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

Choosing the Right Method

If you are building a custom agent or agentic workflow:

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:

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