- name
- tavily-best-practices
- description
- 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
Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.
Installation
Python:
pip install tavily-pythonJavaScript:
npm install @tavily/coreSee 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 reportKey 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