Tavily: Search API for AI Agents

Tavily is a search API built specifically for AI agents and RAG pipelines. Instead of returning messy search-engine result pages, it fetches relevant pages, summarizes them, and returns structured results with source citations that LLMs can consume directly.

• Your agent needs real-time facts rather than stale training data.
• You want search results in a format models can parse without heavy post-processing.
• You need source citations for research, fact-checking, or grounded generation.
• You are building research agents, news monitors, or documentation assistants.

• LLM-ready output. Returns summarized results with URLs and snippets formatted for agent consumption.
• Depth control. Choose how many sources to retrieve and how deeply to scrape each page.
• Include raw content. Get full page content for advanced RAG or direct summarization.
• Simple API. One call with a query and parameters; no complex parsing layer needed.
• Good free tier. Enough for prototypes and low-volume agents.

• Not a general search engine. Optimized for research-style queries; shopping, local, or image search are weaker.
• Rate and accuracy limits. Like any search tool, it can miss or return stale sources.
• Usage pricing at scale. Production research agents can burn through credits quickly.
• Does not replace domain APIs. For structured data like stock prices or weather, use dedicated APIs first.

• Free tier covers a generous number of searches per month.
• Paid plans start around $100/month and scale by request volume and depth.
• Heavy multi-agent research workloads may need higher tiers.

Agents that reason over current web content: market research, competitive analysis, news summarization, and grounded Q&A. Pairs naturally with Firecrawl for deeper page extraction and Pinecone for storing embeddings.

• LangChain / LlamaIndex search tool definitions.
• Firecrawl for full-page extraction after Tavily identifies the right URLs.
• OpenClaw / Hermes research skills via the Tavily MCP or SDK.