# Tavily Research

> Conduct deep, multi-source web research with citations using the Tavily CLI, producing structured reports for comparisons, market analysis, and literature reviews.

- Skill: `scoheart/tavily-research` (Agent Skill)
- Install (CLI): `npx skillmds add scoheart/tavily-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/scoheart/tavily-research/raw
- Safety review: WARNING (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search, Web Research
- Tags: Citations, Report, Research, Tavily, Web Search
- Author: Scoheart (https://skillmd.com/u/scoheart)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/scoheart/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:

```bash
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](../tavily-cli/SKILL.md) 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](../tavily-cli/SKILL.md): 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

| 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:

```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](../tavily-search/SKILL.md) — quick web search for simple lookups
- [tavily-crawl](../tavily-crawl/SKILL.md) — bulk extract from a site for your own analysis