Tavily Best Practices

Tavily

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

Installation

Python:

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pip install tavily-python

JavaScript:

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npm install @tavily/core

See references/sdk.md for complete SDK reference.

Client Initialization

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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:

For out-of-the-box research:

Quick Reference

search() - Web Search

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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

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# 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

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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

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

research() - AI-Powered Research

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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