Tavily Search 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/

When to Use

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

Prerequisites

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}'

How to Use

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.

1. Basic Search

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)

2. Advanced Search

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
```
.

3. Typical Response Structure (Example)

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

4. Using Tavily in n8n (HTTP Request Node)

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.

Guidelines

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.

tavily

NPX Install

Tags

SKILL.md Content

Tavily Search API

When to Use

Prerequisites

How to Use

1. Basic Search

2. Advanced Search

3. Typical Response Structure (Example)

4. Using Tavily in n8n (HTTP Request Node)

Guidelines