---
title: Implementing the Hierarchical Process in CrewAI
description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
---

## Introduction
The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution. This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.

!!! note "Complexity and Efficiency"
    The hierarchical process is designed to leverage advanced models like GPT-4, optimizing token usage while handling complex tasks with greater efficiency.

## Hierarchical Process Overview
By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent can now be either automatically created by CrewAI or explicitly set by the user.

### Key Features
- **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
- **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
- **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
- **System Prompt Handling**: Optionally specify whether the system should use predefined prompts.
- **Stop Words Control**: Optionally specify whether stop words should be used, supporting various models including the o1 models.
- **Context Window Respect**: Prioritize important context by enabling respect of the context window, which is now the default behavior.
- **Delegation Control**: Delegation is now disabled by default to give users explicit control.
- **Max Requests Per Minute**: Configurable option to set the maximum number of requests per minute.
- **Max Iterations**: Limit the maximum number of iterations for obtaining a final answer.


## Implementing the Hierarchical Process
To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.

!!! note "Tools and Agent Assignment"
    Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance. Tools can also be specified at the task level for precise control over tool availability during task execution.

!!! note "Manager LLM Requirement"
    Configuring the `manager_llm` parameter is crucial for the hierarchical process. The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.

```python
from langchain_openai import ChatOpenAI
from crewai import Crew, Process, Agent

# Agents are defined with attributes for backstory, cache, and verbose mode
researcher = Agent(
    role='Researcher',
    goal='Conduct in-depth analysis',
    backstory='Experienced data analyst with a knack for uncovering hidden trends.',
    cache=True,
    verbose=False,
    # tools=[]  # This can be optionally specified; defaults to an empty list
    use_system_prompt=True,  # Enable or disable system prompts for this agent
    max_rpm=30,  # Limit on the number of requests per minute
    max_iter=5  # Maximum number of iterations for a final answer
)
writer = Agent(
    role='Writer',
    goal='Create engaging content',
    backstory='Creative writer passionate about storytelling in technical domains.',
    cache=True,
    verbose=False,
    # tools=[]  # Optionally specify tools; defaults to an empty list
    use_system_prompt=True,  # Enable or disable system prompts for this agent
    max_rpm=30,  # Limit on the number of requests per minute
    max_iter=5  # Maximum number of iterations for a final answer
)

# Establishing the crew with a hierarchical process and additional configurations
project_crew = Crew(
    tasks=[...],  # Tasks to be delegated and executed under the manager's supervision
    agents=[researcher, writer],
    manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),  # Mandatory if manager_agent is not set
    process=Process.hierarchical,  # Specifies the hierarchical management approach
    respect_context_window=True,  # Enable respect of the context window for tasks
    memory=True,  # Enable memory usage for enhanced task execution
    manager_agent=None,  # Optional: explicitly set a specific agent as manager instead of the manager_llm
    planning=True,  # Enable planning feature for pre-execution strategy
)
```

### Workflow in Action
1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
2. **Execution and Review**: Agents complete their tasks with the option for asynchronous execution and callback functions for streamlined workflows.
3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.

## Conclusion
Adopting the hierarchical process in CrewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management. Utilize the advanced features and customizations to tailor the workflow to your specific needs, ensuring optimal task execution and project success.