TypeScript AI Agents Framework - Praison Labs Documentation

Praison Labs is a production-ready Multi AI Agents framework for TypeScript, 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.

Install Package

npm install praisonai

Set API Key

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

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")

Run Script

npx ts-node app.ts

Install Package

npm install praisonai

Set API Key

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx

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")

Run Script

npx ts-node app.ts

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, Praison LabsAgents } 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 Praison LabsAgents({
  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, Praison LabsAgents } 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 Praison LabsAgents instance with tasks
const agents = new Praison LabsAgents({
  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

Tool Calls Examples

Direct Function Tools

Create an agent with directly registered function tools:

import { Agent } from 'praisonai';

async function getWeather(location: string) {
  console.log(`Getting weather for ${location}...`);
  return `${Math.floor(Math.random() * 30)}°C`;
}

async function getTime(location: string) {
  console.log(`Getting time for ${location}...`);
  const now = new Date();
  return `${now.getHours()}:${now.getMinutes()}`;
}

const agent = new Agent({ 
  instructions: `You provide the current weather and time for requested locations.`,
  name: "DirectFunctionAgent",
  tools: [getWeather, getTime]
});

agent.start("What's the weather and time in Paris, France and Tokyo, Japan?");

Assistant

Responses are generated using AI and may contain mistakes.

JavaScript

​ Code Example

​ Code Example

​ Usage Examples

​ Running the Examples

​ Tool Calls Examples

Code Example

Code Example

Usage Examples

Running the Examples

Tool Calls Examples