tavily
Tavily API for AI search. Use when user mentions "Tavily", "AI search", "research", or asks for cited search results.
What this skill does
## Troubleshooting
If requests fail, run `zero doctor check-connector --env-name TAVILY_TOKEN` or `zero doctor check-connector --url https://api.tavily.com/search --method POST`
## How to Use
All examples below assume you have `TAVILY_TOKEN` 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
curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer $TAVILY_TOKEN" -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
curl -s -X POST "https://api.tavily.com/search" --header "Content-Type: application/json" --header "Authorization: Bearer $TAVILY_TOKEN" -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_TOKEN }}`
- **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
1. **Use `advanced` only when necessary**: it consumes more resources and is best for deep research / high-value questions.
2. **Mind quotas and cost**: Tavily typically offers free tiers plus paid usage; in automation flows, add guards (filters, rate limits).
3. **Post-process results with an LLM**: use Tavily for retrieval, then let your LLM summarize, extract tables, or generate reports.
4. **Handle sensitive data carefully**: avoid sending raw secrets or PII directly in `query`; anonymize or mask when possible.
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