Node.js AI Agents Framework

A production-ready Multi AI Agents framework for Node.js

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).

1. Simple Usage

   npm

   npm install 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.

   v-next (from PR #4416): praisonai-ts no longer runs dotenv.config() on import, and no longer reads process.env.LOGLEVEL at import. The package is safe to import in any JavaScript runtime (browser, Electron renderer, webview, React Native). It also no longer pulls dotenv or node-fetch into your bundle, so modern bundlers can tree-shake unused code paths. If you relied on the automatic .env load, install dotenv yourself and call dotenv.config() at the top of your entrypoint — see JS Import Safety & Runtimes.

   praisonai ships both ESM and CommonJS builds, so both import and require work. For bundlers, React Native, and edge runtimes see Packaging & Bundlers.

2. 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.

3. Create File

   Create app.js file

   Code Example

   ESM

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

   CommonJS

   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');
   

   Single Agent

   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');
   
4. Run Script

   node app.js
   

Source & contributing: the TypeScript/JavaScript SDK is developed in MervinPraison/PraisonAI at src/praisonai-ts/. The praisonai-js repo is only the npm mirror — file issues and PRs against the main monorepo. See Contributing.

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

1. Set Environment Variables

   export OPENAI_API_KEY='your-api-key'
   
2. Create Example File

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

3. Run the Example

   npx ts-node app.ts
   

Related

TypeScript SDK

TypeScript agent framework

Agent

Agent configuration