Quick Start

Get started with Vyntra in minutes. This guide will help you set up a basic cognitive mesh with autonomous AI agents.

Installation

pip install Vyntra-cogmesh

Basic Example

from Vyntra import CognitiveMesh, Agent

mesh = CognitiveMesh(name="quickstart")
agent = Agent(name="processor-1")
mesh.register(agent)
mesh.run()

Installation

Complete installation guide for different environments.

Using pip

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Unix
venv\Scripts\activate     # Windows

# Install Vyntra
pip install Vyntra-cogmesh

Using Docker

docker pull Vyntra/mesh:latest
docker run -p 50051:50051 Vyntra/mesh:latest

Prerequisites

System requirements and setup instructions.

System Requirements

CPU:4+ cores recommended
RAM:8GB minimum, 16GB+ recommended
Storage:1GB for base installation
Network:Stable internet connection

Development Environment

# Install development tools
pip install -r requirements-dev.txt

# Set up pre-commit hooks
pre-commit install

Dependencies

Core dependencies and optional packages for enhanced functionality.

Required Dependencies

Python:3.8 or higher
Go:1.18 or higher
Node.js:16+

Optional Dependencies

# GPU Support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Monitoring Tools
pip install prometheus-client grafana-api

# Development Tools
pip install pytest pytest-asyncio black mypy

Basic Usage

Learn the fundamentals of using Vyntra.

Creating a Mesh

from Vyntra import CognitiveMesh

mesh = CognitiveMesh(
    name="my-mesh",
    config={
        "max_agents": 10,
        "learning_rate": 0.01
    }
)

Adding Agents

from Vyntra import Agent

# Create text processing agent
text_agent = Agent(
    name="text-processor",
    capabilities=["nlp"]
)

# Create vision processing agent
vision_agent = Agent(
    name="vision-processor",
    capabilities=["vision"]
)

# Register agents
mesh.register(text_agent)
mesh.register(vision_agent)

Best Practice

Start with a small number of agents and gradually scale up as needed. Monitor performance and resource usage.

Architecture Overview

A comprehensive look at Vyntra's distributed cognitive mesh architecture.

System Overview

Vyntra/
├── core/           # Core mesh components
├── agents/         # Agent implementations
├── runtime/        # WebAssembly runtime
├── network/        # Communication layer
└── security/       # Security components

Design Philosophy

Distributed First:Built for scalable, distributed operations
Security by Design:End-to-end encryption and sandboxing
Privacy Preserving:Federated learning and differential privacy
Cloud Native:Kubernetes-ready, containerized deployment

Key Features

Vyntra combines high-performance Go networking, Python's AI capabilities, and WebAssembly security in a unified platform.

Mesh Architecture

The Vyntra cognitive mesh is built on a distributed architecture that enables seamless communication and collaboration between AI agents.

Core Components

Mesh Controller:Central orchestrator for agent coordination
Agent Runtime:WebAssembly-based secure execution environment
Learning Engine:Federated learning and model management
Communication Layer:High-performance Go messaging system

Design Principles

Built on cloud-native principles: containerized, scalable, and resilient. Each component is independently scalable.

Agent Communication

Agents communicate through a high-performance message passing system.

Message Types

from Vyntra.messages import Message

# Task message
task_msg = Message(
    type="TASK",
    payload={"action": "process_data"}
)

# Learning update
learning_msg = Message(
    type="MODEL_UPDATE",
    payload={"weights": model.get_weights()}
)

Communication Patterns

Direct:Point-to-point agent communication
Broadcast:Send to all agents in the mesh
Pub/Sub:Topic-based messaging
Request/Response:Synchronous patterns

Federated Learning

Privacy-preserving collaborative learning across distributed agents.

Training Configuration

from Vyntra.learning import FederatedConfig

config = FederatedConfig(
    rounds=10,
    min_agents=3,
    aggregation="fedavg",
    privacy={
        "differential_privacy": True,
        "epsilon": 0.1
    }
)

Model Updates

# Agent-side training
local_update = await agent.train_local(data)
await agent.submit_update(local_update)

# Mesh-side aggregation
global_model = await mesh.aggregate_updates(
    min_updates=5,
    timeout=300
)

Security Model

Understanding Vyntra's comprehensive security architecture.

Security Layers

Network Security:TLS 1.3, mutual authentication
Agent Isolation:WebAssembly sandboxing
Data Privacy:Differential privacy, encryption at rest
Access Control:Role-based access control (RBAC)

Authentication Flow

from Vyntra.security import AuthFlow

auth = AuthFlow(
    method="jwt",
    key_rotation=True,
    mfa_required=True,
    session_timeout=3600
)

Security Best Practices

Always use secure communication channels and regularly rotate authentication credentials in production environments.

Mesh Configuration

Configure your cognitive mesh for optimal performance and scalability.

Basic Configuration

from Vyntra import MeshConfig

config = MeshConfig(
    name="production-mesh",
    version="1.0.0",
    environment="production",
    max_agents=100,
    timeout=30
)

Advanced Settings

advanced_config = {
    "retry_policy": {
        "max_attempts": 3,
        "backoff_factor": 1.5
    },
    "load_balancing": {
        "strategy": "least_loaded",
        "health_check_interval": 30
    }
}

Agent Configuration

Configure individual agents for specific tasks and capabilities.

Agent Types

from Vyntra.agents import ProcessorAgent

agent = ProcessorAgent(
    name="text-processor",
    capabilities=["nlp", "sentiment"],
    models={
        "nlp": "bert-base-uncased",
        "sentiment": "distilbert"
    }
)

Runtime Settings

runtime_config = {
    "execution": {
        "timeout": 60,
        "max_retries": 3
    },
    "resources": {
        "memory": "2Gi",
        "cpu": "1"
    }
}

Network Settings

Configure network behavior and communication patterns.

Protocol Configuration

network_config = {
    "protocols": {
        "mesh": {
            "type": "grpc",
            "port": 50051,
            "max_connections": 1000
        },
        "monitoring": {
            "type": "http",
            "port": 8080
        }
    }
}

Service Discovery

discovery_config = {
    "mechanism": "dns",
    "interval": 30,
    "zones": ["us-east", "us-west"]
}

Network Security

Always use TLS for production deployments and configure appropriate firewall rules.

Security Settings

Configure security settings for your mesh deployment.

Authentication

from Vyntra.security import SecurityConfig

security = SecurityConfig(
    auth_method="jwt",
    token_expiry="24h",
    refresh_enabled=True,
    allowed_origins=["https://yourdomain.com"]
)

Access Control

rbac_config = {
    "roles": {
        "admin": ["read", "write", "manage"],
        "operator": ["read", "write"],
        "viewer": ["read"]
    },
    "default_role": "viewer"
}

Resource Limits

Configure resource constraints for optimal performance.

Memory Management

memory_config = {
    "heap_size": "4Gi",
    "cache_size": "2Gi",
    "buffer_pool": "1Gi"
}

Resource Planning

Carefully plan resource allocation based on your workload patterns and agent requirements.

Custom Agents

Create and deploy custom agents with specialized capabilities.

Agent Template

from Vyntra import BaseAgent

class CustomAgent(BaseAgent):
    def __init__(self, name, config):
        super().__init__(name, config)
        self.capabilities = ["custom_task"]
    
    async def process_task(self, task):
        # Custom task processing logic
        result = await self.execute_custom_logic(task)
        return result

    async def execute_custom_logic(self, task):
        # Implement your custom processing here
        pass

Agent Registration

# Register custom agent
custom_agent = CustomAgent(
    name="specialized-agent",
    config={
        "model_path": "models/custom.pt",
        "batch_size": 32
    }
)

mesh.register_agent(custom_agent)

Best Practices

Always implement proper error handling and resource cleanup in custom agents. Test thoroughly before deployment.

Scaling

Scale your mesh infrastructure to handle increased workloads.

Horizontal Scaling

from Vyntra.scaling import AutoScaler

scaler = AutoScaler(
    min_instances=3,
    max_instances=10,
    target_cpu_utilization=80,
    cooldown_period=300
)

Load Balancing

load_balancer_config = {
    "algorithm": "round_robin",
    "health_check": {
        "interval": 30,
        "timeout": 5,
        "healthy_threshold": 2,
        "unhealthy_threshold": 3
    }
}

Scaling Considerations

Monitor resource usage and costs when implementing auto-scaling. Set appropriate limits to prevent runaway scaling.

Monitoring

Monitor and observe your mesh deployment.

Metrics Collection

from Vyntra.monitoring import MetricsCollector

metrics = MetricsCollector(
    backend="prometheus",
    interval=15,
    exporters=["node", "mesh"]
)

Alerting

alert_rules = {
    "high_latency": {
        "metric": "request_duration_seconds",
        "threshold": 5,
        "duration": "5m",
        "severity": "warning"
    },
    "error_rate": {
        "metric": "request_errors_total",
        "threshold": 0.1,
        "duration": "5m",
        "severity": "critical"
    }
}

Monitoring Dashboard

Use Grafana or similar tools to visualize metrics and create custom dashboards for your mesh deployment.

Deployment

Deploy your mesh in production environments.

Container Deployment

version: '3'
services:
  mesh-controller:
    image: Vyntra/controller:latest
    ports:
      - "50051:50051"
    environment:
      - NODE_ENV=production
      - MESH_NAME=production
    volumes:
      - mesh-data:/data

Cloud Deployment

from Vyntra.cloud import CloudProvider

provider = CloudProvider(
    type="aws",
    region="us-east-1",
    auto_scale=True,
    spot_instances=True
)

Troubleshooting

Common issues and their solutions.

Common Issues

Connection Issues

If agents can't connect, check network settings and ensure ports are open.

Debugging

from Vyntra.debug import Debug

debug = Debug(
    level="verbose",
    log_file="debug.log",
    include_traces=True
)

Mesh API

Core API methods for managing the cognitive mesh.

Lifecycle Management

from Vyntra import CognitiveMesh

# Initialize mesh
mesh = CognitiveMesh(name="production")
await mesh.initialize()

# Start operations
await mesh.start()

# Graceful shutdown
await mesh.shutdown()

Agent Management

# Register agent
await mesh.register_agent(agent)

# Get agent status
status = await mesh.get_agent_status(agent_id)

# Remove agent
await mesh.remove_agent(agent_id)

API Versioning

The Mesh API follows semantic versioning. Breaking changes are only introduced in major versions.

Agent API

API methods for implementing and managing agents.

Agent Implementation

from Vyntra import Agent

class CustomAgent(Agent):
    async def initialize(self):
        await self.load_models()
        await self.connect_services()
    
    async def process(self, message):
        result = await self.handle_message(message)
        return result
    
    async def cleanup(self):
        await self.save_state()
        await self.disconnect()

Capability Management

# Add capabilities
agent.add_capability("nlp")
agent.add_capability("vision")

# Check capabilities
if agent.has_capability("nlp"):
    result = await agent.process_text(text)

# Remove capability
agent.remove_capability("vision")

Resource Management

Always implement proper cleanup in your agents to prevent resource leaks.

Events

Event system for mesh and agent interactions.

Event Types

from Vyntra.events import EventHandler

# System events
@mesh.on("agent.joined")
async def handle_join(event):
    agent_id = event.agent_id
    print(f"Agent {agent_id} joined")

# Custom events
@mesh.on("task.completed")
async def handle_completion(event):
    task_id = event.task_id
    await process_completion(task_id)

Event Filtering

# Pattern matching
@mesh.on("task.*")
async def handle_task_events(event):
    if event.type == "task.completed":
        await process_completion(event)
    elif event.type == "task.failed":
        await handle_failure(event)

# Conditional filtering
@mesh.on("resource.low", 
    condition=lambda e: e.severity == "critical")
async def handle_critical_resource(event):
    await scale_resources(event.resource_type)

Event Handling

Keep event handlers lightweight and non-blocking. Use background tasks for heavy processing.

Error Handling

Handle errors and exceptions in your mesh applications.

Custom Error Types

from Vyntra.exceptions import MeshError

class ModelError(MeshError):
    def __init__(self, message, model_id):
        super().__init__(message)
        self.model_id = model_id

try:
    await model.train()
except ModelError as e:
    await handle_model_error(e)

State Management

Manage and persist mesh and agent state.

State Store

from Vyntra.state import StateManager

state = StateManager(
    backend="etcd",
    persistence=True,
    sync_interval=30
)

Checkpointing

# Save mesh state
await mesh.save_checkpoint("checkpoint-1")

# Restore from checkpoint
await mesh.restore_checkpoint("checkpoint-1")