T

tavily-search

by @tavily-aiv
4.6(120)

Tavily Search delivers LLM-optimized web search results, featuring concise content snippets and relevance scores. It empowers AI agents to swiftly gather up-to-date information on any topic, proving especially valuable for RAG applications that demand precise facts and comprehensive context, ultimately enhancing information retrieval efficiency and the quality of model responses.

web-searchinformation-retrievalllm-optimizationragGitHub
Installation
npx skills add https://github.com/tavily-ai/skills --skill tavily-search
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Before / After Comparison

1
Before

When performing complex information retrieval for Large Language Models (LLMs), traditional search methods often consume significant time sifting through irrelevant content, leading to inefficient RAG pipelines and compromised model output quality.

After

With Tavily Search's optimized results, AI agents can quickly access highly relevant and concise information, drastically reducing information retrieval time and significantly boosting RAG pipeline efficiency and LLM response accuracy.

SKILL.md

tavily search

Web search returning LLM-optimized results with content snippets and relevance scores.

Before running any command

If tvly is not found on PATH, install it first:

curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

Do not skip this step or fall back to other tools.

See tavily-cli for alternative install methods and auth options.

When to use

  • You need to find information on any topic
  • You don't have a specific URL yet
  • First step in the workflow: search → extract → map → crawl → research

Quick start

# Basic search
tvly search "your query" --json

# Advanced search with more results
tvly search "quantum computing" --depth advanced --max-results 10 --json

# Recent news
tvly search "AI news" --time-range week --topic news --json

# Domain-filtered
tvly search "SEC filings" --include-domains sec.gov,reuters.com --json

# Include full page content in results
tvly search "react hooks tutorial" --include-raw-content --max-results 3 --json

Options

OptionDescription
--depthultra-fast, fast, basic (default), advanced
--max-resultsMax results, 0-20 (default: 5)
--topicgeneral (default), news, finance
--time-rangeday, week, month, year
--start-dateResults after date (YYYY-MM-DD)
--end-dateResults before date (YYYY-MM-DD)
--include-domainsComma-separated domains to include
--exclude-domainsComma-separated domains to exclude
--countryBoost results from country
--include-answerInclude AI answer (basic or advanced)
--include-raw-contentInclude full page content (markdown or text)
--include-imagesInclude image results
--include-image-descriptionsInclude AI image descriptions
--chunks-per-sourceChunks per source (advanced/fast depth only)
-o, --outputSave output to file
--jsonStructured JSON output

Search depth

DepthSpeedRelevanceBest for
ultra-fastFastestLowerReal-time chat, autocomplete
fastFastGoodNeed chunks, latency matters
basicMediumHighGeneral-purpose (default)
advancedSlowerHighestPrecision, specific facts

Tips

  • Keep queries under 400 characters — think search query, not prompt.
  • Break complex queries into sub-queries for better results.
  • Use --include-raw-content when you need full page text (saves a separate extract call).
  • Use --include-domains to focus on trusted sources.
  • Use --time-range for recent information.
  • Read from stdin: echo "query" | tvly search - --json

See also

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Statistics

Installs25.4K
Rating4.6 / 5.0
Version
Updated2026年7月18日
Comparisons1

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4.6(120)
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Timeline

Created2026年5月29日
Last Updated2026年7月18日
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