tavily-best-practices

by tavily-ai

Web search API for LLMs with real-time data access, content extraction, site crawling, and AI-powered research. Five core methods: search() for web results, extract() for URL content, crawl() for site-wide extraction, map() for URL discovery, and research() for end-to-end AI synthesis Supports Python and JavaScript SDKs with async clients for parallel queries and configurable search depth (ultra-fast/fast/basic/advanced) Crawl method accepts semantic instructions to focus extraction on... npx skills add https://github.com/tavily-ai/skills --skill tavily-best-practices Download ZIPGitHub399

Tavily

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

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