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CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.

What is a Tool?


A tool in CrewAI is a skill or function that agents can utilize to perform various actions. This includes tools from the crewAI Toolkit and LangChain Tools, enabling everything from simple searches to complex interactions and effective teamwork among agents.

Key Characteristics of Tools

  • Utility: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
  • Integration: Boosts agent capabilities by seamlessly integrating tools into their workflow.
  • Customizability: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
  • Error Handling: Incorporates robust error handling mechanisms to ensure smooth operation.
  • Caching Mechanism: Features intelligent caching to optimize performance and reduce redundant operations.

Using crewAI Tools

To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:

pip install 'crewai[tools]'

Here's an example demonstrating their use:

import os
from crewai import Agent, Task, Crew
# Importing crewAI tools
from crewai_tools import (

# Set up API keys
os.environ["SERPER_API_KEY"] = "Your Key" # API key
os.environ["OPENAI_API_KEY"] = "Your Key"

# Instantiate tools
docs_tool = DirectoryReadTool(directory='./blog-posts')
file_tool = FileReadTool()
search_tool = SerperDevTool()
web_rag_tool = WebsiteSearchTool()

# Create agents
researcher = Agent(
    role='Market Research Analyst',
    goal='Provide up-to-date market analysis of the AI industry',
    backstory='An expert analyst with a keen eye for market trends.',
    tools=[search_tool, web_rag_tool],

writer = Agent(
    role='Content Writer',
    goal='Craft engaging blog posts about the AI industry',
    backstory='A skilled writer with a passion for technology.',
    tools=[docs_tool, file_tool],

# Define tasks
research = Task(
    description='Research the latest trends in the AI industry and provide a summary.',
    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',

write = Task(
    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
    output_file='blog-posts/'  # The final blog post will be saved here

# Assemble a crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research, write],

# Execute tasks

Available crewAI Tools

  • Error Handling: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
  • Caching Mechanism: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time, you can also define finner control over the caching mechanism, using cache_function attribute on the tool.

Here is a list of the available tools and their descriptions:

Tool Description
CodeDocsSearchTool A RAG tool optimized for searching through code documentation and related technical documents.
CSVSearchTool A RAG tool designed for searching within CSV files, tailored to handle structured data.
DirectorySearchTool A RAG tool for searching within directories, useful for navigating through file systems.
DOCXSearchTool A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.
DirectoryReadTool Facilitates reading and processing of directory structures and their contents.
FileReadTool Enables reading and extracting data from files, supporting various file formats.
GithubSearchTool A RAG tool for searching within GitHub repositories, useful for code and documentation search.
SerperDevTool A specialized tool for development purposes, with specific functionalities under development.
TXTSearchTool A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.
JSONSearchTool A RAG tool designed for searching within JSON files, catering to structured data handling.
MDXSearchTool A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.
PDFSearchTool A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.
PGSearchTool A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries.
RagTool A general-purpose RAG tool capable of handling various data sources and types.
ScrapeElementFromWebsiteTool Enables scraping specific elements from websites, useful for targeted data extraction.
ScrapeWebsiteTool Facilitates scraping entire websites, ideal for comprehensive data collection.
WebsiteSearchTool A RAG tool for searching website content, optimized for web data extraction.
XMLSearchTool A RAG tool designed for searching within XML files, suitable for structured data formats.
YoutubeChannelSearchTool A RAG tool for searching within YouTube channels, useful for video content analysis.
YoutubeVideoSearchTool A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.

Creating your own Tools

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:

pip install 'crewai[tools]'

Once you do that there are two main ways for one to create a crewAI tool:

Subclassing BaseTool

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"

Utilizing the tool Decorator

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
    return "Result from your custom tool"

Custom Caching Mechanism


Tools can optionally implement a cache_function to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.

from crewai_tools import tool

def multiplication_tool(first_number: int, second_number: int) -> str:
    """Useful for when you need to multiply two numbers together."""
    return first_number * second_number

def cache_func(args, result):
    # In this case, we only cache the result if it's a multiple of 2
    cache = result % 2 == 0
    return cache

multiplication_tool.cache_function = cache_func

writer1 = Agent(
        goal="You write lesssons of math for kids.",
        backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",

Using LangChain Tools

LangChain Integration

CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain LangChain Toolkit


from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper

# Setup API keys
os.environ["SERPER_API_KEY"] = "Your Key"

search = GoogleSerperAPIWrapper()

# Create and assign the search tool to an agent
serper_tool = Tool(
  name="Intermediate Answer",,
  description="Useful for search-based queries",

agent = Agent(
  role='Research Analyst',
  goal='Provide up-to-date market analysis',
  backstory='An expert analyst with a keen eye for market trends.',

# rest of the code ...


Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.