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tavily-research

by @tavily-aiv
4.5(104)

Tavily Research provides AI-driven deep research, collecting, analyzing sources, and generating reports to improve research efficiency and quality.

ai-researchdeep-researchinformation-gatheringdata-analysisGitHub
Installation
npx skills add tavily-ai/skills --skill tavily-research
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Before / After Comparison

1
Before

Traditional research requires manual collection and analysis of information, which is time-consuming, laborious, and inefficient.

After

AI-driven in-depth research automatically collects and analyzes sources, quickly gaining insights.

SKILL.md

tavily-research

tavily research

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

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 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.md

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

Options

Option Description

--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

Model Use for Speed

mini Single-topic, targeted research ~30s

pro Comprehensive multi-angle analysis ~60-120s

auto API chooses based on complexity Varies

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

Weekly Installs465Repositorytavily-ai/skillsGitHub Stars95First Seen2 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykFailInstalled oncodex457opencode456cursor456kimi-cli455gemini-cli455amp455

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Statistics

Installs10.4K
Rating4.5 / 5.0
Version
Updated2026年5月23日
Comparisons1

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Timeline

Created2026年3月18日
Last Updated2026年5月23日