Tasks

Overview of a Task¶

What is a Task?

In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.

Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.

Task Attributes¶

Attribute	Parameters	Description
Description	`description`	A clear, concise statement of what the task entails.
Agent	`agent`	The agent responsible for the task, assigned either directly or by the crew's process.
Expected Output	`expected_output`	A detailed description of what the task's completion looks like.
Tools (optional)	`tools`	The functions or capabilities the agent can utilize to perform the task.
Async Execution (optional)	`async_execution`	If set, the task executes asynchronously, allowing progression without waiting for completion.
Context (optional)	`context`	Specifies tasks whose outputs are used as context for this task.
Config (optional)	`config`	Additional configuration details for the agent executing the task, allowing further customization.
Output JSON (optional)	`output_json`	Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.
Output Pydantic (optional)	`output_pydantic`	Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.
Output File (optional)	`output_file`	Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved.
Output (optional)	`output`	The output of the task, containing the raw, JSON, and Pydantic output plus additional details.
Callback (optional)	`callback`	A Python callable that is executed with the task's output upon completion.
Human Input (optional)	`human_input`	Indicates if the task requires human feedback at the end, useful for tasks needing human oversight.

Creating a Task¶

Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:

from crewai import Task

task = Task(
    description='Find and summarize the latest and most relevant news on AI',
    agent=sales_agent,
    expected_output='A bullet list summary of the top 5 most important AI news',
)

Task Assignment

Directly specify an agent for assignment or let the hierarchical CrewAI's process decide based on roles, availability, etc.

Task Output¶

Understanding Task Outputs

The output of a task in the crewAI framework is encapsulated within the TaskOutput class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models. By default, the TaskOutput will only include the raw output. A TaskOutput will only include the pydantic or json_dict output if the original Task object was configured with output_pydantic or output_json, respectively.

Task Output Attributes¶

Attribute	Parameters	Type	Description
Description	`description`	`str`	A brief description of the task.
Summary	`summary`	`Optional[str]`	A short summary of the task, auto-generated from the description.
Raw	`raw`	`str`	The raw output of the task. This is the default format for the output.
Pydantic	`pydantic`	`Optional[BaseModel]`	A Pydantic model object representing the structured output of the task.
JSON Dict	`json_dict`	`Optional[Dict[str, Any]]`	A dictionary representing the JSON output of the task.
Agent	`agent`	`str`	The agent that executed the task.
Output Format	`output_format`	`OutputFormat`	The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW.

Task Output Methods and Properties¶

Method/Property	Description
json	Returns the JSON string representation of the task output if the output format is JSON.
to_dict	Converts the JSON and Pydantic outputs to a dictionary.
**str**	Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw.

Accessing Task Outputs¶

Once a task has been executed, its output can be accessed through the output attribute of the Task object. The TaskOutput class provides various ways to interact with and present this output.

Example¶

# Example task
task = Task(
    description='Find and summarize the latest AI news',
    expected_output='A bullet list summary of the top 5 most important AI news',
    agent=research_agent,
    tools=[search_tool]
)

# Execute the crew
crew = Crew(
    agents=[research_agent],
    tasks=[task],
    verbose=2
)

result = crew.kickoff()

# Accessing the task output
task_output = task.output

print(f"Task Description: {task_output.description}")
print(f"Task Summary: {task_output.summary}")
print(f"Raw Output: {task_output.raw}")
if task_output.json_dict:
    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
if task_output.pydantic:
    print(f"Pydantic Output: {task_output.pydantic}")

Integrating Tools with Tasks¶

Leverage tools from the crewAI Toolkit and LangChain Tools for enhanced task performance and agent interaction.

Creating a Task with Tools¶

import os
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key

from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool

research_agent = Agent(
  role='Researcher',
  goal='Find and summarize the latest AI news',
  backstory="""You're a researcher at a large company.
  You're responsible for analyzing data and providing insights
  to the business.""",
  verbose=True
)

search_tool = SerperDevTool()

task = Task(
  description='Find and summarize the latest AI news',
  expected_output='A bullet list summary of the top 5 most important AI news',
  agent=research_agent,
  tools=[search_tool]
)

crew = Crew(
    agents=[research_agent],
    tasks=[task],
    verbose=2
)

result = crew.kickoff()
print(result)

This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.

Referring to Other Tasks¶

In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.

This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the context attribute of the task:

# ...

research_ai_task = Task(
    description='Find and summarize the latest AI news',
    expected_output='A bullet list summary of the top 5 most important AI news',
    async_execution=True,
    agent=research_agent,
    tools=[search_tool]
)

research_ops_task = Task(
    description='Find and summarize the latest AI Ops news',
    expected_output='A bullet list summary of the top 5 most important AI Ops news',
    async_execution=True,
    agent=research_agent,
    tools=[search_tool]
)

write_blog_task = Task(
    description="Write a full blog post about the importance of AI and its latest news",
    expected_output='Full blog post that is 4 paragraphs long',
    agent=writer_agent,
    context=[research_ai_task, research_ops_task]
)

#...

Asynchronous Execution¶

You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.

You can then use the context attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.

#...

list_ideas = Task(
    description="List of 5 interesting ideas to explore for an article about AI.",
    expected_output="Bullet point list of 5 ideas for an article.",
    agent=researcher,
    async_execution=True # Will be executed asynchronously
)

list_important_history = Task(
    description="Research the history of AI and give me the 5 most important events.",
    expected_output="Bullet point list of 5 important events.",
    agent=researcher,
    async_execution=True # Will be executed asynchronously
)

write_article = Task(
    description="Write an article about AI, its history, and interesting ideas.",
    expected_output="A 4 paragraph article about AI.",
    agent=writer,
    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
)

#...

Callback Mechanism¶

The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.

# ...

def callback_function(output: TaskOutput):
    # Do something after the task is completed
    # Example: Send an email to the manager
    print(f"""
        Task completed!
        Task: {output.description}
        Output: {output.raw_output}
    """)

research_task = Task(
    description='Find and summarize the latest AI news',
    expected_output='A bullet list summary of the top 5 most important AI news',
    agent=research_agent,
    tools=[search_tool],
    callback=callback_function
)

#...

Accessing a Specific Task Output¶

Once a crew finishes running, you can access the output of a specific task by using the output attribute of the task object:

# ...
task1 = Task(
    description='Find and summarize the latest AI news',
    expected_output='A bullet list summary of the top 5 most important AI news',
    agent=research_agent,
    tools=[search_tool]
)

#...

crew = Crew(
    agents=[research_agent],
    tasks=[task1, task2, task3],
    verbose=2
)

result = crew.kickoff()

# Returns a TaskOutput object with the description and results of the task
print(f"""
    Task completed!
    Task: {task1.output.description}
    Output: {task1.output.raw_output}
""")

Tool Override Mechanism¶

Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.

Error Handling and Validation Mechanisms¶

While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:

Ensuring only one output type is set per task to maintain clear output expectations.
Preventing the manual assignment of the id attribute to uphold the integrity of the unique identifier system.

These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.

Creating Directories when Saving Files¶

You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.

# ...

save_output_task = Task(
    description='Save the summarized AI news to a file',
    expected_output='File saved successfully',
    agent=research_agent,
    tools=[file_save_tool],
    output_file='outputs/ai_news_summary.txt',
    create_directory=True
)

#...

Conclusion¶

Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.