Understanding Tasks

Tasks are units of work that agents execute. Each task has a clear goal, input requirements, and expected outputs.

Core Components

Task Definition

Clear description of work to be done and expected outputs

Agent Assignment

Matching tasks with capable agents

Tool Access

Resources available for task completion

Output Handling

Managing and formatting task results

Task Configuration

task = Task(
    description="Research AI trends",
    expected_output="Summary report",
    agent=research_agent
)

Understanding Tasks

Task Types

Basic Task

Simple, single-operation tasks with clear inputs and outputs

Decision Task

Tasks involving choices and conditional paths

decision_task = Task(
    type="decision",
    conditions={
        "success": ["next_task"],
        "failure": ["error_task"]
    }
)

Loop Task

Repetitive operations over collections

loop_task = Task(
    type="loop",
    items=data_list,
    operation="process_item"
)

Task Relationships

Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.

task_a = Task(name="research")
task_b = Task(
    name="analyze",
    context=[task_a]  # Uses task_a's output
)

Task Dependencies

Relationship	Description	Example
Sequential	Tasks run one after another	Research → Analysis → Report
Parallel	Independent tasks run simultaneously	Data Collection + Processing
Conditional	Tasks depend on previous results	Success → Next Task, Failure → Retry

Advanced Features

Output Handling

Multiple output formats
Structured data validation
File system integration

Task Control

Execution flow control
Error recovery
Progress tracking

Getting Started

Install Praison Labs

Install the core package:

Terminal

pip install praisonaiagents

Configure Environment

Terminal

export OPENAI_API_KEY=your_openai_key

Generate your OpenAI API key from OpenAI Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the Models for more information.

Create Agent

Create app.py:

from praisonaiagents import Agent, Task, Praison LabsAgents

# Create an agent
researcher = Agent(
    name="Researcher",
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="You are an expert at a technology research group",
    verbose=True,
    llm="gpt-4o"
)

# Define a task
task = Task(
    name="research_task",
    description="Analyze 2024's AI advancements",
    expected_output="A detailed report",
    agent=researcher
)

# Run the agents
agents = Praison LabsAgents(
    agents=[researcher],
    tasks=[task],
    verbose=False
)

result = agents.start()

Best Practices

Design tasks to be atomic and focused. Each task should have a single, clear responsibility.

Task Design Principles

Clear Task Descriptions

Write precise, unambiguous task descriptions that clearly state the task’s purpose and requirements.

task = Task(
    description=(
        "Analyze Q4 2024 sales data "
        "and generate growth metrics"
    ),
    expected_output="Sales growth report"
)

Success Criteria

Define measurable success criteria to ensure task completion can be properly evaluated.

task = Task(
    success_criteria={
        "format": "markdown",
        "metrics": [
            "growth_rate",
            "revenue_change"
        ],
        "min_confidence": 0.95
    }
)

Task Dependencies

Specify any dependencies or prerequisites for the task.

task = Task(
    description="Generate final report",
    dependencies=[
        data_collection_task,
        analysis_task
    ],
    wait_for_dependencies=True
)

Resource Management

Tool Selection

Choose appropriate tools that match your task’s requirements.

task = Task(
    tools=[
        DataAnalyzer(),
        ReportGenerator(),
        MetricsCalculator()
    ],
    tool_config={
        "timeout": 30,
        "retry": True
    }
)

Error Handling

Implement comprehensive error management strategies.

task = Task(
    error_strategy="retry",
    max_retries=3,
    fallback_handler=handler,
    error_callbacks=[
        log_error,
        notify_admin
    ]
)

Resource Limits

Set appropriate resource constraints for optimal performance.

task = Task(
    memory_limit="2GB",
    timeout=300,
    max_concurrent=5,
    cpu_priority="high",
    auto_scale=True
)

Performance Monitoring

Configure monitoring and logging for task execution.

task = Task(
    monitoring={
        "metrics": ["duration", "memory", "cpu"],
        "log_level": "INFO",
        "trace_enabled": True,
        "alert_threshold": 0.85
    }
)

Async Task Execution

Async Task Features

async_task = Task(
    description="Perform async operation",
    expected_output="Async result",
    agent=async_agent,
    async_execution=True,  # Enable async execution
    callback=async_callback  # Optional async callback
)

Enable async execution with async_execution=True
Perfect for I/O-heavy operations
Supports parallel task execution

async_task = Task(
    description="Perform async operation",
    expected_output="Async result",
    agent=async_agent,
    async_execution=True,  # Enable async execution
    callback=async_callback  # Optional async callback
)

Enable async execution with async_execution=True
Perfect for I/O-heavy operations
Supports parallel task execution

async def async_callback(output: TaskOutput):
    await asyncio.sleep(1)  # Simulate async operation
    print(f"Processed output: {output.raw}")

task = Task(
    description="Task with async callback",
    expected_output="Result",
    agent=agent,
    async_execution=True,
    callback=async_callback
)

Support for async callbacks
Process results asynchronously
Handle task completion events

# Create mixed task types
sync_task = Task(
    description="Synchronous operation",
    async_execution=False
)

async_task = Task(
    description="Asynchronous operation",
    async_execution=True
)

# Run with mixed execution
agents = Praison LabsAgents(
    agents=[sync_agent, async_agent],
    tasks=[sync_task, async_task]
)
result = await agents.astart()

Mix sync and async tasks
Flexible execution patterns
Optimal resource utilization

Pro Tip: Use async execution for tasks that involve I/O operations (like API calls or file operations) to improve overall performance. Keep CPU-intensive tasks synchronous.

Next Steps

Create Tasks

Learn how to create and configure tasks

Task API

Explore the complete Task API documentation

Assistant

Responses are generated using AI and may contain mistakes.

Getting Started

Core Concepts

Workflows

Features

Models

Tools

Other Features

Monitoring

Developers

Getting Started (No Code)

API Reference

Tasks

Understanding Tasks

Core Components

Task Definition

Agent Assignment

Tool Access

Output Handling

Task Configuration

Understanding Tasks

Task Types

Task Relationships

Task Dependencies

Advanced Features

Output Handling

Task Control

Getting Started

Best Practices

Task Design Principles

Resource Management

Async Task Execution

Async Task Features

Next Steps

Create Tasks

Task API

Getting Started

Core Concepts

Workflows

Features

Models

Tools

Other Features

Monitoring

Developers

Getting Started (No Code)

API Reference

​ Understanding Tasks

​ Core Components

Task Definition

Agent Assignment

Tool Access

Output Handling

​ Task Configuration

​ Understanding Tasks

​ Task Types

​ Task Relationships

​ Context Sharing

​ Task Dependencies

​ Advanced Features

Output Handling

Task Control

​ Getting Started

​ Best Practices

Task Design Principles

Resource Management

​ Async Task Execution

Async Task Features

​ Next Steps

Create Tasks

Task API

Understanding Tasks

Core Components

Task Configuration

Understanding Tasks

Task Types

Task Relationships

Context Sharing

Task Dependencies

Advanced Features

Getting Started

Best Practices

Async Task Execution

Next Steps