tavily-research

द्वारा tavily-ai

बहु-स्रोत संश्लेषण और उद्धरणों के साथ व्यापक AI-संचालित अनुसंधान। मॉडल चयन के आधार पर 30-120 सेकंड लेते हुए वेब स्रोतों पर आधारित संरचित रिपोर्ट तैयार करता है (लक्षित प्रश्नों के लिए मिनी, जटिल तुलनाओं के लिए प्रो)। कई आउटपुट प्रारूपों का समर्थन करता है: मार्कडाउन रिपोर्ट, कस्टम स्कीमा के साथ JSON, और कॉन्फ़िगरेबल उद्धरण शैलियाँ (क्रमांकित, MLA, APA, शिकागो)। --no-wait, status, और poll कमांड के

npx skills add https://github.com/tavily-ai/skills --skill tavily-research

ZIP डाउनलोड करें GitHub

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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

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 the JSON response to a 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