tavily

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Tavily Search API

Tavily 搜索API

Use Tavily's search API via direct

curl

calls to perform live web search, ideal for powering retrieval-augmented generation (RAG) for LLMs and agents.

Official documentation:
https://docs.tavily.com/

通过直接调用

curl

使用Tavily的搜索API来执行实时网络搜索，非常适合为LLM和Agent提供检索增强生成（RAG）支持。

官方文档：
https://docs.tavily.com/

When to Use

使用场景

Use this skill when you need:

Fresh, up-to-date information (news, trends, ongoing events)
Search results with sources/links to ground LLM or agent answers
Research / desk research inside automation workflows
A reliable retrieval layer for RAG, combined with skills like Notion or Firecrawl

在以下场景中使用该技能：

获取新鲜、实时的信息（新闻、趋势、正在发生的事件）
获取带来源/链接的搜索结果，为LLM或Agent的回答提供依据
在自动化工作流中进行调研/案头研究
为RAG搭建可靠的检索层，可与Notion或Firecrawl等技能结合使用

Prerequisites

前置条件

Sign up for Tavily and create an API key
Store your Tavily API key in the environment variable
```
TAVILY_API_KEY
```

Set it in your local shell or runtime environment, for example:

bash

export TAVILY_API_KEY="tvly-xxxxxxxxxxxxxxxx"

Important: When using
$VAR
in a command that pipes to another command, wrap the command containing
$VAR
in
bash -c '...'
. Due to a Claude Code bug, environment variables are silently cleared when pipes are used directly.
bash
bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"' | jq '.results[] | {title, url}'

注册Tavily账号并创建API密钥
将你的Tavily API密钥存储到环境变量
```
TAVILY_API_KEY
```
中

在本地Shell或运行时环境中设置，例如：

bash

export TAVILY_API_KEY="tvly-xxxxxxxxxxxxxxxx"

重要提示： 当在管道到另一个命令的命令中使用
$VAR
时，将包含
$VAR
的命令用
bash -c '...'
包裹。由于Claude Code的一个bug，当直接使用管道时，环境变量会被静默清除。
bash
bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"' | jq '.results[] | {title, url}'

How to Use

使用方法

All examples below assume you have

TAVILY_API_KEY

set in your environment. The base endpoint for the Tavily search API is a

POST

request to:

```
https://api.tavily.com/search
```

with a JSON body.

以下所有示例均假设你已在环境中设置了

TAVILY_API_KEY

。 Tavily搜索API的基础端点为向以下地址发送

POST

请求：

```
https://api.tavily.com/search
```

请求体为JSON格式。

1. Basic Search

1. 基础搜索

Write to

/tmp/tavily_request.json

json

{
  "query": "2025 AI Trending",
  "search_depth": "basic",
  "max_results": 5
}

Then run:

bash

bash -c 'curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer ${TAVILY_API_KEY}" -d @/tmp/tavily_request.json'

Key parameters:

```
query
```
: Search query or natural language question
```
search_depth
```
:
- ```
"basic"
```
  – faster, good for most use cases
- ```
"advanced"
```
  – deeper search and higher recall
```
max_results
```
: Maximum number of results to return (e.g. 3 / 5 / 10)

将以下内容写入

/tmp/tavily_request.json

：

json

{
  "query": "2025 AI Trending",
  "search_depth": "basic",
  "max_results": 5
}

然后运行：

bash

bash -c 'curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer ${TAVILY_API_KEY}" -d @/tmp/tavily_request.json'

关键参数：

```
query
```
：搜索查询或自然语言问题
```
search_depth
```
:
- ```
"basic"
```
  – 速度更快，适用于大多数场景
- ```
"advanced"
```
  – 搜索更深入，召回率更高
```
max_results
```
：返回的最大结果数（例如3/5/10）

2. Advanced Search

2. 高级搜索

Write to

/tmp/tavily_request.json

json

{
  "query": "serverless SaaS pricing best practices",
  "search_depth": "advanced",
  "max_results": 8,
  "include_answer": true,
  "include_domains": ["docs.aws.amazon.com", "cloud.google.com"],
  "exclude_domains": ["reddit.com", "twitter.com"],
  "include_raw_content": false
}

Then run:

bash

bash -c 'curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer ${TAVILY_API_KEY}" -d @/tmp/tavily_request.json'

Common advanced parameters:

```
include_answer
```
: When
```
true
```
, Tavily returns a summarized
```
answer
```
field
```
include_domains
```
: Whitelist of domains to include
```
exclude_domains
```
: Blacklist of domains to exclude
```
include_raw_content
```
: Whether to include raw page content (HTML / raw text). Default is
```
false
```
.

将以下内容写入

/tmp/tavily_request.json

：

json

{
  "query": "serverless SaaS pricing best practices",
  "search_depth": "advanced",
  "max_results": 8,
  "include_answer": true,
  "include_domains": ["docs.aws.amazon.com", "cloud.google.com"],
  "exclude_domains": ["reddit.com", "twitter.com"],
  "include_raw_content": false
}

然后运行：

bash

bash -c 'curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer ${TAVILY_API_KEY}" -d @/tmp/tavily_request.json'

常见高级参数：

```
include_answer
```
：设为
```
true
```
时，Tavily会返回一个汇总的
```
answer
```
字段
```
include_domains
```
：允许搜索的域名白名单
```
exclude_domains
```
：禁止搜索的域名黑名单
```
include_raw_content
```
：是否包含页面原始内容（HTML/纯文本），默认值为
```
false
```

3. Typical Response Structure (Example)

3. 典型响应结构（示例）

Tavily returns a JSON object similar to:

json

{
  "answer": "Brief summary...",
  "results": [
  {
  "title": "Article title",
  "url": "https://example.com/article",
  "content": "Snippet or extracted content...",
  "score": 0.89
  }
  ]
}

In agents or automation flows you typically:

Use
```
answer
```
as a concise, ready-to-use summary
Iterate over
```
results
```
to extract
```
title
```
+
```
url
```
as references / citations

Tavily返回的JSON对象类似如下格式：

json

{
  "answer": "Brief summary...",
  "results": [
  {
  "title": "Article title",
  "url": "https://example.com/article",
  "content": "Snippet or extracted content...",
  "score": 0.89
  }
  ]
}

在Agent或自动化流程中，你通常会：

使用
```
answer
```
作为简洁、可直接使用的摘要
遍历
```
results
```
提取
```
title
```
+
```
url
```
作为参考/引用

4. Using Tavily in n8n (HTTP Request Node)

4. 在n8n中使用Tavily（HTTP请求节点）

To integrate Tavily in n8n with the HTTP Request node:

Method:
```
POST
```
URL:
```
https://api.tavily.com/search
```

Headers:

```
Content-Type
```
:
```
application/json
```

Authorization

Bearer {{ $env.TAVILY_API_KEY }}

Body: JSON, for example:

json

{
  "query": "n8n self-hosted best practices",
  "search_depth": "basic",
  "max_results": 5
}

This lets you pipe Tavily search results into downstream nodes such as LLMs, Notion, Slack notifications, etc.

要在n8n中通过HTTP请求节点集成Tavily：

方法：
```
POST
```
URL：
```
https://api.tavily.com/search
```

请求头:

```
Content-Type
```
:
```
application/json
```

Authorization

Bearer {{ $env.TAVILY_API_KEY }}

请求体：JSON格式，例如：

json

{
  "query": "n8n self-hosted best practices",
  "search_depth": "basic",
  "max_results": 5
}

这样你就可以将Tavily的搜索结果传递给下游节点，如LLM、Notion、Slack通知等。

Guidelines

使用指南

Use
advanced
only when necessary: it consumes more resources and is best for deep research / high-value questions.
Mind quotas and cost: Tavily typically offers free tiers plus paid usage; in automation flows, add guards (filters, rate limits).
Post-process results with an LLM: use Tavily for retrieval, then let your LLM summarize, extract tables, or generate reports.
Handle sensitive data carefully: avoid sending raw secrets or PII directly in
```
query
```
; anonymize or mask when possible.

仅在必要时使用
advanced
模式：该模式会消耗更多资源，最适合深度研究/高价值问题。
注意配额和成本：Tavily通常提供免费层和付费使用选项；在自动化流程中，添加防护措施（过滤器、速率限制）。
用LLM对结果进行后处理：使用Tavily进行检索，然后让LLM生成摘要、提取表格或生成报告。
谨慎处理敏感数据：避免在
```
query
```
中直接发送原始密钥或个人身份信息（PII）；尽可能匿名化或掩码处理。