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Basic

1

Install Package

Install the PraisonAI Agents package:
pip install praisonaiagents
2

Set API Key

export OPENAI_API_KEY=your_openai_key
Generate your OpenAI API key from OpenAI. Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the Models for more information.
3

Create Agents

Create app.py:
from praisonaiagents import Agent, PraisonAIAgents

# Create a simple agent
summarise_agent = Agent(instructions="Summarise Photosynthesis")

# Run the agent
agents = PraisonAIAgents(agents=[summarise_agent])
agents.start()
4

Run Agents

Execute your script:
python app.py
You’ll see:
  • Agent initialization
  • Task execution progress
  • Final results
You have successfully CreatedAI Agents and made them work for you!
Prerequisites
  • Python 3.10 or higher
  • OpenAI API key (get it here)
  • For other LLM providers, see Models

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Playground

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Advanced

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Providing Detailed Tasks to Agents (Optional)

1

Install PraisonAI

Install the core package:
Terminal
pip install praisonaiagents
2

Configure Environment

Terminal
export OPENAI_API_KEY=your_openai_key
Generate your OpenAI API key from OpenAI Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the Models for more information.
3

Create Agent

Create app.py:
from praisonaiagents import Agent, Task, PraisonAIAgents

# Create an agent
researcher = Agent(
    name="Researcher",
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="You are an expert at a technology research group",
    verbose=True,
    llm="gpt-4o"
)

# Define a task
task = Task(
    name="research_task",
    description="Analyze 2024's AI advancements",
    expected_output="A detailed report",
    agent=researcher
)

# Run the agents
agents = PraisonAIAgents(
    agents=[researcher],
    tasks=[task],
    verbose=False
)

result = agents.start()
4

Start Agents

Execute your script:
Terminal
python app.py
You should see:
  • Agent initialization
  • Agents progress
  • Final results
  • Generated report

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Creating Custom Tool for Agents (Optional)

Tools makes the Agent powerful.
More information about tools can be found in the Tools section.
1

Install PraisonAI

Install the core package and duckduckgo_search package:
Terminal
pip install praisonai duckduckgo_search
2

Create Tools and Agents

from praisonaiagents import Agent, Task, PraisonAIAgents
from duckduckgo_search import DDGS
from typing import List, Dict

# 1. Tool
def internet_search_tool(query: str) -> List[Dict]:
    """
    Perform Internet Search
    """
    results = []
    ddgs = DDGS()
    for result in ddgs.text(keywords=query, max_results=5):
        results.append({
            "title": result.get("title", ""),
            "url": result.get("href", ""),
            "snippet": result.get("body", "")
        })
    return results

# 2. Agent
data_agent = Agent(
    name="DataCollector",
    role="Search Specialist",
    goal="Perform internet searches to collect relevant information.",
    backstory="Expert in finding and organising internet data.",
    tools=[internet_search_tool],
    self_reflect=False
)

# 3. Tasks
collect_task = Task(
    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
    expected_output="List of search results with titles, URLs, and snippets.",
    agent=data_agent,
    name="collect_data",
)

# 4. Start Agents
agents = PraisonAIAgents(
    agents=[data_agent],
    tasks=[collect_task],
    process="sequential"
)

agents.start()
3

Start Agents

Run your script:
Terminal
python app.py

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Next Steps

Core Concepts

Learn about agents, tasks, and processes

API Reference

Explore detailed API documentation