Conditional Tasks

Learn how to use conditional tasks in a crewAI kickoff

Introduction

Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks. This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.

Example Usage

Code

from typing import List
from pydantic import BaseModel
from crewai import Agent, Crew
from crewai.tasks.conditional_task import ConditionalTask
from crewai.tasks.task_output import TaskOutput
from crewai.task import Task
from crewai_tools import SerperDevTool

# Define a condition function for the conditional task
# If false, the task will be skipped, if true, then execute the task.
def is_data_missing(output: TaskOutput) -> bool:
    return len(output.pydantic.events) < 10  # this will skip this task

# Define the agents
data_fetcher_agent = Agent(
    role="Data Fetcher",
    goal="Fetch data online using Serper tool",
    backstory="Backstory 1",
    verbose=True,
    tools=[SerperDevTool()]
)

data_processor_agent = Agent(
    role="Data Processor",
    goal="Process fetched data",
    backstory="Backstory 2",
    verbose=True
)

summary_generator_agent = Agent(
    role="Summary Generator",
    goal="Generate summary from fetched data",
    backstory="Backstory 3",
    verbose=True
)

class EventOutput(BaseModel):
    events: List[str]

task1 = Task(
    description="Fetch data about events in San Francisco using Serper tool",
    expected_output="List of 10 things to do in SF this week",
    agent=data_fetcher_agent,
    output_pydantic=EventOutput,
)

conditional_task = ConditionalTask(
    description="""
        Check if data is missing. If we have less than 10 events,
        fetch more events using Serper tool so that
        we have a total of 10 events in SF this week..
        """,
    expected_output="List of 10 Things to do in SF this week",
    condition=is_data_missing,
    agent=data_processor_agent,
)

task3 = Task(
    description="Generate summary of events in San Francisco from fetched data",
    expected_output="A complete report on the customer and their customers and competitors, including their demographics, preferences, market positioning and audience engagement.",
    agent=summary_generator_agent,
)

# Create a crew with the tasks
crew = Crew(
    agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
    tasks=[task1, conditional_task, task3],
    verbose=True,
    planning=True
)

# Run the crew
result = crew.kickoff()
print("results", result)