Tavily

Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.

Installation

Python:

pip install tavily-python

JavaScript:

npm install @tavily/core

See references/sdk.md for complete SDK reference.

Client Initialization

from tavily import TavilyClient

Uses TAVILY_API_KEY env var (recommended)

client = TavilyClient()

#With project tracking (for usage organization) client = TavilyClient(project_id="your-project-id")

Async client for parallel queries

from tavily import AsyncTavilyClient async_client = AsyncTavilyClient()

Choosing the Right Method

For custom agents/workflows:

Need Method

Web search results search()

Content from specific URLs extract()

Content from entire site crawl()

URL discovery from site map()

For out-of-the-box research:

Need Method

End-to-end research with AI synthesis research()

Quick Reference

search() - Web Search

response = client.search( query="quantum computing breakthroughs", # Keep under 400 chars max_results=10, search_depth="advanced" ) print(response)

Key parameters: query , max_results , search_depth (ultra-fast/fast/basic/advanced), include_domains , exclude_domains , time_range

See references/search.md for complete search reference.

extract() - URL Content Extraction

Simple one-step extraction

response = client.extract( urls=["https://docs.example.com"], extract_depth="advanced" ) print(response)

Key parameters: urls (max 20), extract_depth , query , chunks_per_source (1-5)

See references/extract.md for complete extract reference.

crawl() - Site-Wide Extraction

response = client.crawl( url="https://docs.example.com", instructions="Find API documentation pages", # Semantic focus extract_depth="advanced" ) print(response)

Key parameters: url , max_depth , max_breadth , limit , instructions , chunks_per_source , select_paths , exclude_paths

See references/crawl.md for complete crawl reference.

map() - URL Discovery

response = client.map( url="https://docs.example.com" ) print(response)

research() - AI-Powered Research

import time

For comprehensive multi-topic research

result = client.research( input="Analyze competitive landscape for X in SMB market", model="pro" # or "mini" for focused queries, "auto" when unsure ) request_id = result["request_id"]

Poll until completed

response = client.get_research(request_id) while response["status"] not in ["completed", "failed"]: time.sleep(10) response = client.get_research(request_id)

print(response["content"]) # The research report

Key parameters: input , model ("mini"/"pro"/"auto"), stream , output_schema , citation_format

See references/research.md for complete research reference.

Detailed Guides

For complete parameters, response fields, patterns, and examples:

• references/sdk.md - Python & JavaScript SDK reference, async patterns, Hybrid RAG

• references/search.md - Query optimization, search depth selection, domain filtering, async patterns, post-filtering

• references/extract.md - One-step vs two-step extraction, query/chunks for targeting, advanced mode

• references/crawl.md - Crawl vs Map, instructions for semantic focus, use cases, Map-then-Extract pattern

• references/research.md - Prompting best practices, model selection, streaming, structured output schemas

• references/integrations.md - LangChain, LlamaIndex, CrewAI, Vercel AI SDK, and framework integrations