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.

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Quick Start

Simple Way - Direct Task

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

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()

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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"

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Task Types

Research

Find, gather, and summarize information

Create

Write articles, reports, or content

Analyze

Process data and find insights

Interact

Answer questions and chat

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Task Dependencies

Tasks can use results from previous tasks:

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()

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Complete Example

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()

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Best Practices

Be Specific

“Research AI trends in healthcare” is better than “Research AI”

One Goal Per Task

Split complex work into multiple focused tasks

Use Context

Connect related tasks with context=[previous_task]

Define Expected Output

Tell the agent what format you want

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Next: Creating Your First Agent

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