Tavily Best Practices
tavily-ai/skillsThis skill provides access to Tavily's search API, enabling real-time web data retrieval for AI applications. It offers capabilities such as web search, URL content extraction, site crawling, URL discovery, and AI-powered research, making it suitable for developers building AI agents, research tools, and data pipelines. The skill supports both SDK and command-based integrations for creating custom workflows or using out-of-the-box research functions.
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
GitHub Owner
Owner: tavily-ai
GitHub Links
- Website: http://tavily.com
- Email: support@tavily.com
Files
sdk.md
- View: https://github.com/tavily-ai/skills/blob/HEAD/skills/tavily/tavily-best-practices/references/sdk.md
search.md
extract.md
crawl.md
research.md
sdk.md
- View: https://github.com/tavily-ai/skills/blob/HEAD/skills/tavily/tavily-best-practices/references/sdk.md