Praison AI

OpenAI Quickstart

Complete OpenAI onboarding guide for all platforms – Windows, macOS, and Linux

Get started with PraisonAI and OpenAI in under 5 minutes, with instructions that work on Windows, macOS, and Linux.

python
from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful AI assistant.",
)
agent.start("What is the capital of France?")

The user installs PraisonAI, sets an API key, and runs their first agent in one session.

Diagram

OpenAI Quickstart Flow

📦 Install

🔑 Configure API Key

✅ Verify Setup

🤖 First Agent

🚀 Advanced Usage

Quick Start

  1. Simple Usage
    bash
    pip install praisonaiagents
export OPENAI_API_KEY="<your-openai-api-key>"
    python
    from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful AI assistant.",
)
print(agent.start("What is the capital of France?"))
  2. With Configuration
    python
    from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="You are an expert researcher.",
    model="gpt-4o",
    temperature=0.7,
    max_tokens=1000,
)
print(agent.start("Summarise renewable energy trends"))

    Verify your setup with the CLI:

    bash
    pip install praisonai
praisonai doctor env --json

    macOS/Linux

    bash
    export OPENAI_API_KEY="<your-openai-api-key>"

    Windows PowerShell

    powershell
    $env:OPENAI_API_KEY="<your-openai-api-key>"

    .env file

    env
    OPENAI_API_KEY=<your-openai-api-key>

How It Works

Diagram
OpenAIAgentUserOpenAIAgentUserCreate with instructionsstart(“query”)API request with contextResponseFormatted result

The agent workflow follows these steps:

StepDescription
CreateInitialize agent with instructions and configuration
ExecuteProcess user input with OpenAI’s LLM
ReturnFormat and return the response

Configuration Options

Environment Variables

VariableDescriptionExample
OPENAI_API_KEYYour OpenAI API keysk-proj-abc123...
OPENAI_BASE_URLCustom API endpoint (optional)https://api.openai.com/v1
OPENAI_MODELDefault model (optional)gpt-4o-mini

Agent Configuration

python
from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    instructions="You are an expert researcher.",
    model="gpt-4o",           # Specify model
    temperature=0.7,          # Creativity (0-1)
    max_tokens=1000,          # Response length
    output=True              # Enable verbose output
)

Common Patterns

Simple Q&A Agent

python
from praisonaiagents import Agent

agent = Agent(
    name="Helper",
    instructions="Answer questions clearly and concisely."
)

response = agent.start("Explain photosynthesis in simple terms")
print(response)

Agent with Memory

python
from praisonaiagents import Agent

agent = Agent(
    name="Personal Assistant",
    instructions="Remember our conversation and provide personalized help.",
    memory=True  # Enable conversation memory
)

# First interaction
agent.start("My name is Alex and I like Python")

# Later interaction - agent remembers
response = agent.start("What programming language do I like?")
print(response)

CLI Usage (requires pip install praisonai)

bash
# Simple prompt
praisonai "What are the benefits of renewable energy?"

# Save output to file
praisonai "Write a haiku about coding" > poem.txt

# Use specific model
praisonai --model gpt-4o "Explain quantum computing"

Advanced Features

Multi-Agent Teams

python
from praisonaiagents import Agent, Agents

# Create specialized agents
researcher = Agent(
    name="Researcher", 
    instructions="Research topics thoroughly"
)

writer = Agent(
    name="Writer", 
    instructions="Write clear, engaging content"
)

# Create team
team = Agents(agents=[researcher, writer])
result = team.start("Write a blog post about AI safety")
print(result)

YAML Workflows

Create workflow.yaml:

yaml
framework: praisonai
topic: "Analyze market trends"
agents:
  - name: "Data Analyst"
    instructions: "Analyze market data and trends"
  - name: "Report Writer"  
    instructions: "Create executive summaries"

Run with:

bash
praisonai workflow.yaml --framework praisonai

Tools Integration

python
from praisonaiagents import Agent

agent = Agent(
    name="Web Researcher",
    instructions="Research topics using web search",
    tools=["websearch"]  # Enable web search tool
)

result = agent.start("Find the latest news about renewable energy")
print(result)

Troubleshooting

'export' command not found (Windows)

Windows doesn’t recognize the export command. Use these alternatives:

PowerShell:

powershell
$env:OPENAI_API_KEY = $env:OPENAI_API_KEY  # set in your shell first

Command Prompt:

cmd
set OPENAI_API_KEY=%OPENAI_API_KEY%

Permanent solution: Use a .env file in your project directory.

'praisonai' command not found

This means you installed praisonaiagents (Python SDK only) but need the CLI tools.

Solution:

bash
pip install praisonai

The packages serve different purposes:

  • praisonaiagents – Python SDK for scripting
  • praisonai – CLI tools and YAML workflows
API key validation fails

If praisonai doctor env shows API key errors:

  1. Check the key format: Should start with sk-
  2. Verify environment: Run echo $OPENAI_API_KEY (bash) or echo $env:OPENAI_API_KEY (PowerShell)
  3. Use .env file: More reliable than environment variables
  4. Check permissions: Ensure you have valid OpenAI credits/usage limits
Encoding errors on Windows

If you see garbled text or encoding errors:

  1. Use JSON output: Add --json flag to CLI commands
  2. Check terminal encoding: Use Windows Terminal instead of Command Prompt
  3. Set encoding: Add chcp 65001 before running commands

Best Practices

Secure your API keys
  • Never hardcode API keys in source code
  • Use .env files for local development
  • Set proper file permissions: chmod 600 .env
  • Use environment variables in production
  • Rotate keys regularly
Choose the right model
  • gpt-4o-mini – Fast and cost-effective for simple tasks
  • gpt-4o – Best performance for complex reasoning
  • gpt-3.5-turbo – Good balance of speed and capability
  • Start with gpt-4o-mini and upgrade as needed
Optimize for your use case
  • Use temperature=0 for consistent, factual responses
  • Use temperature=0.7-1.0 for creative tasks
  • Set appropriate max_tokens to control response length
  • Enable memory=True for conversational agents
Monitor usage and costs
  • Check your OpenAI dashboard regularly
  • Set usage alerts in OpenAI console
  • Use shorter prompts when possible
  • Cache responses for repeated queries

Next Steps

Advanced Agent Features

Explore planning, reflection, and advanced reasoning capabilities

 Multi-Agent Teams

Build collaborative agent teams for complex workflows

 Tools & Integrations

Add web search, file tools, and custom integrations

 Memory & Knowledge

Implement persistent memory and knowledge bases

Related

Installation Guide

Complete installation instructions for all platforms

 CLI Reference

Full command-line interface documentation