tavily-best-practices

tarafından tavily-ai

We need to translate the given English text into Turkish, preserving the name "tavily-best-practices" but not including it unless it appears in the source. The source text does not include the name, so we just translate the description. We must preserve product names, protocol names, URLs, numbers, technical terms. No extra commentary. The text: "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..." Translate to Turkish. Note: "LLMs" should remain as is. "API" remains. Method names like search(), extract(), etc. remain. "SDKs" remains. "async clients" - maybe "asenk

npx skills add https://github.com/tavily-ai/skills --skill tavily-best-practices

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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