majiayu000 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