FAQs
Frequently asked questions about CrewAI Enterprise
In the hierarchical process, a manager agent is automatically created and coordinates the workflow, delegating tasks and validating outcomes for streamlined and effective execution. The manager agent utilizes tools to facilitate task delegation and execution by agents under the manager’s guidance. The manager LLM is crucial for the hierarchical process and must be set up correctly for proper function.
The most up-to-date documentation for CrewAI is available on our official documentation website; https://docs.crewai.com/
Hierarchical Process:
Tasks are delegated and executed based on a structured chain of command.
A manager language model (manager_llm) must be specified for the manager agent.
Manager agent oversees task execution, planning, delegation, and validation.
Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities.
Sequential Process:
Tasks are executed one after another, ensuring tasks are completed in an orderly progression. Output of one task serves as context for the next. Task execution follows the predefined order in the task list.
Which Process is Better Suited for Complex Projects?
The hierarchical process is better suited for complex projects because it allows for:
- Dynamic task allocation and delegation: Manager agent can assign tasks based on agent capabilities, allowing for efficient resource utilization.
- Structured validation and oversight: Manager agent reviews task outputs and ensures task completion, increasing reliability and accuracy.
- Complex task management: Assigning tools at the agent level allows for precise control over tool availability, facilitating the execution of intricate tasks.
- Adaptive Learning: Crews become more efficient over time, adapting to new information and refining their approach to tasks.
- Enhanced Personalization: Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
- Improved Problem Solving: Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
Setting a maximum RPM limit for an agent prevents the agent from making too many requests to external services, which can help to avoid rate limits and improve performance.
It allows agents to request additional information or clarification when necessary. This feature is crucial in complex decision-making processes or when agents require more details to complete a task effectively.
To integrate human input into agent execution, set the human_input flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer.
This input can provide extra context, clarify ambiguities, or validate the agent’s output.
CrewAI provides a range of advanced customization options to tailor and enhance agent behavior and capabilities:
-
Language Model Customization: Agents can be customized with specific language models (
llm) and function-calling language models (function_calling_llm), offering advanced control over their processing and decision-making abilities. -
Performance and Debugging Settings: Adjust an agent’s performance and monitor its operations for efficient task execution.
-
Verbose Mode: Enables detailed logging of an agent’s actions, useful for debugging and optimization.
-
RPM Limit: Sets the maximum number of requests per minute (
max_rpm). -
Maximum Iterations for Task Execution: The
max_iterattribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. -
Delegation and Autonomy: Control an agent’s ability to delegate or ask questions, tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the
allow_delegationattribute is set to True, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements. -
Human Input in Agent Execution: Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
Human input is particularly useful in agent execution when:
- Agents require additional information or clarification: When agents encounter ambiguity or incomplete data, human input can provide the necessary context to complete the task effectively.
- Agents need to make complex or sensitive decisions: Human input can assist agents in ethical or nuanced decision-making, ensuring responsible and informed outcomes.
- Oversight and validation of agent output: Human input can help validate the results generated by agents, ensuring accuracy and preventing any misinterpretation or errors.
- Customizing agent behavior: Human input can provide feedback on agent responses, allowing users to refine the agent’s behavior and responses over time.
- Identifying and resolving errors or limitations: Human input can help identify and address any errors or limitations in the agent’s capabilities, enabling continuous improvement and optimization.
The different types of memory available in CrewAI are:
short-term memorylong-term memoryentity memorycontextual memory
Learn more about the different types of memory here:
You can create custom tools by subclassing the BaseTool class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from BaseTool, specifying the name, description, and the _run method for operational logic. The tool decorator allows you to create a Tool object directly with the required attributes and a functional logic.
Click here for more details:
To use Output Pydantic in a task, you need to define the expected output of the task as a Pydantic model. Here’s an example:
Define a Pydantic model
First, you need to define a Pydantic model. For instance, let’s create a simple model for a user:
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
Then, when creating a task, specify the expected output as this Pydantic model:
from crewai import Task, Crew, Agent
# Import the User model
from my_models import User
# Create a task with Output Pydantic
task = Task(
description="Create a user with the provided name and age",
expected_output=User, # This is the Pydantic model
agent=agent,
tools=[tool1, tool2]
)
In your agent, make sure to set the output_pydantic attribute to the Pydantic model you're using:
from crewai import Agent
# Import the User model
from my_models import User
# Create an agent with Output Pydantic
agent = Agent(
role='User Creator',
goal='Create users',
backstory='I am skilled in creating user accounts',
tools=[tool1, tool2],
output_pydantic=User
)
When executing the crew, the output of the task will be a User object:
from crewai import Crew
# Create a crew with the agent and task
crew = Crew(agents=[agent], tasks=[task])
# Kick off the crew
result = crew.kickoff()
# The output of the task will be a User object
print(result.tasks[0].output)
Here’s a tutorial on how to consistently get structured outputs from your agents:
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