For AI agents: Use llms.txt for a full index of all documentation.

Build with Tavily

Your journey to state-of-the-art web search starts right here.

Onboard your agent

Let your coding agent install and configure Tavily for you.

Read and execute tavily.com/agent-setup/SKILL.md

Install the SDK

Add the Tavily client to your project.

Python

pip install tavily-python

JavaScript

npm i @tavily/core

Try it now

Make your first request to any Tavily endpoint.

Search the web
Extract webpages
Crawl webpages
Map webpages
Create Research Task

from tavily import TavilyClient

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.search("Who is Leo Messi?")

print(response)

const { tavily } = require("@tavily/core");

const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });
const response = await tvly.search("Who is Leo Messi?");

console.log(response);

curl --request POST \
  --url https://api.tavily.com/search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "query": "who is Leo Messi?",
  "auto_parameters": false,
  "topic": "general",
  "search_depth": "basic",
  "chunks_per_source": 3,
  "max_results": 1,
  "time_range": null,
  "start_date": "2025-02-09",
  "end_date": "2025-12-29",
  "include_answer": false,
  "include_raw_content": false,
  "include_images": false,
  "include_image_descriptions": false,
  "include_favicon": false,
  "include_domains": [],
  "exclude_domains": [],
  "country": null,
  "include_usage": false
}
'

Learn more about the Search API →

from tavily import TavilyClient

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.extract("https://en.wikipedia.org/wiki/Artificial_intelligence")

print(response)

const { tavily } = require("@tavily/core");

const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });
const response = await tvly.extract("https://en.wikipedia.org/wiki/Artificial_intelligence");

console.log(response);

curl --request POST \
  --url https://api.tavily.com/extract \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "urls": "https://en.wikipedia.org/wiki/Artificial_intelligence",
  "query": "<string>",
  "chunks_per_source": 3,
  "extract_depth": "basic",
  "include_images": false,
  "include_favicon": false,
  "format": "markdown",
  "timeout": "None",
  "include_usage": false
}
'

Learn more about the Extract API →

from tavily import TavilyClient

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.crawl("https://docs.tavily.com", instructions="Find all pages on the Python SDK")

print(response)

const { tavily } = require("@tavily/core");

const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });
const response = await tvly.crawl("https://docs.tavily.com", { instructions: "Find all pages on the Python SDK" });

console.log(response);

curl --request POST \
  --url https://api.tavily.com/crawl \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "url": "docs.tavily.com",
  "instructions": "Find all pages about the Python SDK",
  "chunks_per_source": 3,
  "max_depth": 1,
  "max_breadth": 20,
  "limit": 50,
  "select_paths": null,
  "select_domains": null,
  "exclude_paths": null,
  "exclude_domains": null,
  "allow_external": true,
  "include_images": false,
  "extract_depth": "basic",
  "format": "markdown",
  "include_favicon": false,
  "timeout": 150,
  "include_usage": false
}
'

Learn more about the Crawl API →

from tavily import TavilyClient

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.map("https://docs.tavily.com")

print(response)

const { tavily } = require("@tavily/core");

const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });
const response = await tvly.map("https://docs.tavily.com");

console.log(response);

curl --request POST \
  --url https://api.tavily.com/map \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "url": "docs.tavily.com",
  "instructions": "Find all pages about the Python SDK",
  "max_depth": 1,
  "max_breadth": 20,
  "limit": 50,
  "select_paths": null,
  "select_domains": null,
  "exclude_paths": null,
  "exclude_domains": null,
  "allow_external": true,
  "timeout": 150,
  "include_usage": false
}
'

Learn more about the Map API →

from tavily import TavilyClient

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.research("What are the latest developments in AI?")

print(response)

const { tavily } = require("@tavily/core");

const tvly = tavily({ apiKey: "tvly-YOUR_API_KEY" });
const response = await tvly.research("What are the latest developments in AI?");

console.log(response);

curl --request POST \
  --url https://api.tavily.com/research \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "What are the latest developments in AI?",
  "model": "auto",
  "stream": false,
  "output_schema": {
    "properties": {
      "company": {
        "type": "string",
        "description": "The name of the company"
      },
      "key_metrics": {
        "type": "array",
        "description": "List of key performance metrics",
        "items": {
          "type": "string"
        }
      },
      "financial_details": {
        "type": "object",
        "description": "Detailed financial breakdown",
        "properties": {
          "operating_income": {
            "type": "number",
            "description": "Operating income for the period"
          }
        }
      }
    },
    "required": [
      "company"
    ]
  },
  "citation_format": "numbered"
}
'

Learn more about the Research API →

Developer Resources

Credits, limits, and a place to test your queries.

API Credits Overview

Learn how Tavily API credits work.

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Understand Tavily’s rate limits and policies.

Playground

Try Tavily’s APIs interactively.

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