tavily-research
tavily-ai/skills
Conduct comprehensive AI-powered research with citations via Tavily CLI.
What is tavily-research?
Tavily research performs deep multi-source analysis and produces cited reports in 30-120 seconds. Use it when you need synthesis across multiple sources, comparisons, market analysis, or literature reviews—not for quick fact-finding.
- Gathers and analyzes multiple web sources automatically
- Produces structured reports with explicit citations in multiple formats (numbered, MLA, APA, Chicago)
- Supports async workflows with polling for long-running research
- Outputs JSON or markdown with customizable schemas
- Streams results in real-time to show progress
- Handles complex multi-angle topics with pro model
How to install tavily-research
npx skills add https://github.com/tavily-ai/skills --skill tavily-research- Tavily CLI installed and authenticated (curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login)
- Tavily API credentials configured
How to use tavily-research
- 1.Install Tavily CLI if not already present: curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login
- 2.Run tvly research with your query and desired options (e.g., tvly research "topic" --model pro)
- 3.Choose model: mini for single-topic (~30s), pro for comprehensive analysis (~60-120s), or auto for automatic selection
- 4.Optionally add flags: --stream for real-time progress, --json for structured output, -o filename to save to file
- 5.For async workflows, use --no-wait to get a request_id, then poll with tvly research poll <request_id>
Use cases
- Compare competing products or technologies (e.g., AI code assistants vs traditional IDEs)
- Analyze market trends and competitive landscapes (e.g., electric vehicle market analysis)
- Synthesize literature reviews across multiple sources
- Generate fintech or industry trend reports with citations
- Evaluate best practices or approaches for complex problems
- Researchers and analysts
- Product managers evaluating competitive landscapes
- Engineers investigating technical trends or frameworks
- Anyone needing multi-source synthesis with proper attribution
tavily-research FAQ
Use tavily-research for deep synthesis, comparisons, market analysis, and literature reviews. Use tavily-search for quick fact-finding and simple lookups.
Typically 30-120 seconds depending on complexity. Use --stream to see progress in real-time, or --no-wait for async operation.
Numbered, MLA, APA, and Chicago formats via the --citation-format option.
Yes, use --json for standard JSON output, or --output-schema with a path to a custom JSON schema for structured results matching your needs.
Use --no-wait to start research and get a request_id immediately, then use tvly research poll <request_id> to check results later.
Full instructions (SKILL.md)
Source of truth, from tavily-ai/skills.
name: tavily-research description: | 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. allowed-tools: Bash(tvly *)
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
--streamto see progress in real-time. - Use
--model profor complex comparisons or multi-faceted topics. - Use
--output-schemato get structured JSON output matching a custom schema. - For quick facts, use
tvly searchinstead — 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
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