npx skills add ...
npx skills add tavily-ai/skills --skill tavily-best-practices
npx skills add tavily-ai/skills --skill tavily-best-practices
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.
Python:
JavaScript:
See references/sdk.md for complete SDK reference.
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() |
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.
Key parameters: urls (max 20), extract_depth, query, chunks_per_source (1-5)
See references/extract.md for complete extract reference.
Key parameters: url, max_depth, max_breadth, limit, instructions, chunks_per_source, select_paths, exclude_paths
See references/crawl.md for complete crawl reference.
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:
response = client.search(
query="quantum computing breakthroughs", # Keep under 400 chars
max_results=10,
search_depth="advanced"
)
print(response)# Simple one-step extraction
response = client.extract(
urls=["https://docs.example.com"],
extract_depth="advanced"
)
print(response)response = client.crawl(
url="https://docs.example.com",
instructions="Find API documentation pages", # Semantic focus
extract_depth="advanced"
)
print(response)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