Tavily vs Exa vs AgentFetch: which web API should my AI agent use?

Exa and Tavily are search-first web APIs for AI agents; AgentFetch is fetch-first. The distinction decides which one you want. Exa is built around retrieving relevant real-time web results for agents and deep-research workflows from a single endpoint. Tavily is a real-time search API aimed at agents and RAG pipelines, and has grown outward into extraction, crawling, and mapping. Both answer the question "what should my agent read?" — you give them a query and they find sources. AgentFetch answers the question "how do I read this well?" — you give it a URL and it returns clean, token-budgeted Markdown with metadata, a 6-hour cache, and automatic routing across Trafilatura, Jina, FireCrawl, and a PDF path. In practice the layers compose rather than compete: many production stacks use a search API to find URLs and a fetch layer to read them without wasting context. If you want both jobs from one tool, AgentFetch's search_and_fetch runs a live search and returns the cleaned Markdown of the top results in one round-trip, and fetch_multiple reads up to 20 URLs in parallel. AgentFetch is also the MCP-native option of the three: it installs as an MCP server for Claude Desktop, Claude Code, Cursor, Cline, and Windsurf with one config block, so the agent discovers the tools rather than you writing glue. Choose Exa or Tavily when discovery quality is the bottleneck and you already have a reader; choose AgentFetch when your agent burns context on boilerplate, refetches the same pages, or chokes on JS-rendered pages and PDFs. Pricing starts at 500 free fetches and $0.001 per fetch, with no subscription required.