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tavily-research

Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

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tavily research

AI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.

Before running

Research requires authentication. Run the requested command directly when tvly is already authenticated; do not add a status check to every invocation.

If tvly is missing, follow the tavily-cli setup. If an installed CLI reports an authentication error, use tvly login for authentication only, or tvly init --skip-skills when guided verification is also useful. Browser-based OAuth is preferred when an interactive user can complete it. --no-browser prints the sign-in link instead of opening it, but still waits for a localhost callback. In an unattended agent or CI environment, leave authentication to the user or use a securely provided TAVILY_API_KEY. Do not start a second login immediately after guided setup has completed.

When to use

  • You need comprehensive, multi-source analysis
  • The user wants a comparison, market report, or literature review
  • Quick searches aren't enough — you need synthesis with citations
  • Step 5 in the workflow: search → extract → map → crawl → research

Quick start

# Basic research (waits for completion)
tvly research "competitive landscape of AI code assistants"

# Pro model for comprehensive analysis
tvly research "electric vehicle market analysis" --model pro

# Stream results in real-time
tvly research "AI agent frameworks comparison" --stream

# Save report to file
tvly research "fintech trends 2025" --model pro -o fintech-report.json

# JSON output for agents
tvly research "quantum computing breakthroughs" --json

Options

OptionDescription
--modelmini, pro, or auto (default)
--streamStream results in real-time
--no-waitReturn request_id immediately (async)
--output-schemaPath to JSON schema for structured output
--citation-formatnumbered, mla, apa, chicago
--poll-intervalSeconds between checks (default: 10)
--timeoutMax wait seconds (default: 600)
-o, --outputSave the JSON response to a file
--jsonStructured JSON output

Model selection

ModelUse forSpeed
miniSingle-topic, targeted research~30s
proComprehensive multi-angle analysis~60-120s
autoAPI chooses based on complexityVaries

Rule of thumb: "What does X do?" → mini. "X vs Y vs Z" or "best way to..." → pro.

Async workflow

For long-running research, you can start and poll separately:

# Start without waiting
tvly research "topic" --no-wait --json    # returns request_id

# Check status
tvly research status <request_id> --json

# Wait for completion
tvly research poll <request_id> --json -o result.json

Tips

  • Research takes 30-120 seconds — use --stream to see progress in real-time.
  • Use --model pro for complex comparisons or multi-faceted topics.
  • Use --output-schema to get structured JSON output matching a custom schema.
  • For quick facts, use tvly search instead — research is for deep synthesis.
  • Read from stdin: echo "query" | tvly research - --json

See also

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.

日本語の概要は準備中です。原文の説明を表示しています。

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日本語の概要は準備中です。原文の説明を表示しています。

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日本語の概要は準備中です。原文の説明を表示しています。

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日本語の概要は準備中です。原文の説明を表示しています。

tavily-ai/skills4882026年9月5日 更新

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