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docs/how-to/Create-Custom-Tools.md Normal file
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---
title: Creating your own Tools
description: Guide on how to create and use custom tools within the crewAI framework.
---
## Creating your own Tools
!!! example "Custom Tool Creation"
Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
To create your own crewAI tools you will need to install our extra tools package:
```bash
pip install 'crewai[tools]'
```
Once you do that there are two main ways for one to create a crewAI tool:
### Subclassing `BaseTool`
```python
from crewai_tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = "Clear description for what this tool is useful for, you agent will need this information to use it."
def _run(self, argument: str) -> str:
# Implementation goes here
return "Result from custom tool"
```
Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
### Utilizing the `tool` Decorator
For a simpler approach, create a `Tool` object directly with the required attributes and a functional logic.
```python
from crewai_tools import tool
@tool("Name of my tool")
def my_tool(question: str) -> str:
"""Clear description for what this tool is useful for, you agent will need this information to use it."""
# Function logic here
```
```python
import json
import requests
from crewai import Agent
from crewai.tools import tool
from unstructured.partition.html import partition_html
# Annotate the function with the tool decorator from crewAI
@tool("Integration with a given API")
def integtation_tool(argument: str) -> str:
"""Integration with a given API"""
# Code here
return resutls # string to be sent back to the agent
# Assign the scraping tool to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[integtation_tool]
)
```

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docs/how-to/Creating-a-Crew-and-kick-it-off.md
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@@ -7,12 +7,11 @@ description: A comprehensive guide to creating a dynamic CrewAI team for your pr
Embark on your CrewAI journey by setting up your environment and initiating your AI crew with enhanced features. This guide ensures a seamless start, incorporating the latest updates.
## Step 0: Installation
Install CrewAI and any necessary packages for your project. The `duckduckgo-search` package is highlighted here for enhanced search capabilities.
Install CrewAI and any necessary packages for your project.
```shell
pip install crewai
pip install crewai[tools]
pip install duckduckgo-search
pip install 'crewai[tools]'
```
## Step 1: Assemble Your Agents
@@ -20,24 +19,24 @@ Define your agents with distinct roles, backstories, and now, enhanced capabilit
```python
import os
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"
from crewai import Agent
from langchain_community.tools import DuckDuckGoSearchRun
search_tool = DuckDuckGoSearchRun()
# Topic for the crew run
topic = 'AI in healthcare'
from crewai_tools import SerperDevTool
search_tool = SerperDevTool()
# Creating a senior researcher agent with memory and verbose mode
researcher = Agent(
role='Senior Researcher',
goal=f'Uncover groundbreaking technologies in {topic}',
goal='Uncover groundbreaking technologies in {topic}',
verbose=True,
memory=True,
backstory="""Driven by curiosity, you're at the forefront of
innovation, eager to explore and share knowledge that could change
the world.""",
backstory=(
"Driven by curiosity, you're at the forefront of"
"innovation, eager to explore and share knowledge that could change"
"the world."
),
tools=[search_tool],
allow_delegation=True
)
@@ -45,12 +44,14 @@ researcher = Agent(
# Creating a writer agent with custom tools and delegation capability
writer = Agent(
role='Writer',
goal=f'Narrate compelling tech stories about {topic}',
goal='Narrate compelling tech stories about {topic}',
verbose=True,
memory=True,
backstory="""With a flair for simplifying complex topics, you craft
engaging narratives that captivate and educate, bringing new
discoveries to light in an accessible manner.""",
backstory=(
"With a flair for simplifying complex topics, you craft"
"engaging narratives that captivate and educate, bringing new"
"discoveries to light in an accessible manner."
),
tools=[search_tool],
allow_delegation=False
)
@@ -64,10 +65,12 @@ from crewai import Task
# Research task
research_task = Task(
description=f"""Identify the next big trend in {topic}.
Focus on identifying pros and cons and the overall narrative.
Your final report should clearly articulate the key points,
its market opportunities, and potential risks.""",
description=(
"Identify the next big trend in {topic}."
"Focus on identifying pros and cons and the overall narrative."
"Your final report should clearly articulate the key points"
"its market opportunities, and potential risks."
),
expected_output='A comprehensive 3 paragraphs long report on the latest AI trends.',
tools=[search_tool],
agent=researcher,
@@ -75,10 +78,12 @@ research_task = Task(
# Writing task with language model configuration
write_task = Task(
description=f"""Compose an insightful article on {topic}.
Focus on the latest trends and how it's impacting the industry.
This article should be easy to understand, engaging, and positive.""",
expected_output=f'A 4 paragraph article on {topic} advancements fromated as markdown.',
description=(
"Compose an insightful article on {topic}."
"Focus on the latest trends and how it's impacting the industry."
"This article should be easy to understand, engaging, and positive."
),
expected_output='A 4 paragraph article on {topic} advancements formatted as markdown.',
tools=[search_tool],
agent=writer,
async_execution=False,
@@ -101,11 +106,11 @@ crew = Crew(
```
## Step 4: Kick It Off
Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome.
Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome. You can also pass the inputs that will be interpolated into the agents and tasks.
```python
# Starting the task execution process with enhanced feedback
result = crew.kickoff()
result = crew.kickoff(inputs={'topic': 'AI in healthcare'})
print(result)
```

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docs/how-to/Customizing-Agents.md
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@@ -17,6 +17,7 @@ Beyond the basic attributes, CrewAI allows for deeper customization to enhance a
### 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.
By default crewAI agents are ReAct agents, but by setting the `function_calling_llm` you can turn them into a function calling agents.
### Enabling Memory for Agents
CrewAI supports memory for agents, enabling them to remember past interactions. This feature is critical for tasks requiring awareness of previous contexts or decisions.
@@ -25,11 +26,11 @@ CrewAI supports memory for agents, enabling them to remember past interactions.
Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
### Verbose Mode and RPM Limit
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization.
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`), controlling the agent's query frequency to external services.
### Maximum Iterations for Task Execution
The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions.
The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 15, providing a balance between thoroughness and efficiency. Once the agent approaches this number it will try it's best to give a good answer.
## Customizing Agents and Tools
Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. In this example we will use the crewAI tools package to create a tool for a research analyst agent.
@@ -42,31 +43,31 @@ pip install 'crewai[tools]'
```python
import os
from crewai import Agent
from crewai_tools import SeperDevTool
from crewai_tools import SerperDevTool
# Set API keys for tool initialization
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key"
# Initialize a search tool
search_tool = SeperDevTool()
search_tool = SerperDevTool()
# Initialize the agent with advanced options
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[serper_tool],
tools=[search_tool],
memory=True,
verbose=True,
max_rpm=10, # Optinal: Limit requests to 10 per minute, preventing API abuse
max_iter=5, # Optional: Limit task iterations to 5 before the agent tried to gives its best answer
max_rpm=10, # Optional: Limit requests to 10 per minute, preventing API abuse
max_iter=5, # Optional: Limit task iterations to 5 before the agent tries to give its best answer
allow_delegation=False
)
```
## Delegation and Autonomy
Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework.
Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the crewAI framework. By default, the `allow_delegation` attribute 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.
### Example: Disabling Delegation for an Agent
```python
@@ -79,4 +80,4 @@ agent = Agent(
```
## Conclusion
Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, and performance settings, equips a nuanced and capable AI team ready for complex challenges.
Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences, equips a nuanced and capable AI team ready for complex challenges.

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docs/how-to/Hierarchical.md
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---
title: Implementing the Hierarchical Process in CrewAI
description: Understanding and applying the hierarchical process within your CrewAI projects, with updates reflecting the latest coding practices.
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 managing tasks, mimicking traditional organizational hierarchies for efficient task delegation and execution. This ensures a systematic workflow that enhances project outcomes.
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
Tasks within this process are managed through a clear hierarchy, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes, ensuring a streamlined and effective execution process.
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 is automatically created by crewAI so you don't need to worry about it.
### Key Features
- **Task Delegation**: A manager agent is responsible for allocating tasks among crew members based on their roles and capabilities.
- **Result Validation**: The manager evaluates the outcomes to ensure they meet the required standards before moving forward.
- **Efficient Workflow**: Emulates corporate structures, offering an organized and familiar approach to task management.
- **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.
## Implementing the Hierarchical Process
To adopt the hierarchical process, define a crew with a designated manager and establish a clear chain of command for task execution. This structure is crucial for maintaining an orderly and efficient workflow.
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"
Tools should be assigned at the agent level, not the task level, to facilitate task delegation and execution by the designated agents under the manager's guidance.
Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance.
!!! note "Manager LLM Configuration"
A manager LLM is automatically assigned to the crew, eliminating the need for manual definition. However, configuring the `manager_llm` parameter is necessary to tailor the manager's decision-making process.
!!! 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 without specifying a manager explicitly
# Agents are defined with an optional tools parameter
researcher = Agent(
role='Researcher',
goal='Conduct in-depth analysis',
# tools = [...]
role='Researcher',
goal='Conduct in-depth analysis',
# tools=[] # This can be optionally specified; defaults to an empty list
)
writer = Agent(
role='Writer',
goal='Create engaging content',
# tools = [...]
role='Writer',
goal='Create engaging content',
# tools=[] # Optionally specify tools; defaults to an empty list
)
# Establishing the crew with a hierarchical process
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"), # Defines the manager's decision-making engine
process=Process.hierarchical # Specifies the hierarchical management approach
tasks=[...], # Tasks to be delegated and executed under the manager's supervision
agents=[researcher, writer],
manager_llm=ChatOpenAI(temperature=0, model="gpt-4"), # Mandatory for hierarchical process
process=Process.hierarchical # Specifies the hierarchical management approach
)
```
### Workflow in Action
1. **Task Assignment**: The manager strategically assigns tasks, considering each agent's role and skills.
2. **Execution and Review**: Agents complete their tasks, followed by a thorough review by the manager to ensure quality standards.
3. **Sequential Task Progression**: The manager ensures tasks are completed in a logical order, facilitating smooth project progression.
1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities.
2. **Execution and Review**: Agents complete their tasks, with the manager ensuring quality standards.
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 facilitates a well-organized and efficient approach to project management. By structuring tasks and delegations within a clear hierarchy, it enhances both productivity and quality control, making it an ideal strategy for managing complex projects.
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.

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docs/how-to/Human-Input-on-Execution.md
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@@ -1,6 +1,7 @@
```markdown
# Human Input in Agent Execution
Human input is crucial in numerous agent execution scenarios, enabling agents to request additional information or clarification when necessary. This feature is particularly useful in complex decision-making processes or when agents require further details to complete a task effectively.
Human input plays a pivotal role in several agent execution scenarios, enabling agents to seek additional information or clarification when necessary. This capability is invaluable in complex decision-making processes or when agents need more details to complete a task effectively.
## Using Human Input with CrewAI
@@ -8,25 +9,35 @@ Incorporating human input with CrewAI is straightforward, enhancing the agent's
### Example:
```shell
pip install crewai
pip install 'crewai[tools]'
```
```python
import os
from crewai import Agent, Task, Crew, Process
from langchain_community.tools import DuckDuckGoSearchRun
from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool
from langchain.agents import load_tools
search_tool = DuckDuckGoSearchRun()
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"
# Loading Human Tools
human_tools = load_tools(["human"])
search_tool = SerperDevTool()
# Define your agents with roles and goals
# Define your agents with roles, goals, and tools
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You are a Senior Research Analyst at a leading tech think tank.
Your expertise lies in identifying emerging trends and technologies in AI and
data science. You have a knack for dissecting complex data and presenting
actionable insights.""",
backstory=(
"You are a Senior Research Analyst at a leading tech think tank."
"Your expertise lies in identifying emerging trends and technologies in AI and data science."
"You have a knack for dissecting complex data and presenting actionable insights."
),
verbose=True,
allow_delegation=False,
tools=[search_tool]+human_tools # Passing human tools to the agent
@@ -34,31 +45,33 @@ researcher = Agent(
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Tech Content Strategist, known for your insightful
and engaging articles on technology and innovation. With a deep understanding of
the tech industry, you transform complex concepts into compelling narratives.""",
backstory=(
"You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation."
"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
),
verbose=True,
allow_delegation=True
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.
Compile your findings in a detailed report.
Make sure to check with a human if the draft is good before finalizing your answer.""",
description=(
"Conduct a comprehensive analysis of the latest advancements in AI in 2024."
"Identify key trends, breakthrough technologies, and potential industry impacts."
"Compile your findings in a detailed report."
"Make sure to check with a human if the draft is good before finalizing your answer."
),
expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
agent=researcher
agent=researcher,
)
task2 = Task(
description="""Using the insights from the researcher's report, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Aim for a narrative that captures the essence of these breakthroughs and their
implications for the future.
Your final answer MUST be the full blog post of at least 3 paragraphs.""",
expected_output='A compelling 3 paragraphs blog post formated as markdown about the latest AI advancements in 2024',
description=(
"Using the insights from the researcher's report, develop an engaging blog post that highlights the most significant AI advancements."
"Your post should be informative yet accessible, catering to a tech-savvy audience."
"Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
),
expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
agent=writer
)

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docs/how-to/LLM-Connections.md
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@@ -1,6 +1,6 @@
---
title: Connect CrewAI to LLMs
description: Guide on integrating CrewAI with various Large Language Models (LLMs).
description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes and methods.
---
## Connect CrewAI to LLMs
@@ -10,15 +10,22 @@ description: Guide on integrating CrewAI with various Large Language Models (LLM
CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
## CrewAI Agent Overview
The `Agent` class in CrewAI is central to implementing AI solutions. Here's a brief overview:
The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's an updated overview reflecting the latest codebase changes:
- **Attributes**:
- `role`: Defines the agent's role within the solution.
- `goal`: Specifies the agent's objective.
- `backstory`: Provides a background story to the agent.
- `llm`: Indicates the Large Language Model the agent uses.
- `function_calling_llm` *Optinal*: Will turn the ReAct crewAI agent into a function calling agent.
- `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
- `memory`: Enables the agent to retain information during the execution.
- `max_rpm`: Sets the maximum number of requests per minute.
- `verbose`: Enables detailed logging of the agent's execution.
- `allow_delegation`: Allows the agent to delegate tasks to other agents, default is `True`.
- `tools`: Specifies the tools available to the agent for task execution.
- `step_callback`: Provides a callback function to be executed after each step.
### Example Changing OpenAI's GPT model
```python
# Required
os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"