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Introduction

This guide shows you how to integrate Tavily with CrewAI to create sophisticated AI agents that can search the web and extract content. By combining CrewAI’s multi-agent framework with Tavily’s real-time web search capabilities, you can build AI systems that research, analyze, and process web information autonomously.

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Prerequisites

Before you begin, make sure you have:

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Installation

Install the required packages:
Note: The stable python versions to use with CrewAI are Python >=3.10 and Python <3.13 . 
pip install 'crewai[tools]'
pip install pydantic

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Setup

Set up your API keys: 
import os

# Set your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

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Using Tavily Search with CrewAI

CrewAI provides built-in Tavily tools that make it easy to integrate web search capabilities into your AI agents. The TavilySearchTool allows your agents to search the web for real-time information. 
import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilySearchTool

# Initialize the Tavily search tool
tavily_tool = TavilySearchTool()

# Create an agent that uses the tool
researcher = Agent(
    role='News Researcher',
    goal='Find trending information about AI agents',
    backstory='An expert News researcher specializing in technology, focused on AI.',
    tools=[tavily_tool],
    verbose=True
)

# Create a task for the agent
research_task = Task(
    description='Search for the top 3 Agentic AI trends in 2025.',
    expected_output='A JSON report summarizing the top 3 AI trends found.',
    agent=researcher
)

# Form the crew and execute the task
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

result = crew.kickoff()
print(result)

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Customizing search tool parameters

Example: 
from crewai_tools import TavilySearchTool

# You can configure the tool with specific parameters
tavily_search_tool = TavilySearchTool(
    search_depth="advanced",
    max_results=10,
    include_answer=True
)
You can customize the search tool by passing parameters to configure its behavior.Below are available parameters in crewai integration: Available Parameters:
  • query (str): Required. The search query string.
  • search_depth (Literal[“basic”, “advanced”], optional): The depth of the search. Defaults to “basic”.
  • topic (Literal[“general”, “news”, “finance”], optional): The topic to focus the search on. Defaults to “general”.
  • time_range (Literal[“day”, “week”, “month”, “year”], optional): The time range for the search. Defaults to None.
  • max_results (int, optional): The maximum number of search results to return. Defaults to 5.
  • include_domains (Sequence[str], optional): A list of domains to prioritize in the search. Defaults to None.
  • exclude_domains (Sequence[str], optional): A list of domains to exclude from the search. Defaults to None.
  • include_answer (Union[bool, Literal[“basic”, “advanced”]], optional): Whether to include a direct answer synthesized from the search results. Defaults to False.
  • include_raw_content (bool, optional): Whether to include the raw HTML content of the searched pages. Defaults to False.
  • include_images (bool, optional): Whether to include image results. Defaults to False.
  • timeout (int, optional): The request timeout in seconds. Defaults to 60.
Explore More Parameters: For a complete list of available parameters and their descriptions, visit our API documentation to discover all the customization options available for search operations.

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Using Tavily Extract with CrewAI

The TavilyExtractorTool allows your CrewAI agents to extract and process content from specific web pages. This is particularly useful for content analysis, data collection, and research tasks. 
import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilyExtractorTool

# Initialize the Tavily extractor tool
tavily_tool = TavilyExtractorTool()

# Create an agent that uses the tool
extractor_agent = Agent(
    role='Web Page Content Extractor',
    goal='Extract key information from the given web pages',
    backstory='You are an expert at extracting relevant content from websites using the Tavily Extract.',
    tools=[tavily_tool],
    verbose=True
)

# Define a task for the agent
extract_task = Task(
    description='Extract the main content from the URL https://en.wikipedia.org/wiki/Lionel_Messi .',
    expected_output='A JSON string containing the extracted content from the URL.',
    agent=extractor_agent
)

# Create and run the crew
crew = Crew(
    agents=[extractor_agent],
    tasks=[extract_task],
    verbose=False
)

result = crew.kickoff()
print(result)

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Customizing extract tool parameters

Example: 
from crewai_tools import TavilyExtractorTool

# You can configure the tool with specific parameters
tavily_extract_tool = TavilyExtractorTool(
    extract_depth="advanced",
    include_images=True,
    timeout=45
)
You can customize the extract tool by passing parameters to configure its behavior. Below are available parameters in crewai integration: Available Parameters:
  • urls (Union[List[str], str]): Required. A single URL string or a list of URL strings to extract data from.
  • include_images (Optional[bool]): Whether to include images in the extraction results. Defaults to False.
  • extract_depth (Literal[“basic”, “advanced”]): The depth of extraction. Use “basic” for faster, surface-level extraction or “advanced” for more comprehensive extraction. Defaults to “basic”.
  • timeout (int): The maximum time in seconds to wait for the extraction request to complete. Defaults to 60.
Explore More Parameters: For a complete list of available parameters and their descriptions, visit our API documentation to discover all the customization options available for extract operations. 
import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilyExtractorTool

# Set up environment variables
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

# Initialize the Tavily extractor tool
tavily_tool = TavilyExtractorTool()

# Create an agent that uses the tool
extractor_agent = Agent(
    role='Web Page Content Extractor',
    goal='Extract key information from the given web pages',
    backstory='You are an expert at extracting relevant content from websites using the Tavily Extract.',
    tools=[tavily_tool],
    verbose=True
)

# Define a task for the agent
extract_task = Task(
    description='Extract the main content from the URL https://en.wikipedia.org/wiki/Lionel_Messi .',
    expected_output='A JSON string containing the extracted content from the URL.',
    agent=extractor_agent
)

# Create and execute the crew
crew = Crew(
    agents=[extractor_agent],
    tasks=[extract_task],
    verbose=True
)

# Run the extraction
result = crew.kickoff()
print("Extraction Results:")
print(result)
For more information about Tavily’s capabilities, check out our API documentation and best practices.
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