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

# Agent Tasks

> Understanding how agents handle and manage tasks

Tasks define what agents should do. In PraisonAI, you can give tasks directly to agents or use the Task class for complex workflows.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Task Flow"
        Define[📋 Define Task] --> Assign[🤖 Assign Agent]
        Assign --> Execute[⚡ Execute]
        Execute --> Result[✅ Result]
    end
    
    classDef define fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef agent fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef result fill:#2E8B57,stroke:#7C90A0,color:#fff
    
    class Define define
    class Assign,Execute agent
    class Result result
```

***

## Quick Start

### Simple Way - Direct Task

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

agent = Agent(instructions="You are a research assistant")
agent.start("Research the latest AI trends")  # This is the task
```

### Advanced Way - Task Class

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam

researcher = Agent(instructions="Research topics")
writer = Agent(instructions="Write summaries")

task1 = Task(description="Research AI trends", agent=researcher)
task2 = Task(description="Write a summary", agent=writer)

team = AgentTeam(agents=[researcher, writer], tasks=[task1, task2])
team.start()
```

***

## Task Options

| Option            | Description           | Example                |
| ----------------- | --------------------- | ---------------------- |
| `description`     | What to do            | `"Research AI trends"` |
| `agent`           | Who does it           | `researcher`           |
| `expected_output` | What to produce       | `"Summary report"`     |
| `context`         | Previous tasks to use | `[task1]`              |
| `output_file`     | Save result to file   | `"report.md"`          |

***

## Task Types

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
    subgraph "Task Types"
        Research[🔍 Research<br/>Find information]
        Create[✍️ Create<br/>Generate content]
        Analyze[📊 Analyze<br/>Process data]
        Interact[💬 Interact<br/>Chat with users]
    end
    
    classDef research fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef create fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef analyze fill:#2E8B57,stroke:#7C90A0,color:#fff
    classDef interact fill:#F59E0B,stroke:#7C90A0,color:#fff
    
    class Research research
    class Create create
    class Analyze analyze
    class Interact interact
```

<CardGroup cols={2}>
  <Card title="Research" icon="magnifying-glass">
    Find, gather, and summarize information
  </Card>

  <Card title="Create" icon="pen">
    Write articles, reports, or content
  </Card>

  <Card title="Analyze" icon="chart-line">
    Process data and find insights
  </Card>

  <Card title="Interact" icon="comments">
    Answer questions and chat
  </Card>
</CardGroup>

***

## Task Dependencies

Tasks can use results from previous tasks:

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Task Chain"
        T1[📋 Task 1<br/>Research] --> T2[📋 Task 2<br/>Analyze]
        T2 --> T3[📋 Task 3<br/>Write]
    end
    
    classDef task fill:#189AB4,stroke:#7C90A0,color:#fff
    class T1,T2,T3 task
```

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam

researcher = Agent(instructions="Research topics")
analyst = Agent(instructions="Analyze data")
writer = Agent(instructions="Write reports")

# Task 1: Research
task1 = Task(
    description="Research renewable energy trends",
    agent=researcher
)

# Task 2: Uses Task 1's output
task2 = Task(
    description="Analyze the research findings",
    agent=analyst,
    context=[task1]  # Gets task1's result
)

# Task 3: Uses Task 2's output
task3 = Task(
    description="Write a summary report",
    agent=writer,
    context=[task2],
    output_file="report.md"  # Save to file
)

team = AgentTeam(agents=[researcher, analyst, writer], tasks=[task1, task2, task3])
team.start()
```

***

## Complete Example

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam

# Create specialized agents
researcher = Agent(
    name="Researcher",
    instructions="Find accurate information on topics",
    web=True
)

writer = Agent(
    name="Writer",
    instructions="Write clear, engaging content"
)

# Define tasks
research_task = Task(
    description="Research the benefits of solar energy",
    expected_output="Key facts and statistics",
    agent=researcher
)

writing_task = Task(
    description="Write a blog post about solar energy benefits",
    expected_output="500-word blog post",
    agent=writer,
    context=[research_task],
    output_file="solar-blog.md"
)

# Run the workflow
team = AgentTeam(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process="sequential"
)

result = team.start()
```

***

## Best Practices

<AccordionGroup>
  <Accordion title="Be Specific">
    "Research AI trends in healthcare" is better than "Research AI"
  </Accordion>

  <Accordion title="One Goal Per Task">
    Split complex work into multiple focused tasks
  </Accordion>

  <Accordion title="Use Context">
    Connect related tasks with `context=[previous_task]`
  </Accordion>

  <Accordion title="Define Expected Output">
    Tell the agent what format you want
  </Accordion>
</AccordionGroup>

***

<Card title="Next: Creating Your First Agent" icon="arrow-right" href="/course/agents/11-creating-your-first-agent">
  Build a complete agent application step by step.
</Card>