# Tavily Best Practices

Build production-ready Tavily integrations with best practices for web search, content...

- Repository: https://github.com/andrewyng/context-hub (13986 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-05-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add andrewyng/context-hub --skill tavily-best-practices -g -a claude-code -y`
Codex: `npx skills add andrewyng/context-hub --skill tavily-best-practices -g -a codex -y`
Cursor: `npx skills add andrewyng/context-hub --skill tavily-best-practices -g -a cursor -y`
Gemini CLI: `npx skills add andrewyng/context-hub --skill tavily-best-practices -g -a gemini-cli -y`

## Description

Build production-ready Tavily integrations with best practices for web search, content extraction, crawling, and research in agentic workflows, RAG systems, and autonomous agents

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Tavily

Tavily is a search API designed for LLMs, enabling AI applications to access real-time web data.

## Installation

**Python:**
```bash
pip install tavily-python
```

**JavaScript:**
```bash
npm install @tavily/core
```

See **[references/sdk.md](references/sdk.md)** for complete SDK reference.

## Client Initialization

```python
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

```python
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](references/search.md)** for complete search reference.

### extract() - URL Content Extraction

```python
# 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](references/extract.md)** for complete extract reference.

### crawl() - Site-Wide Extraction

The rest: https://github.com/andrewyng/context-hub/blob/HEAD/content/tavily/skills/tavily-best-practices/SKILL.md