> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tavily.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CrewAI

> Integrate Tavily with CrewAI to build powerful AI agents that can search the web.

## 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.

## Prerequisites

Before you begin, make sure you have:

* An OpenAI API key from [OpenAI Platform](https://platform.openai.com/)
* A Tavily API key from [Tavily Dashboard](https://app.tavily.com/sign-in)

## Installation

Install the required packages:

> **Note:** The stable python versions to use with CrewAI are `Python >=3.10 and Python <3.13` .

```bash theme={null}
pip install 'crewai[tools]'
pip install pydantic
```

## Setup

Set up your API keys:

```python theme={null}
import os

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

## 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.

```python theme={null}
import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilySearchTool
```

```python theme={null}
# Initialize the Tavily search tool
tavily_tool = TavilySearchTool()
```

```python theme={null}
# 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
)
```

```python theme={null}
# 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
)
```

```python theme={null}
# Form the crew and execute the task
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

result = crew.kickoff()
print(result)
```

### Customizing search tool parameters

**Example:**

```python theme={null}
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](/documentation/api-reference/endpoint/search) to discover all the customization options available for search operations.

<Accordion title="Full Code Example - Search">
  ```python theme={null}
  import os
  from crewai import Agent, Task, Crew
  from crewai_tools import TavilySearchTool

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

  # Initialize the 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 kick it off
  crew = Crew(
      agents=[researcher],
      tasks=[research_task],
      verbose=True
  )

  result = crew.kickoff()
  print(result)

  ```
</Accordion>

## 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.

```python theme={null}
import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilyExtractorTool
```

```python theme={null}
# Initialize the Tavily extractor tool
tavily_tool = TavilyExtractorTool()
```

```python theme={null}
# 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
)
```

```python theme={null}
# 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
)
```

```python theme={null}
# Create and run the crew
crew = Crew(
    agents=[extractor_agent],
    tasks=[extract_task],
    verbose=False
)

result = crew.kickoff()
print(result)
```

### Customizing extract tool parameters

**Example:**

```python theme={null}
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](/documentation/api-reference/endpoint/extract) to discover all the customization options available for extract operations.

<Accordion title="Full Code Example - Extract">
  ```python theme={null}
  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)
  ```
</Accordion>

For more information about Tavily's capabilities, check out our [API documentation](/documentation/api-reference/introduction) and [best practices](/documentation/best-practices/best-practices-search).