Node.js AI Agents Framework

PraisonAI is a production-ready Multi AI Agents framework for Node.js, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.

Node.js

Quick Start

Requires Node.js 18+ (the bundled openai dependency needs Node 18).

Simple Usage

npm install praisonai@latest

yarn add praisonai@latest

As of v1.7.4, praisonai requires Node.js 18 or later (openai@4 needs Node 18+). Importing the package no longer requires OPENAI_API_KEY — the key is only checked when an OpenAI client is created, so Anthropic and Google users can require('praisonai') without one.

With Configuration

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

Requires Node ≥ 18 (engines.node >= 18). As of praisonai-ts 1.7.4, require('praisonai') no longer throws when OPENAI_API_KEY is unset — the key is only needed at client creation, so the 5-line example below runs end-to-end.

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, Agents } = require('praisonai');

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

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

Run Script

node app.js

Usage Examples

Single Agent Example

Create and run a single agent to perform a specific task:

import { Agent } from 'praisonai';

// Single agent example - Science Explainer
const agent = new Agent({ 
  instructions: `You are a science expert who explains complex phenomena in simple terms.
Provide clear, accurate, and easy-to-understand explanations.`,
  name: "ScienceExplainer",
  verbose: true
});

agent.start("Why is the sky blue?")
  .then(response => {
    console.log('\nExplanation:');
    console.log(response);
  })
  .catch(error => {
    console.error('Error:', error);
  });

Multi-Agent Example

Create and run multiple agents working together:

import { Agent, AgentTeam } from 'praisonai';

// Create story agent
const storyAgent = new Agent({
  instructions: "You are a storyteller. Write a very short story (2-3 sentences) about a given topic.",
  name: "StoryAgent",
  verbose: true
});

// Create summary agent
const summaryAgent = new Agent({
  instructions: "You are an editor. Create a one-sentence summary of the given story.",
  name: "SummaryAgent",
  verbose: true
});

// Create and start agents
const agents = new AgentTeam({
  agents: [storyAgent, summaryAgent],
  tasks: [
    "Write a short story about a cat",
    "{previous_result}"  // This will be replaced with the story
  ],
  verbose: true
});

agents.start()
  .then(results => {
    console.log('\nStory:', results[0]);
    console.log('\nSummary:', results[1]);
  })
  .catch(error => console.error('Error:', error));

Task-Based Agent Example

Create agents with specific tasks and dependencies:

import { Agent, AgentTeam } from 'praisonai';

// Create recipe agent
const recipeAgent = new Agent({
  instructions: `You are a professional chef and nutritionist. Create 5 healthy food recipes that are both nutritious and delicious.
Each recipe should include:
1. Recipe name
2. List of ingredients with quantities
3. Step-by-step cooking instructions
4. Nutritional information
5. Health benefits

Format your response in markdown.`,
  name: "RecipeAgent",
  verbose: true
});

// Create blog agent
const blogAgent = new Agent({
  instructions: `You are a food and health blogger. Write an engaging blog post about the provided recipes.
The blog post should:
1. Have an engaging title
2. Include an introduction about healthy eating
3. Discuss each recipe and its unique health benefits
4. Include tips for meal planning and preparation
5. End with a conclusion encouraging healthy eating habits

Here are the recipes to write about:
{previous_result}

Format your response in markdown.`,
  name: "BlogAgent",
  verbose: true
});

// Create Agents instance with tasks
const agents = new AgentTeam({
  agents: [recipeAgent, blogAgent],
  tasks: [
    "Create 5 healthy and delicious recipes",
    "Write a blog post about the recipes"
  ],
  verbose: true
});

// Start the agents
agents.start()
  .then(results => {
    console.log('\nFinal Results:');
    console.log('\nRecipe Task Results:');
    console.log(results[0]);
    console.log('\nBlog Task Results:');
    console.log(results[1]);
  })
  .catch(error => {
    console.error('Error:', error);
  });

Running the Examples

Set Environment Variables

export OPENAI_API_KEY='your-api-key'

Create Example File

Create a new TypeScript file (e.g., app.ts) with any of the above examples.

Run the Example

npx ts-node app.ts

TypeScript SDK

TypeScript agent framework

Agent

Agent configuration

​Quick Start

​Code Example

​Usage Examples

​Running the Examples

​Related

TypeScript SDK

Agent

Quick Start

Code Example

Usage Examples

Running the Examples

Related