tavily

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

---

1. Sign up for Tavily and create an API key
2. 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}'

> ```

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.

---

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.