- Skill ID
- tavily-ai/skills/tavily-research
- Publisher
- tavily-ai
- Repository
- skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
tavily-ai/skills/tavily-researchInstalls these files- SKILL.md
What this skill tells the agent
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" --jsonOptions
| 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.jsonTips
- Research takes 30-120 seconds — use
--streamto 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 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
More from tavily-ai
Use this skill in a project
Open any RiverX project, search the Skills panel for tavily-ai/skills/tavily-research, and install it in one click.
