tavily-best-practices

Xây dựng tích hợp Tavily sẵn sàng cho sản xuất với các phương pháp hay nhất được tích hợp sẵn. Tài liệu tham khảo dành cho nhà phát triển sử dụng trợ lý mã hóa (Claude Code, Cursor,…)

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