> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Tasks

> Define work for agents to complete

Tasks define specific work for agents to complete with clear objectives.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Task Execution"
        A[👤 User] --> B[📋 Task]
        B --> C[🤖 Agent]
        C --> D[✅ Result]
    end
    
    
    class A user
    class B task
    class C,D output

    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff

    class A user
    class B task
    class C agent
    class D output
    classDef agent fill:#8B0000,color:#fff
    classDef tool fill:#189AB4,color:#fff
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    ```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    import { Agent, Task } from 'praisonai';

    const agent = new Agent({
      instructions: 'You research and summarize topics'
    });

    const task = new Task({
      description: 'Research AI trends in 2024',
      agent: agent
    });

    const result = await task.execute();
    console.log(result.output);
    ```
  </Step>

  <Step title="With Configuration">
    ```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    const task = new Task({
      description: 'Analyze sales data',
      expectedOutput: 'A summary with top 3 insights',
      agent: analyst
    });
    ```
  </Step>
</Steps>

***

## User Interaction Flow

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Task
    participant Agent
    
    User->>Task: Define task
    Task->>Agent: Assign work
    Agent->>Agent: Execute
    Agent-->>Task: Complete
    Task-->>User: Result
```

***

## Configuration Levels

```typescript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
// Level 1: String - Simple description
const task = new Task({
  description: 'Write a blog post about AI',
  agent: writer
});

// Level 2: Dict - With options
const task = new Task({
  description: 'Write a blog post',
  expectedOutput: '500 words, engaging tone',
  agent: writer
});

// Level 3: Instance - Full control
const task = new Task({
  description: 'Write a blog post',
  expectedOutput: '500 words',
  agent: writer,
  context: [researchTask],  // Use output from other tasks
  callback: (result) => console.log('Done!')
});
```

***

## Task Options

| Option           | Description                     |
| ---------------- | ------------------------------- |
| `description`    | What the task should accomplish |
| `expectedOutput` | Format/structure of result      |
| `agent`          | Agent assigned to the task      |
| `context`        | Other tasks to use as input     |
| `async`          | Run without blocking            |

***

## API Reference

<Card title="TaskConfig" icon="code" href="/docs/sdk/reference/typescript/classes/TaskConfig">
  Task configuration options
</Card>

<Card title="Task" icon="robot" href="/docs/sdk/reference/typescript/classes/Task">
  Task class documentation
</Card>

***

## Best Practices

<AccordionGroup>
  <Accordion title="Be specific in descriptions">
    "Write 500-word blog post about AI benefits" beats "Write about AI".
  </Accordion>

  <Accordion title="Define expected output">
    Telling the agent what format you want improves results.
  </Accordion>

  <Accordion title="Use context for dependencies">
    Chain tasks by passing earlier outputs as context.
  </Accordion>
</AccordionGroup>

***

## Related

<CardGroup cols={2}>
  <Card title="Workflows" icon="diagram-project" href="/docs/js/workflows">
    Multi-step workflows
  </Card>

  <Card title="Teams" icon="users" href="/docs/js/teams">
    Multi-agent teams
  </Card>
</CardGroup>