- name
- tavily-api-expert
- description
- Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
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.
Prerequisites
Tavily API Key Required - Get your key at https://tavily.com
Add to ~/.claude/settings.json:
{
"env": {
"TAVILY_API_KEY": "tvly-your-api-key-here"
}
}Restart Claude Code after adding your API key.
Tavily Python SDK
Installation
pip install tavily-pythonClient 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