Introduction

PraisonAI runs AI agents on your own computers, even fully offline, so private data never has to leave the building. It is built for highly regulated teams, and free and open source for everyone.

PraisonAI runs AI agents on your own computers, even fully offline, so private data never has to leave the building. It is built for highly regulated teams, and free and open source for everyone.

Private AI that earns trust one task at a time.

from praisonaiagents import Agent

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

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What is PraisonAI?

PraisonAI is an open-source Python framework for building AI agents. It is still a general-purpose framework: build any kind of agent, with any model, in the cloud or on your own computers. Our company focus is private AI for regulated teams, and this framework is the foundation for that work.

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Building something else?

Everything works as before. Nothing has been removed, and every feature page is where it was.

Agents

One agent with instructions and tools

Multi-agent teams

Several agents working together

AgentFlow

Step-based pipelines: route, parallel, loop

Memory

Agents that remember across sessions

Knowledge

Give agents your documents

RAG

Retrieve before you answer

Tools

Let agents call your functions

MCP

Connect Model Context Protocol servers

Messaging bots

Telegram, Discord, Slack, WhatsApp

CLI

Run agents from the command line

Training

Train agents and fine-tune models

Browser automation

Agents that use a web browser

Sandbox

Run agent code in isolation

Deploy

Docker, cloud and local servers

UI

Chat interfaces for your agents

YAML

Define agents without Python

JavaScript SDK

The same agents in TypeScript and JavaScript

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Run fully offline

Agents can run on a model on your own computer, with no internet connection and no cloud API key.

Run fully offline

Install, pull a local model, and run an agent with the network turned off.

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Early access: task handover

We are building a way to move each task to your own AI only after it passes a test, and move it back if quality slips. It is in early access, and we are taking on our first design partners.

Task handover (early access)

The five steps, and the parts of the open-source framework it builds on.

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Core Components

🤖 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

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When to Use What

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

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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.

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Getting Started

Python

1. Install

   pip install praisonaiagents
   
2. Set API Key

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
   
3. Create Agent

   Single Agent

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

TypeScript

1. Install

   npm install praisonai
   
2. Set API Key

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
   
3. 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');
   

No Code (YAML)

1. Install

   pip install praisonai
   
2. Create agents.yaml

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

   praisonai agents.yaml
   

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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

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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

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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.

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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.

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Related

Quick Start

Build your first AI agent in minutes.

Workflows

Learn workflow patterns for multi-step pipelines.

Join our community on Discord to connect with other developers and get help!