PraisonAI TypeScript Node.js AI Agents Framework

PraisonAI is a production-ready Multi AI Agents framework, 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.

Installation

``bash
npm install praisonai
`

Development Setup

1. Clone the repository:
`bash
git clone https://github.com/MervinPraison/PraisonAI.git
cd src/praisonai-ts
`

2. Install dependencies:
`bash
npm install
`

3. Build the package:
`bash
npm run build
`

Usage

Here are examples of different ways to use PraisonAI:

$3

`typescript
import { Agent, AgentTeam } from 'praisonai';

async function main() {
// Create a simple agent (no task specified)
const agent = new Agent({
name: "BiologyExpert",
instructions: "Explain the process of photosynthesis in detail.",
verbose: true
});

// Run the agent with AgentTeam
const team = new AgentTeam({
agents: [agent],
tasks: ["Explain the process of photosynthesis in detail."],
verbose: true
});

try {
console.log('Starting single agent example...');
const results = await team.start();
console.log('\nFinal Results:', results);
} catch (error) {
console.error('Error:', error);
}
}

main();
`

$3

`typescript
import { Agent, AgentTeam } from 'praisonai';

async function main() {
// Create multiple agents with different roles
const researchAgent = new Agent({
name: "ResearchAgent",
instructions: "Research and provide detailed information about renewable energy sources.",
verbose: true
});

const summaryAgent = new Agent({
name: "SummaryAgent",
instructions: "Create a concise summary of the research findings about renewable energy sources. Use {previous_result} as input.",
verbose: true
});

const recommendationAgent = new Agent({
name: "RecommendationAgent",
instructions: "Based on the summary in {previous_result}, provide specific recommendations for implementing renewable energy solutions.",
verbose: true
});

// Run the agents in sequence with AgentTeam
const team = new AgentTeam({
agents: [researchAgent, summaryAgent, recommendationAgent],
tasks: [
"Research and analyze current renewable energy technologies and their implementation.",
"Summarize the key findings from the research.",
"Provide actionable recommendations based on the summary."
],
verbose: true
});

try {
console.log('Starting multi-agent example...');
const results = await team.start();
console.log('\nFinal Results:', results);
} catch (error) {
console.error('Error:', error);
}
}

main();
`

$3

`typescript
import { Agent, Task, AgentTeam } from 'praisonai';

async function main() {
// Create agents first
const dietAgent = new Agent({
name: "DietAgent",
role: "Nutrition Expert",
goal: "Create healthy and delicious recipes",
backstory: "You are a certified nutritionist with years of experience in creating balanced meal plans.",
verbose: true, // Enable streaming output
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.
});

const blogAgent = new Agent({
name: "BlogAgent",
role: "Food Blogger",
goal: "Write engaging blog posts about food and recipes",
backstory: "You are a successful food blogger known for your ability to make recipes sound delicious and approachable.",
verbose: true, // Enable streaming output
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
});

// Create tasks
const createRecipesTask = new Task({
name: "Create Recipes",
description: "Create 5 healthy and delicious recipes",
agent: dietAgent
});

const writeBlogTask = new Task({
name: "Write Blog",
description: "Write a blog post about the recipes",
agent: blogAgent,
dependencies: [createRecipesTask] // This task depends on the recipes being created first
});

// Run the tasks with AgentTeam
const team = new AgentTeam({
tasks: [createRecipesTask, writeBlogTask],
verbose: true
});

try {
console.log('Starting task-based example...');
const results = await team.start();
console.log('\nFinal Results:', results);
} catch (error) {
console.error('Error:', error);
}
}

main();
`

$3

1. First, set up your environment variables:
`bash
export OPENAI_API_KEY='your-api-key'
`

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

3. Run the example:
`bash
npx ts-node example.ts
`

For more examples, check out the examples/concepts/ directory in the repository.

Package Structure

`
src/
├── agent/ # Agent-related interfaces and implementations
├── agents/ # Multi-agent system management
├── knowledge/ # Knowledge base and management
├── llm/ # Language Model interfaces
├── memory/ # Memory management systems
├── process/ # Process management
├── task/ # Task management
└── tools/ # Various utility tools
├── arxivTools.ts
└── ... (other tools)
`

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see the LICENSE file for details

Testing

$3

`bash
export OPENAI_API_KEY='your-api-key'
npx ts-node tests/development/simple/single-agent.ts
npx ts-node tests/development/simple/multi-agent.ts
npx ts-node tests/development/simple/multi-agents-simple.js
`

Examples Testing

`bash
export OPENAI_API_KEY='your-api-key'
npx ts-node examples/simple/single-agent.ts
npx ts-node examples/simple/multi-agent.ts
`

$3

`bash
npm run test
``