Tavily is a search API built for AI agents and RAG pipelines, giving LLMs real-time, structured access to search, extract, and crawl the web.
Tavily is a search API built specifically for AI agents and retrieval-augmented generation, known as RAG, a technique where an LLM pulls in outside information before answering. Instead of returning a list of links for a human to click, Tavily returns reranked, information-dense snippets that an LLM can reason over directly. It also handles extracting clean content from a URL, crawling a whole site, and generating a synthesised research report from a single query. For developers building agents that need current, factual information, Tavily removes the work of scraping and cleaning web data yourself.
Tavily exposes its capabilities through a small set of API endpoints that agents call as tools, meaning the LLM decides when to invoke Tavily mid-conversation to fill a gap in its knowledge.
Search: returns reranked web results formatted as clean, structured snippets rather than raw HTML, so an agent gets the relevant information without wasting tokens filtering out noise.
Extract: pulls the clean article or page content from a specific URL, stripping ads, navigation, and other clutter, useful when an agent already knows which page it needs.
Crawl and map: capture an entire website as a structured graph of pages, useful when an agent needs to explore a site rather than answer one query.
Research: runs a multi-step process that searches, reads multiple sources, and returns a synthesised report with citations, instead of a single list of raw results.
Framework integrations: Tavily ships official integrations for LangChain, LangGraph, LlamaIndex, n8n, Zapier, and Make, so you can drop it into an existing agent pipeline without writing a custom wrapper.
Research assistants: an agent that answers questions about current events, recent papers, or fast-moving topics by searching the live web instead of relying on a fixed training cutoff.
Customer-facing chatbots with fresh data: a support or sales bot that checks current pricing, stock availability, or policy pages before answering, reducing outdated or hallucinated responses.
Competitive research agents: a workflow that regularly searches and extracts information about competitor products, pricing, or announcements and compiles it into a report.
RAG pipelines grounded in the web: a retrieval system that combines your own documents with live web search, so answers reflect both internal knowledge and current external information.
Automation workflows: a no-code flow in n8n, Zapier, or Make that triggers a Tavily search or extraction step as part of a larger business process, such as lead enrichment or content monitoring.
Multi-agent research systems: a LangGraph or CrewAI setup where one agent uses Tavily to gather sources and another synthesises them into a final answer or document.
Tavily gives AI agents and RAG applications a way to search, extract, and crawl the live web. Developers use it to answer questions an LLM cannot answer from its training data alone, such as current news, recent research papers, or up-to-date pricing, and to feed that information back into an agent's reasoning loop.
Tavily has a free tier called Researcher that includes 1,000 API credits a month with no credit card required. A basic search costs 1 credit and an advanced search costs 2 credits. Paid plans start around 30 dollars a month for teams that need higher volume or lower per-credit cost.
Tavily returns results already reranked and distilled into information-dense snippets, formatted for an LLM to reason over directly instead of a list of links for a human to click. It also bundles extraction, crawling, and multi-step research report generation into the same API, which a general search engine API does not provide.
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