tavily-best-practices
Build production-ready Tavily integrations with best practices for web search, content extraction, crawling, and research in agentic workflows, RAG systems, and autonomous agents
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/andrewyng/context-hub --skill tavily-best-practices- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tavily-best-practices automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
FAQ
What does the tavily-best-practices skill do?
Build production-ready Tavily integrations with best practices for web search, content extraction, crawling, and research in agentic workflows, RAG systems, and autonomous agents
How do I install tavily-best-practices?
Run npx skills add https://github.com/andrewyng/context-hub --skill tavily-best-practices in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tavily-best-practices/ (personal) or .claude/skills/tavily-best-practices/ (project).
Which AI agents can use tavily-best-practices?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tavily-best-practices safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains tavily-best-practices?
It is published by andrewyng in andrewyng/context-hub. It has 51 recorded installs.
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