Skip to main content
Create one focused agent with instructions, tools, and memory—then iterate from a single prompt. 
from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
)
agent.start("Say hello in one sentence.")
The user sends a prompt; the agent calls the model (and tools if configured) and returns text. This guide walks you through creating your first AI agent with PraisonAI.

​
Quick Start

1

Install

pip install praisonaiagents
export OPENAI_API_KEY=your_api_key
2

Create your first agent

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
)

result = agent.start("Hello! What can you help me with?")
print(result)

​
Prerequisites

pip install praisonaiagents
Set your API key: 
export OPENAI_API_KEY="your-api-key"

​
Basic Agent

from praisonaiagents import Agent

# Create a simple agent
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant."
)

# Run the agent
result = agent.start("Hello! What can you help me with?")
print(result)

​
Agent with Custom Role

from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    role="Senior Research Analyst",
    goal="Provide accurate and comprehensive research",
    backstory="You are an experienced researcher with expertise in AI and technology.",
    instructions="Always cite sources and provide balanced perspectives."
)

result = agent.start("What are the latest trends in AI?")
print(result)

​
Agent with Tools

from praisonaiagents import Agent
from praisonaiagents import internet_search

agent = Agent(
    name="Web Researcher",
    instructions="Search the web to find accurate information.",
    tools=[internet_search]
)

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

​
Agent with Memory

from praisonaiagents import Agent, Memory

# Create memory instance
memory = Memory()

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant with memory.",
    memory=memory
)

# First conversation
agent.start("My name is Alice")

# Agent remembers the name
result = agent.start("What's my name?")
print(result)  # "Your name is Alice"

​
Agent Configuration Options

ParameterTypeDescription
namestrAgent name
rolestrAgent’s role description
goalstrWhat the agent aims to achieve
backstorystrBackground context for the agent
instructionsstrSystem instructions
modelstrLLM model to use (default: gpt-4o-mini)
toolslistList of tools available to the agent
memoryMemoryMemory instance for persistence
verboseboolEnable verbose logging

​
Using Different Models

from praisonaiagents import Agent

# OpenAI
agent = Agent(name="GPT Agent", llm="gpt-4o")

# Anthropic
agent = Agent(name="Claude Agent", llm="claude-3-5-sonnet-20241022")

# Ollama (local)
agent = Agent(name="Local Agent", llm="ollama/llama3.2")

# Google
agent = Agent(name="Gemini Agent", llm="gemini/gemini-2.0-flash")

​
Chat Mode

from praisonaiagents import Agent

agent = Agent(
    name="ChatBot",
    instructions="You are a friendly chatbot."
)

# Multi-turn conversation
messages = [
    {"role": "user", "content": "Hi!"},
    {"role": "assistant", "content": "Hello! How can I help?"},
    {"role": "user", "content": "Tell me a joke"}
]

result = agent.chat(messages)
print(result)

​
Best Practices

Clear, specific instructions lead to more consistent and useful agent behavior.
Use tools to extend what your agent can do - search, code execution, file operations, and more.
Add memory=True for agents that need to remember information across multiple turns.
Start with llm='gpt-4o-mini' for testing - switch to gpt-4o when you need more capability.

​
Next Steps

Multi-Agent Systems

Orchestrate multiple agents working together

Tools

Extend your agent with tools