Deployment Guide

Deployment Options

graph LR
    A[Workforce] --> B[Export]
    B --> C[Cloud Deploy]
    B --> D[Local Deploy]
    B --> E[Replit Deploy]
    style A fill:#2563eb,stroke:#1e40af,color:#fff
    style B fill:#059669,stroke:#047857,color:#fff
    style C fill:#7c3aed,stroke:#6d28d9,color:#fff
    style D fill:#7c3aed,stroke:#6d28d9,color:#fff
    style E fill:#7c3aed,stroke:#6d28d9,color:#fff

Export Process

1. Navigate to File > Export As > Python Code

2. Select export location

3. Configure export settings

interface ExportConfig {
    format: 'python' | 'notebook';
    includeComments: boolean;
    environmentSetup: boolean;
}

Deployment Methods

Cloud Deployment

Enterprise-grade deployment with managed infrastructure.

# Contact [e-mail] for:
FEATURES = {
    "managed_infrastructure": True,
    "auto_scaling": True,
    "monitoring": True,
    "enterprise_support": True
}

Local Deployment

Prerequisites

# Using pip
pip install pyautogen==0.2.7

# Using conda
conda install pyautogen==0.2.7

# Using poetry
poetry add pyautogen==0.2.7

Environment Setup

# requirements.txt
pyautogen==0.2.7
python-dotenv>=0.19.0

Replit Deployment

Setup Steps

1. Create Python project

2. Import source code

3. Configure dependencies:

# pyproject.toml
[tool.poetry.dependencies]
python = "^3.9"
pyautogen = "0.2.7"

Execution Configuration

execution_config = {
    "environment": {
        "API_KEY": "your_api_key",
        "PYTHONPATH": "${workspaceFolder}"
    },
    "runtime": {
        "memory_limit": "4G",
        "timeout": 3600
    }
}

Best Practices

Local Development

• Use virtual environments

• Maintain API key security

• Enable error logging

• Monitor resource usage

Production Deployment

• Implement error handling

• Set up monitoring

• Configure auto-scaling

• Manage API rate limits

Troubleshooting

Issue	Resolution
Import Errors	Verify package installation
API Key Issues	Check environment variables
Memory Errors	Adjust resource limits
Timeout Issues	Configure execution timeouts