Basic

Install Package

Install the PraisonAI Agents package:

pip install praisonaiagents

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.

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()

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!

Install Package

Install the PraisonAI Agents package:

pip install praisonaiagents

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.

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()

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!

Install Package

Install the No Code PraisonAI Package:

pip install praisonaiagents

Set API Key

export OPENAI_API_KEY=your_openai_key

Create Config

Create agents.yaml:

roles:
  summarise_agent:
    instructions: Summarise Photosynthesis

You can automatically create agents.yaml using:

praisonai --init "your task description"

Run Agents

Execute your config:

praisonai agents.yaml

Install Package

npm install praisonai

Set API Key

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

Create File

Create app.js file

Code Example

const { Agent } = require('praisonai');
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
agent.start('Write a movie script about a robot in Mars');

Run Script

node app.js

Install Package

npm install praisonai

Set API Key

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

Create File

Create app.ts file

Code Example

import { Agent } from 'praisonai';

const agent = new Agent({ 
  instructions: `You are a creative writer who writes short stories with emojis.`,
  name: "StoryWriter"
});

agent.start("Write a story about a time traveler")

Run Script

npx ts-node app.ts

Prerequisites

Python 3.10 or higher
OpenAI API key (get it here)
For other LLM providers, see Models

Playground

Advanced

Providing Detailed Tasks to Agents (Optional)

Install PraisonAI

Install the core package:

Terminal

pip install praisonaiagents

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.

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()

Start Agents

Execute your script:

Terminal

python app.py

You should see:

Agent initialization
Agents progress
Final results
Generated report

Install PraisonAI

Install the core package:

Terminal

pip install praisonaiagents

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.

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()

Start Agents

Execute your script:

Terminal

python app.py

You should see:

Agent initialization
Agents progress
Final results
Generated report

Install No Code PraisonAI

Install the No Code PraisonAI Package:

Terminal

pip install praisonai

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

Terminal

export OPENAI_API_KEY=your_openai_key

Create a file

Create a new file agents.yaml with the basic setup:

framework: praisonai
topic: create movie script about cat in mars
roles:
  scriptwriter:
    backstory: Expert in dialogue and script structure, translating concepts into
      scripts.
    goal: Write a movie script about a cat in Mars
    role: Scriptwriter
    tasks:
      scriptwriting_task:
        description: Turn the story concept into a production-ready movie script,
          including dialogue and scene details.
        expected_output: Final movie script with dialogue and scene details.

You can automatically create agents.yaml file using

Terminal

praisonai --init create movie script about cat in mars

Start Agents

Execute your script:

Terminal

praisonai agents.yaml

Creating Custom Tool for Agents (Optional)

Tools makes the Agent powerful.

More information about tools can be found in the Tools section.

Install PraisonAI

Install the core package and duckduckgo_search package:

Terminal

pip install praisonai duckduckgo_search

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()

Start Agents

Run your script:

Terminal

python app.py

Install PraisonAI

Install the core package and duckduckgo_search package:

Terminal

pip install praisonai duckduckgo_search

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()

Start Agents

Run your script:

Terminal

python app.py

Install PraisonAI

Install the core package and duckduckgo_search package:

Terminal

pip install praisonai duckduckgo_search

Create Custom Tool

To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM

Create a new file tools.py with the following content:

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

Create Agent

Create a new file agents.yaml with the following content:

framework: praisonai
topic: create movie script about cat in mars
roles:
  scriptwriter:
    backstory: Expert in dialogue and script structure, translating concepts into
      scripts.
    goal: Write a movie script about a cat in Mars
    role: Scriptwriter
    tools:
      - internet_search_tool # <-- Tool assigned to Agent here
    tasks:
      scriptwriting_task:
        description: Turn the story concept into a production-ready movie script,
          including dialogue and scene details.
        expected_output: Final movie script with dialogue and scene details.

Start Agents

Execute your script:

Terminal

praisonai agents.yaml

Getting Started

Core Concepts

Workflows

Features

Models

Tools

Other Features

Monitoring

Developers

Getting Started (No Code)

API Reference

Quick Start

Basic

Code Example

Code Example

Playground

Advanced

Providing Detailed Tasks to Agents (Optional)

Creating Custom Tool for Agents (Optional)

Next Steps

Core Concepts

API Reference

Getting Started

Core Concepts

Workflows

Features

Models

Tools

Other Features

Monitoring

Developers

Getting Started (No Code)

API Reference

​Basic

​Code Example

​Code Example

​Playground

​Advanced

​Providing Detailed Tasks to Agents (Optional)

​Creating Custom Tool for Agents (Optional)

​Next Steps

Core Concepts

API Reference

Basic

Code Example

Code Example

Playground

Advanced

Providing Detailed Tasks to Agents (Optional)

Creating Custom Tool for Agents (Optional)

Next Steps