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.

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.

Quick Start

1. Simple Usage

   pip install praisonaiagents
   export OPENAI_API_KEY="<your-openai-api-key>"
   

   Get your API key from OpenAI Dashboard and set it as an environment variable before running.

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

   Use pip install praisonai instead if you need CLI tools and YAML workflows.

2. With Configuration

   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:

   pip install praisonai
   praisonai doctor env --json
   

   On PraisonAI 4.6.104+, you can also run praisonai doctor --live --json to validate your OpenAI key against the API (not just check its presence).

   macOS/Linux

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

   Windows PowerShell

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

   .env file

   OPENAI_API_KEY=<your-openai-api-key>
   

   Get your API key from OpenAI Dashboard. Never commit API keys to version control.

---

How It Works

The agent workflow follows these steps:

Step	Description
Create	Initialize agent with instructions and configuration
Execute	Process user input with OpenAI’s LLM
Return	Format and return the response

---

Configuration Options

Environment Variables

Variable	Description	Example
OPENAI_API_KEY	Your OpenAI API key	sk-proj-abc123...
OPENAI_BASE_URL	Custom API endpoint (optional)	https://api.openai.com/v1
OPENAI_MODEL	Default model (optional)	gpt-4o-mini

Agent Configuration

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

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

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)

# 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

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)

Multi-agent functionality requires the latest version. If you encounter issues, ensure you have the newest release.

YAML Workflows

Create workflow.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:

praisonai workflow.yaml --framework praisonai

Tools Integration

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:

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

Command Prompt:

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:

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

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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

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Related

Installation Guide

Complete installation instructions for all platforms

CLI Reference

Full command-line interface documentation