From tavily
Provides best practices and reference docs for Tavily API integration in Python/JS apps for web search, content extraction, crawling, and AI research in agentic workflows or RAG systems.
npx claudepluginhub tavily-ai/skills --plugin tavilyThis skill uses the workspace's default tool permissions.
Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.
Enables web search, content extraction, site mapping, crawling, and research via Tavily REST API using curl. Use for live web data needs or LLM-friendly structured outputs when built-in tools unavailable.
Provides web search, content extraction, crawling, and research via Tavily API. Use for current web information, URL content extraction, or website crawling.
Performs AI-optimized web searches via Tavily API, returning clean AI-ready snippets for current info, news, and research. Supports deep mode, news filtering by days, result count; triggers on general search queries without URLs.
Share bugs, ideas, or general feedback.
Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.
Python:
pip install tavily-python
JavaScript:
npm install @tavily/core
See references/sdk.md for complete SDK reference.
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()
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() |
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.
# 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.
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.
response = client.map(
url="https://docs.example.com"
)
print(response)
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.
For complete parameters, response fields, patterns, and examples: