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Deploy your PraisonAI agents to production environments. 
from praisonaiagents import Agent

agent = Agent(
    name="Production Agent",
    instructions="Handle production workloads.",
)

agent.start("Ready for production deployment")
The user chooses a hosting pattern, configures secrets and scaling, then points clients at the running agent.

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Quick Start

1

Simple Usage

Start with an agent-centric approach:
from praisonaiagents import Agent

# Create production-ready agent
agent = Agent(
    name="Production Agent",
    instructions="Handle production workloads",
)

# Deploy to production
agent.start("Ready for production deployment")
2

With Configuration

Choose between different deployment options based on your needs.

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Deployment Methods

24/7 Production Setup

Start here for always-on deployment with process supervision

Advanced Production

Comprehensive production guide with monitoring and scaling

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Deployment Options

OptionBest ForComplexityGuide
Production MinimalAlways-on 24/7 operationLow📖 Guide
Docker ComposeContainerized deploymentsLow📖 Guide
KubernetesScalable productionMedium📖 Guide
Cloud FunctionsServerlessLow📖 Guide
API ServerREST/WebSocket APIsMedium📖 Guide

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Quick Deploy with CLI

# Deploy as API server
praisonai deploy --type api

# Deploy as Docker container
praisonai deploy --type docker

# Deploy to cloud
praisonai deploy --type cloud --provider aws

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API Server

from praisonaiagents import Agent
from praisonaiagents.api import serve

agent = Agent(name="API Agent")

# Start API server
serve(agent, host="0.0.0.0", port=8000)

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Environment Variables

# Required
export OPENAI_API_KEY="sk-..."

# Optional
export PRAISONAI_LOG_LEVEL="INFO"
export PRAISONAI_DB_URL="postgresql://..."

Production Minimal

Always-on deployment

Docker Deployment

Containerised agents