tavily-api

Research

Implement web search, url content extraction, website crawling, and deep research with the tavily API. Use when building agentic workflows, rag systems, or autonomous agents that require real-time context from the web.

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Tavily is a specialized search API designed specifically for LLMs, enabling developers to build AI applications that can access real-time, accurate web data. Let's use the Python SDK to build with tavily.

Tavily Python SDK

Installation

pip install tavily-python

Client Initialization

from tavily import TavilyClient

client = TavilyClient(api_key="tvly-YOUR_API_KEY")

# Or use environment variable TAVILY_API_KEY
client = TavilyClient()

Async client:

The async client enables parallel query execution, ideal for agentic workflows that need to gather information quickly before passing it to a model for analysis.

from tavily import AsyncTavilyClient

async_client = AsyncTavilyClient(api_key="tvly-YOUR_API_KEY")

Available Endpoints

Endpoint	Purpose	Use Case
`search()`	Web search	real time data retrieval from the web
`extract()`	Scrape content from URLs	Page content extraction
`crawl() and map()`	Traverse website structures and simultaneously scrape pages	Documentation, site-wide extraction
`research`	Out of the box research agent	ready-to-use iterative research

Choosing the Right Method

If you are building a custom agent or agentic workflow:

Need	Method
Web search results	`search()`
Content from specific URLs	`extract()`
Content from an entire site	`crawl()`
URL discovery from a site	`map()`

These methods give you full control but require additional work: data processing, LLM integration, and workflow orchestration.

If you want an out-of-the-box solution:

Need	Method
End-to-end research with AI synthesis and built-in context engineering	`research()`

The research endpoint provides faster time-to-value with AI-synthesized insights, but offers less flexibility than building custom workflows.

Detailed Guides

For detailed usage instructions, parameters, patterns, and best practices:

references/search.md — Query optimization, filtering, async patterns, post-filtering strategies (regex + LLM verification)
references/extract.md — One-step vs two-step extraction, advanced mode, research pipelines
references/crawl.md — Crawl vs Map, depth/breadth control, path patterns, performance optimization
references/research.md — Usage, Streaming, structured output, polling