Tavily

Real-time web search and extraction for AI agents via one API

Technical setup Web · API

About

Call one API to search the web, scrape pages, filter, and return structured chunks your models can use. Developers building LLM and RAG systems use it to ground answers with fresh sources, run large research jobs, and keep latency steady at scale. Built-in safeguards block PII leakage and prompt injection, with caching and indexing to handle thousands of queries.

Editor's Take

We recommend Tavily for teams building production-grade LLM retrievers or RAG pipelines that need live-web grounding and predictable latency; best suited when you want one API to replace custom scrapers and pre-processing.

Key Features

Send one query to the Search API → get searching, scraping, filtering, and structured results in a single response
Point to live web sources → receive extracted content chunked for LLM consumption to reduce hallucinations
Spike traffic with thousands of queries → predictable latency via real-time search, intelligent caching, and indexing
Restrict domains or tune search depth → focus results on trusted sources for your use case
Route requests through safeguards → block PII leakage, prompt injection, and malicious content before results reach your agent

Use Cases

An LLM engineer grounding a customer-support bot with current web citations for policy and product updates
A data scientist running nightly research sweeps across selected domains to feed a RAG pipeline
A news automation developer monitoring hundreds of topics hourly with stable latency and filtered sources

Try It Like This

1
Ground a customer-support LLM with live docs
Developer points the Search API at the company docs domain → request returns chunked, source-attributed passages ready for embedding or direct prompt injection → use the chunks so your bot answers with citations and reduced hallucinations.
2
Nightly RAG ingestion pipeline
Schedule a job to send topic queries to the Search API each night → receive filtered, pre-scraped content chunks and metadata for indexing → store chunks in your vector DB to keep the RAG store fresh without custom scrapers.
3
Realtime monitoring for breaking news
Register a set of domains and topics, then call the API on an hourly cadence → get consistent structured results even under traffic spikes thanks to caching and indexing → trigger downstream alerts or summary jobs when new chunks match your filters.
4
Build a research agent that cites sources
Integrate the API as the agent's retriever so queries return filtered snippets with URLs → agent composes answers referencing returned source metadata → safeguards (PII/prompt-injection filters) reduce risk before content reaches the agent.
5
Controlled data collection from trusted sites
Configure domain restrictions and search depth to focus on approved sources → each API response returns content chunked for LLM consumption so you avoid noisy pages → use caching to scale thousands of queries without latency spikes.

Pros & Cons

Pros

Single API call handles searching, scraping, filtering, and returns structured chunks ready for LLM consumption — simplifies retriever pipelines.
Built-in safeguards (PII leakage and prompt injection filters) route requests before results reach agents, reducing exposure to malicious content.
Caching and indexing designed for scale provide predictable latency under spike traffic and support thousands of queries.

Cons

Search results may sometimes return cached or lower-quality links and don’t guarantee live high-quality sources for every query.

Getting Started

1 Sign up at tavily.com and create an API key in the dashboard
2 Call the Tavily Search API with a query, setting search_depth or allowed_domains as needed
3 Parse the structured JSON chunks and display sourced answers in your agent within minutes