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Warning: The langchain_community.tools.tavily_search.tool is deprecated. While it remains functional for now, we strongly recommend migrating to the new langchain-tavily Python package which supports Search, Extract, Map, and Crawl functionality and receives continuous updates with the latest features.
The langchain-tavily Python package is the official LangChain integration of Tavily, including Search, Extract, Map, and Crawl functionality.

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Installation

pip install -U langchain-tavily

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Credentials

We also need to set our Tavily API key. You can get an API key by visiting this site and creating an account. 
import getpass
import os

if not os.environ.get("TAVILY_API_KEY"):
    os.environ["TAVILY_API_KEY"] = getpass.getpass("Tavily API key:\n")
Here we show how to instantiate the Tavily search tool. This tool allows you to complete search queries using Tavily’s Search API endpoint.

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Available Parameters

The Tavily Search API accepts various parameters to customize the search:
  • max_results (optional, int): Maximum number of search results to return. Default is 5.
  • topic (optional, str): Category of the search. Can be “general”, “news”, or “finance”. Default is “general”.
  • include_answer (optional, bool): Include an answer to original query in results. Default is False.
  • include_raw_content (optional, bool): Include cleaned and parsed HTML of each search result. Default is False.
  • include_images (optional, bool): Include a list of query related images in the response. Default is False.
  • include_image_descriptions (optional, bool): Include descriptive text for each image. Default is False.
  • search_depth (optional, str): Depth of the search, either “basic” or “advanced”. Default is “basic”.
  • time_range (optional, str): The time range back from the current date ( publish date ) to filter results - “day”, “week”, “month”, or “year”. Default is None.
  • start_date (optional, str): Will return all results after the specified start date ( publish date ). Required to be written in the format YYYY-MM-DD. Default is None.
  • end_date (optional, str): Will return all results before the specified end date. Required to be written in the format YYYY-MM-DD. Default is None.
  • include_domains (optional, List[str]): List of domains to specifically include. Maximum 300 domains. Default is None.
  • exclude_domains (optional, List[str]): List of domains to specifically exclude. Maximum 150 domains. Default is None.
For a comprehensive overview of the available parameters, refer to the Tavily Search API documentation

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Instantiation

from langchain_tavily import TavilySearch

tool = TavilySearch(
    max_results=5,
    topic="general",
    # include_answer=False,
    # include_raw_content=False,
    # include_images=False,
    # include_image_descriptions=False,
    # search_depth="basic",
    # time_range="day",
    # start_date=None,
    # end_date=None,
    # include_domains=None,
    # exclude_domains=None
)

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Invoke directly with args

The Tavily search tool accepts the following arguments during invocation:
  • query (required): A natural language search query
  • The following arguments can also be set during invocation: include_images, search_depth, time_range, include_domains, exclude_domains, start_date, end_date
  • For reliability and performance reasons, certain parameters that affect response size cannot be modified during invocation: include_answer and include_raw_content. These limitations prevent unexpected context window issues and ensure consistent results.
NOTE: The optional arguments are available for agents to dynamically set. If you set an argument during instantiation and then invoke the tool with a different value, the tool will use the value you passed during invocation.

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Direct Tool Invocation

# Basic usage
result = tavily_search.invoke({"query": "What happened at the last wimbledon"})
Example output: 
{
 'query': 'What happened at the last wimbledon',
 'follow_up_questions': None,
 'answer': None,
 'images': [],
 'results': [
   {'url': 'https://en.wikipedia.org/wiki/Wimbledon_Championships',
    'title': 'Wimbledon Championships - Wikipedia',
    'content': 'Due to the COVID-19 pandemic, Wimbledon 2020 was cancelled ...',
    'score': 0.62365627198,
    'raw_content': None},
   {'url': 'https://www.cbsnews.com/news/wimbledon-men-final-carlos-alcaraz-novak-djokovic/',
    'title': "Carlos Alcaraz beats Novak Djokovic at Wimbledon men's final to ...",
    'content': 'In attendance on Sunday was Catherine, the Princess of Wales ...',
    'score': 0.5154731446,
    'raw_content': None}
 ],
 'response_time': 2.3
}

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Use with Agent

# !pip install -qU langchain langchain-openai langchain-tavily
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from langchain_tavily import TavilySearch

# Initialize the Tavily Search tool
tavily_search = TavilySearch(max_results=5, topic="general")

# Initialize the agent with the search tool
agent = create_agent(
    model=ChatOpenAI(model="gpt-5"),
    tools=[tavily_search],
    system_prompt="You are a helpful research assistant. Use web search to find accurate, up-to-date information."
)

# Use the agent
response = agent.invoke({
    "messages": [{"role": "user", "content": "What is the most popular sport in the world? Include only Wikipedia sources."}]
})
Tip: For more relevant and time-aware results, inject today’s date into your system prompt. This helps the agent understand the current context when searching for recent information. For example: f"You are a helpful research assistant. Today's date is {datetime.today().strftime('%B %d, %Y')}. Use web search to find accurate, up-to-date information."

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Tavily Extract

Here we show how to instantiate the Tavily extract tool. This tool allows you to extract content from URLs using Tavily’s Extract API endpoint.

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Available Parameters

The Tavily Extract API accepts various parameters:
  • extract_depth (optional, str): The depth of the extraction, either “basic” or “advanced”. Default is “basic”.
  • include_images (optional, bool): Whether to include images in the extraction. Default is False.
For a comprehensive overview of the available parameters, refer to the Tavily Extract API documentation

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Instantiation

from langchain_tavily import TavilyExtract

tool = TavilyExtract(
    extract_depth="basic",
    # include_images=False
)

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Invoke directly with args

The Tavily extract tool accepts the following arguments during invocation:
  • urls (required): A list of URLs to extract content from.
  • Both extract_depth and include_images can also be set during invocation
NOTE: The optional arguments are available for agents to dynamically set. If you set an argument during instantiation and then invoke the tool with a different value, the tool will use the value you passed during invocation.

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Direct Tool Invocation

# Extract content from a URL
result = tavily_extract.invoke({
    "urls": ["https://en.wikipedia.org/wiki/Lionel_Messi"]
})
Example output: 
{
    'results': [{
        'url': 'https://en.wikipedia.org/wiki/Lionel_Messi',
        'raw_content': 'Lionel Messi\nLionel Andrés "Leo" Messi...',
        'images': []
    }],
    'failed_results': [],
    'response_time': 0.79
}

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Tavily Map/Crawl

Tavily provides two complementary tools for website exploration: Map and Crawl. The map tool discovers and lists URLs from a website, providing a structural overview without extracting content. The crawl tool then extracts the full content from these discovered URLs, making it ideal for data extraction, documentation indexing, and building knowledge bases.

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Tavily Map

The Map tool discovers all internal links starting from a base URL, perfect for understanding site structure or planning content extraction.

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Available Parameters

  • url (required, str): The root URL to begin mapping.
  • instructions (optional, str): Natural language instructions guiding the mapping process.
For a comprehensive overview, refer to the Tavily Map API documentation

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Instantiation

from langchain_tavily import TavilyMap

tool = TavilyMap()

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Direct Tool Invocation

# Map a website structure
result = tavily_map.invoke({
    "url": "https://docs.example.com",
    "instructions": "Find all documentation and tutorial pages"
})
Example output: 
{
    'base_url': 'https://docs.example.com',
    'results': [
        'https://docs.example.com',
        'https://docs.example.com/api',
        'https://docs.example.com/tutorials',
        'https://docs.example.com/api/endpoints',
        'https://docs.example.com/tutorials/getting-started'
    ],
    'request_id': 'req_abc123',
    'response_time': 2.1
}

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Tavily Crawl

The Crawl tool extracts full content from URLs. It works perfectly with mapped URLs or can be used standalone to crawl from a starting point.

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Available Parameters

  • url (required, str): The root URL to begin the crawl.
  • instructions (optional, str): Natural language instructions guiding content extraction.
For a comprehensive overview, refer to the Tavily Crawl API documentation

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Instantiation

from langchain_tavily import TavilyCrawl

tool = TavilyCrawl()

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Direct Tool Invocation

# Crawl and extract content
result = tavily_crawl.invoke({
    "url": "https://docs.example.com",
    "instructions": "Extract API documentation and code examples"
})
Example output: 
{
    'base_url': 'https://docs.example.com',
    'results': [
        {
            'url': 'https://docs.example.com',
            'raw_content': '# Documentation\nWelcome to our API documentation...'
        },
        {
            'url': 'https://docs.example.com/api',
            'raw_content': '# API Reference\nComplete API reference guide...'
        }
    ],
    'response_time': 4.5,
    'request_id': 'req_abc123'
}

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Tavily Research Agent

This example demonstrates how to build a powerful web research agent using Tavily’s search and extract tools with create_agent.

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Features

  • Internet Search: Query the web for up-to-date information using Tavily’s search API
  • Content Extraction: Extract and analyze specific content from web pages
  • Seamless Integration: Simple, elegant agent setup with LangChain v1
# !pip install -qU langchain langchain-openai langchain-tavily
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from langchain_tavily import TavilySearch, TavilyExtract

# Initialize Tavily tools
tavily_search = TavilySearch(max_results=5, topic="general")
tavily_extract = TavilyExtract()

# Create the research agent
agent = create_agent(
    model=ChatOpenAI(model="gpt-5"),
    tools=[tavily_search, tavily_extract],
    system_prompt="""You are a helpful research assistant. Use web search to find relevant 
    information, then extract detailed content from the most promising sources to provide 
    comprehensive insights."""
)

# Use the agent
response = agent.invoke({
    "messages": [{
        "role": "user", 
        "content": "Research the latest developments in quantum computing and provide a detailed summary of how it might impact cybersecurity in the next decade."
    }]
})
Tip: For more relevant and time-aware results, inject today’s date into your system prompt. This helps the agent understand the current context when searching for recent information. For example: f"You are a helpful research assistant. Today's date is {datetime.today().strftime('%B %d, %Y')}. Use web search to find relevant information..."