
• Maximum features (circuit breaker, anti-starvation, security)

System Architecture

AgenteMaestroNASA (Orchestrator)
├── ProtocolAdapter (Multi-protocol)
├── EventSink (Persistence)
├── QueueManager (Validation & backpressure)
├── TaskProcessor (Execution & retry)
├── AgentRuntime (Lifecycle)
├── Facades (Simplified APIs)
│   ├── MetricsFacade
│   ├── SecurityFacade
│   └── HealthFacade
└── Enterprise
    ├── CircuitBreaker
    ├── RetryStrategy
    ├── RateLimiter
    └── Security

Documentation

Start here: Documentation Index (EN) | Índice de Documentación (ES)

English

• CONCEPTUAL_GUIDE.md - Core concepts and design
• MULTIAGENT_SYSTEMS.md - Building multi-agent systems
• EXAMPLES.md - Code examples
• API_REFERENCE.md - Complete API documentation
• ARCHITECTURE.md - Internal design
• TROUBLESHOOTING.md - Problem-solving guide

Español

• CONCEPTUAL_GUIDE_ES.md - Conceptos centrales y diseño
• MULTIAGENT_SYSTEMS_ES.md - Sistemas multi-agente
• EXAMPLES_ES.md - Ejemplos de código
• API_REFERENCE_ES.md - Documentación completa de la API
• ARCHITECTURE_ES.md - Diseño interno
• TROUBLESHOOTING_ES.md - Guía de solución de problemas

Typical Workflow

# 1. Create agent
agent = MyAgent("WorkerAgent")

# 2. Send tasks
task_id = agent.send_task("process data", priority=1, ttl=30)

# 3. Monitor
metrics = agent.metrics.get_all()
print(f"Success rate: {1 - agent.metrics.get_error_rate():.2%}")

# 4. Graceful shutdown
stats = agent.graceful_shutdown(timeout=30)
print(f"Processed {stats['successful_tasks']} tasks")

Testing

All 131 tests passing:

pytest tests/ -v

Includes:

• Unit tests (maestro, backlog features)
• Integration tests (multi-protocol communication)
• Stress tests (1000+ tasks, concurrent processing)

Requirements

• Python 3.10+
• No external dependencies (stdlib only)

---

FEID-MAS: Enterprise intelligent agents, simplified.

Enviar tarea con correlation ID (distributed tracing)

m_id = agente.enviar_orden( "procesar-datos", correlation_id="req-2024-001" )

Monitorear salud (incluye estado de circuit breaker y estrategia)

salud = agente.monitorear_salud()

## Documentación

- [Enterprise Features Guide](docs/enterprise_features.md) - Correlation IDs, Circuit Breaker, Retry Strategies
- [Handler Development](docs/handler_guide.md) - Integración con sistemas externos
- [Contributing](CONTRIBUTING.md) - Guía para contribuidores

## Ejemplos

Ver [examples/enterprise_demo.py](examples/enterprise_demo.py) para demostración completa de features.

```bash
python examples/enterprise_demo.py

Estado

Proyecto experimental (v0.1.0). Ver BACKLOG para roadmap y próximos pasos.

Autor

Leon Alberne Torres Restrepo

• GitHub: albernetr
• LinkedIn: leon-alberne-torres-restrepo
• Email: [e-mail]

Disclaimer

Proyecto de desarrollo personal realizado en tiempo personal con equipos personales. No afiliado ni respaldado por empleadores anteriores o actuales. Proveido "como esta", sin garantias.

Licencia

MIT License - Ver LICENSE