tavily

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:

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

Then run:

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:

{
  "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:

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:

{
  "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:

{
  "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.