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.
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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:
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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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蚂蚁银行开户
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Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.
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Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
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仅使用 AWS CLI 操作 AWS。Agent 直接执行 aws 命令（无需脚本）。仅在用户提到 AWS、S3、EC2、Lambda 等时激活。
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