# 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-js.git
cd praisonai-js
```
2. Install dependencies:
```bash
npm install
```
3. Build the package:
```bash
npm run build
```
## Usage
Here are examples of different ways to use PraisonAI:
### 1. Single Agent Example
```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();
```
### 2. Multi-Agent Example
```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. Task-Based Agent Example
```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();
```
### Running the Examples
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
### Manual Testing
```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
```
### Automated Testing (WIP)
```bash
npm run test
```
## Reporting issues
| Topic | Where to file | Where fixes land |
|-------|---------------|------------------|
| TypeScript / npm (`praisonai`) | [PraisonAI](https://github.com/MervinPraison/PraisonAI/issues) | `src/praisonai-ts/` in the monorepo |
| Python SDK / CLI | [PraisonAI](https://github.com/MervinPraison/PraisonAI/issues) | monorepo Python paths |
| Cross-language parity | [PraisonAI](https://github.com/MervinPraison/PraisonAI/issues) | often both |
This repo is the **npm mirror**. Sync: **monorepo `src/praisonai-ts/` → praisonai-js** ([SYNC.md](SYNC.md)).