Tavily by Nebius
Build production AI agents that can reason, retrieve, and act fast and secure on the live web.
Transform the open web into structured, reasoning-ready context your AI agents can trust, verify, and use to make decisions in real time.
Ground agents with fresh, reasoning-ready web context
Retrieve live web data, extract relevant content, and return it structured for models, so your agents can reason over trusted information without hallucinating or wasting tokens.
Protect data with zero-retention privacy
Keep sensitive information private with zero-retention data handling, designed to support security, compliance, and enterprise requirements.
Thousands of web queries in seconds
Keep latency predictable as traffic grows with a production-grade retrieval stack with real-time search, intelligent caching, and indexing.
Built for agent workloads at scale
Developers building with Tavily (and growing)
Requests processed every month
Factual accuracy on agentic search benchmarks
Drop-in integration with leading LLM providers
OpenAI, Anthropic, Groq
Search capabilities to power any agentic workflow
Search the live web in real time
Distill fresh results into concise, source-aware context that agents can use without wasting tokens on noise.
Extract clean context from any page
Convert pages into structured, agent-ready content that reduces boilerplate and downstream token usage.
Generate comprehensive research reports
Run multi-step research across sources to analyze, synthesize, and return detailed reports with grounded context.
Crawl entire sites with intent
Extract content across websites using explicit objectives, producing stable, repeatable inputs for research pipelines and long-running agent workflows.
Map sites before agents fetch content
Generate normalized, deduplicated site graphs so agents know what pages exist, what’s reachable, and where to focus before retrieving content.
Reduce token waste and inference costs
Pre-processing, reranking, and context compression help reduce context window usage and avoid unnecessary model calls in high-volume agent workflows.