tavily-research

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Translated

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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NPX Install

npx skill4agent add tavily-ai/skills tavily-research

Tags

Translated version includes tags in frontmatter
ai-researchtavily-clicited-report-generationmulti-source-synthesisasync-workflow

SKILL.md Content

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tavily research

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

Prerequisites

Requires the Tavily CLI. See tavily-cli for install and auth setup.
Quick install: 
curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

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

bash
# 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.md

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

Options

OptionDescription
--model
mini
, 
pro
, or 
auto
(default)
--stream
Stream results in real-time
--no-wait
Return request_id immediately (async)
--output-schema
Path to JSON schema for structured output
--citation-format
numbered
, 
mla
, 
apa
, 
chicago
--poll-interval
Seconds between checks (default: 10)
--timeout
Max wait seconds (default: 600)
-o, --output
Save output to file
--json
Structured JSON output

Model selection

ModelUse forSpeed
mini
Single-topic, targeted research~30s
pro
Comprehensive multi-angle analysis~60-120s
auto
API 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:
bash
# 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

  • tavily-search — quick web search for simple lookups
  • tavily-crawl — bulk extract from a site for your own analysis