OpenClaw

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.

evanydl0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install evanydl/tavily-bestpractices

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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 d...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/evanydl/tavily-bestpractices
Version: —
Commit: 625f054b328e9f3c42b219748fa7a816b879454a
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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

```python
response = client.crawl(
    url="https://docs.example.com",
    instructions="Find API d...

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