Introduction

Welcome to PraisonAI — build, deploy, and manage multi-agent AI systems with self-reflection, 100+ LLM integrations, and production-ready tooling.

PraisonAI builds and deploys autonomous AI agents that understand, reason, and execute complex tasks — starting from a single line of code.

python

from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant")
agent.start("Write a haiku about AI")

What is PraisonAI?

PraisonAI is a powerful Multi-Agent Framework for building and deploying autonomous, self-improving AI agents that can understand, reason, and execute complex tasks.

Welcome to PraisonAI

Build powerful autonomous, self-improving agents that understand, decide, and execute with unprecedented capability.

Core Components

Diagram

🤖 Agent

Single AI worker with tools and instructions

👥 AgentTeam

Multi-agent orchestration with sequential/hierarchical process

🔄 AgentFlow

Step-based pipelines with route, parallel, loop patterns

⚡ AgentOS

Production deployment with API, webhooks, scheduler

When to Use What

Diagram

Scenario	Use This
Chat with one AI	`Agent`
Research → Analyze → Write	`AgentTeam` or `AgentFlow`
Route to specialists	`AgentFlow` + `route()`
Parallel processing	`AgentFlow` + `parallel()`
Production API	`AgentOS`

Use Cases

Customer Service

Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.

Data Analysis

Create agents that can process, analyze, and derive insights from complex datasets.

Content Creation

Deploy agents that can generate, edit, and optimize content across various formats.

Process Automation

Automate complex workflows with intelligent agents that can coordinate and execute tasks.

Getting Started

Python

Install
bash
```
pip install praisonaiagents
```

Set API Key

bash

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

Create Agent

Single Agent

from praisonaiagents import Agent

agent = Agent(instructions="You are a helpful assistant")
agent.start("Write a haiku about AI")

Multi-Agent Team

from praisonaiagents import Agent, AgentTeam

researcher = Agent(instructions="Research topics")
writer = Agent(instructions="Write content")

team = AgentTeam(agents=[researcher, writer])
team.start()

AgentFlow Pipeline

from praisonaiagents import Agent, AgentFlow

researcher = Agent(instructions="Research topics")
writer = Agent(instructions="Write content")

flow = AgentFlow(steps=[researcher, writer])
flow.start("Research AI trends")

TypeScript

Install
bash
```
npm install praisonai
```

Set API Key

bash

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

Create Agent

Single Agent

import { Agent } from 'praisonai';

const agent = new Agent({ instructions: 'You are a helpful assistant' });
agent.start('Write a haiku about AI');

Multi-Agent Team

import { Agent, AgentTeam } from 'praisonai';

const researcher = new Agent({ instructions: 'Research topics' });
const writer = new Agent({ instructions: 'Write content' });

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

No Code (YAML)

Install
bash
```
pip install praisonai
```

Create agents.yaml

yaml

agents:
  researcher:
    instructions: Research AI trends
  writer:
    instructions: Write content based on research

Run
bash
```
praisonai agents.yaml
```

Key Features

Autonomous, Self-Improving Agents

Understand natural language
Make decisions
Execute tasks
Learn and persist new skills

Flexible Architecture

Modular components
Extensible tools
Custom workflows

Advanced Capabilities

Multi-agent collaboration
Memory management
Tool integration

Why PraisonAI?

Developer First

Modern SDK designed to be intuitive and powerful

Production Ready

Enterprise-grade with built-in security and scale

Open Source

Available on GitHub

Low Code Friendly

Easy for non-technical users

How It Works

An Agent receives your instructions, calls the LLM, and returns a result — teams and flows chain multiple agents on top of that.

Diagram

Best Practices

Pick the right building block

Use Agent for one task, AgentTeam when agents collaborate, and AgentFlow for step-based pipelines with routing, parallel, or loop patterns.

Keep the first example tiny

Start with Agent(instructions="…").start("…"). Add name, role, tools, and Task only as your needs grow.

Set provider keys in the environment

Export OPENAI_API_KEY (or another provider’s key) in your shell. PraisonAI auto-selects a matching default model.

Move to AgentOS for production

Wrap agents in AgentOS when you need an API, webhooks, or a scheduler for production deployment.

Quick Start

Build your first AI agent in minutes.

Workflows

Learn workflow patterns for multi-step pipelines.

Introduction

What is PraisonAI?

Core Components

When to Use What

Use Cases

Getting Started

Python

TypeScript

No Code (YAML)

Key Features

Why PraisonAI?

How It Works

Best Practices

Related