Create an Agent, set one API key, and run it in three lines of code.

from praisonaiagents import Agent

agent = Agent(instructions="Summarise Photosynthesis")
agent.start()

Pick the provider whose key you already have — PraisonAI selects the matching model automatically.

Basic

Code
No Code
JavaScript
TypeScript

Install Package

Install the PraisonAI Agents package:

pip install praisonaiagents

Set API Key

Set the key for whichever provider you use — PraisonAI picks the right model automatically:

OpenAI
Anthropic
Gemini
Groq
Ollama

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
praisonai "hello"

export ANTHROPIC_API_KEY="${ANTHROPIC_API_KEY:?Set ANTHROPIC_API_KEY in your shell}"
praisonai "hello"

export GEMINI_API_KEY="${GEMINI_API_KEY:?Set GEMINI_API_KEY in your shell}"
praisonai "hello"

export GROQ_API_KEY="${GROQ_API_KEY:?Set GROQ_API_KEY in your shell}"
praisonai "hello"

export OLLAMA_HOST=http://localhost:11434
praisonai "hello"

No --model flag needed — the matching provider’s default model is selected automatically. See Provider Auto-Detection for the full list.

Create Agents

Create app.py:

from praisonaiagents import Agent, AgentTeam

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

# Run the agent
team = AgentTeam(agents=[summarise_agent])
team.start()

from praisonaiagents import Agent, AgentTeam

# Create agents with specific roles
diet_agent = Agent(
    instructions="Give me 5 healthy food recipes",
)

blog_agent = Agent(
    instructions="Write a blog post about the food recipes",
)

# Run multiple agents
team = AgentTeam(agents=[diet_agent, blog_agent])
team.start()

For the absolute shortest Python entry point — no class instantiation — use praisonai.run("agents.yaml"). See the one-liner guide.

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="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create Config

Create agents.yaml:

agents:  # Canonical
  summarise_agent:
    instructions: Summarise Photosynthesis

agents:  # Canonical
  diet_agent:
    instructions: Give me 5 healthy food recipes
  blog_agent:
    instructions: Write a blog post about the food recipes

You can automatically create agents.yaml using:

praisonai --init "your task description"

First time? Run praisonai setup to pick a provider and save your API key — --init will print provider guidance if no key is configured.

Run Agents

Execute your config:

praisonai agents.yaml

Install Package

npm install praisonai

Set API Key

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

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');

const { Agent, AgentTeam } = require('praisonai');

const researchAgent = new Agent({ instructions: 'Research about AI' });
const summariseAgent = new Agent({ instructions: 'Summarise research agent\'s findings' });

const team = new AgentTeam({ agents: [researchAgent, summariseAgent] });
team.start();

Run Script

node app.js

Install Package

npm install praisonai

yarn add praisonai

Set API Key

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

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")

import { Agent, AgentTeam } from 'praisonai';

const storyAgent = new Agent({
  instructions: "Generate a very short story (2-3 sentences) about artificial intelligence with emojis.",
  name: "StoryAgent"
});

const summaryAgent = new Agent({
  instructions: "Summarize the provided AI story in one sentence with emojis.",
  name: "SummaryAgent"
});

const team = new AgentTeam({
  agents: [storyAgent, summaryAgent]
});

team.start()

Run Script

npx ts-node app.ts

Prerequisites

Python 3.10 or higher
An API key for any supported provider (OpenAI, Anthropic, Google, Groq, Cohere) or a running Ollama instance — OpenAI is not required
For the full list of providers, see Models

Don’t have an OpenAI key? PraisonAI picks a default model that matches whichever provider credential you do have set — set ANTHROPIC_API_KEY, GEMINI_API_KEY, GROQ_API_KEY, or run a local OLLAMA_HOST and the same praisonai command works without --model. Full precedence.

Advanced

Providing Detailed Tasks to Agents (Optional)

Code
No Code

Install PraisonAI

Install the core package:

Terminal

pip install praisonaiagents

Configure Environment

Terminal

export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

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, AgentTeam

# 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",
    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
team = AgentTeam(
    agents=[researcher],
    tasks=[task]
)

result = team.start()

from praisonaiagents import Agent, Task, AgentTeam

# Create multiple agents
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",
    llm="gpt-4o"
)

writer = Agent(
    name="Writer",
    role="Tech Content Strategist",
    goal="Craft compelling content on tech advancements",
    backstory="You are a content strategist",
    llm="gpt-4o"
)

# Define multiple tasks
task1 = Task(
    name="research_task",
    description="Analyze 2024's AI advancements",
    expected_output="A detailed report",
    agent=researcher
)

task2 = Task(
    name="writing_task",
    description="Create a blog post about AI advancements",
    expected_output="A blog post",
    agent=writer
)

# Run with hierarchical process
team = AgentTeam(
    agents=[researcher, writer],
    tasks=[task1, task2],
    process="hierarchical",
    manager_llm="gpt-4o"
)

result = team.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="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

Create a file

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

framework: praisonai
topic: create movie script about cat in mars
agents:  # Canonical: use 'agents' instead of 'roles'
  scriptwriter:
    instructions:  # Canonical: use 'instructions' instead of '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

First time? Run praisonai setup to pick a provider and save your API key — --init will print provider guidance if no key is configured.

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.

Code
No Code

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, AgentTeam
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],
    reflection=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
team = AgentTeam(
    agents=[data_agent],
    tasks=[collect_task],
    process="sequential"
)

team.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
agents:  # Canonical: use 'agents' instead of 'roles'
  scriptwriter:
    instructions:  # Canonical: use 'instructions' instead of '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

How It Works

An Agent takes your instructions, calls the LLM behind the scenes, and returns the result.

Best Practices

Set keys in your shell, never inline

Export OPENAI_API_KEY (or your provider’s key) in your shell or a .env file. Never paste the raw string into Agent(llm=...).

Start with the shortest shape

Agent(instructions="…").start() is the shortest working agent. Add name, role, tools, or a full Task only when you need them.

Skip --model on first runs

PraisonAI picks the right default for whichever provider credential you set. See Provider Auto-Detection.

Add tools progressively

Give an agent a plain Python function via tools=[...] when it needs to search, fetch, or compute. Keep tool functions small and typed.

Models

Choose an LLM provider and model.

Tools

Extend agents with functions and integrations.

​Basic

​Code Example

​Code Example

​Advanced

​Providing Detailed Tasks to Agents (Optional)

​Creating Custom Tool for Agents (Optional)

​How It Works

​Best Practices

​Related

Models

Tools

Basic

Code Example

Code Example

Advanced

Providing Detailed Tasks to Agents (Optional)

Creating Custom Tool for Agents (Optional)

How It Works

Best Practices

Related