tavily

Perform real-time web search and structured content extraction via the Tavily API.

22|1|Updated Jan 10, 2026
One-click install
npx skills add https://github.com/itechmeat/llm-code --skill tavily-itechmeat
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tavily
Source: https://github.com/itechmeat/llm-code/tree/main/skills/tavily
Command: npx skills add https://github.com/itechmeat/llm-code --skill tavily-itechmeat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Tavily provides a scalable interface to perform real-time web searches, extract content, crawl sites, map structures, and conduct autonomous research for AI agents, delivering ready-to-use data for RAG pipelines.

Core Features & Use Cases

  • Search: Real-time web search with AI-generated answers and content extraction for immediate integrated results.
  • Extract / Map / Crawl / Research: Build end-to-end workflows for content gathering, site mapping, and autonomous research.
  • Use Case: An AI agent needs current information about a topic; Tavily powers the search, extracts relevant content, and compiles sources for downstream reasoning.

Quick Start

Use the Tavily Python or JavaScript SDKs or REST API to perform a search like: client.search("What is the latest in AI?").

Frequently Asked Questions about tavily

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get real-time web search results into my RAG pipeline?

Real-time web search for RAG pipelines is achieved by using API clients to query the web, extract relevant content, and compile sources. This delivers ready-to-use data directly into your downstream reasoning components.

Can AI agents autonomously crawl websites and extract structured content?

AI agents can autonomously crawl websites and extract structured content using dedicated API operations. This allows them to gather data, map site structures, and compile information for end-to-end research workflows.

What is the best way to perform autonomous research across diverse domains with LLMs?

Autonomous research across diverse domains is performed by integrating search, extract, and map operations into your LLM application. This provides scalable interfaces to gather and compile current information for agent reasoning.

Do I need usage credits to perform web searches and site mapping?

You need usage credits to perform web searches and site mapping. Accessing the search, extract, crawl, and map operations requires connecting through the API client libraries or REST access, which consumes these credits.

How do I extract content from web pages for AI-generated answers?

Content extraction for AI-generated answers is handled by executing a search query through the API client. The system performs real-time web search, extracts relevant content, and returns immediate integrated results.

How does web search and content extraction work for autonomous AI agents?

Web search and content extraction for autonomous AI agents works by providing a scalable API interface that queries the web, maps site structures, and delivers ready-to-use data directly into RAG pipelines for reasoning.