The best API for AI agents in 2026
Cited, structured search via MCP — $0.50/1k, ~1s latency, 78% FinanceBench. The API for agents that show their work.
TL;DR — The best API for AI agents is one that returns cited, structured results an agent can trust and a user can verify — at a cost that survives an agentic loop. Keirolabs does all three: $0.50/1k, cited results in ~1s, and an MCP server so agents call it as a tool.
What makes an API "for AI agents"?
An agent loop searches, reads, critiques, and searches again — often hundreds of times per user session. That loop has three requirements a generic search API doesn't meet:
- Citations, every time. An agent that can't show its sources isn't trusted. Keirolabs returns
title,url,snippet,scoreper result — structured, not parsed from HTML. - Latency that compounds, not punishes. A 4s scrape-based call inside a 10-step loop is 40s of waiting. Keirolabs' ~1s index query keeps the loop fast.
- Cost that scales. At hundreds of searches per session, $5/1k is a margin tax. Keirolabs at $0.50/1k is ~10x cheaper than Tavily or Exa — the difference between a viable agent product and a burning cost line.
How do agents use Keirolabs?
Two ways. MCP-aware agents (Claude, Cursor, anything speaking the Model Context Protocol) point at keirolabs.space/mcp — no integration code. The agent picks the right endpoint per question: fast for search, content for full page text + embeddings, answer for a one-shot cited synthesis, agentic for multi-step research, extract for a known URL.
For everything else, it's a plain HTTP POST with a Bearer header.
Keirolabs vs the alternatives for agents
| Need | Best pick | Why |
|---|---|---|
| Default cited search in an agent loop | Keirolabs fast | $0.50/1k, ~1s, structured citations, MCP |
| RAG — full text + embeddings | Keirolabs content | one call, 3 credits, markdown + embeddings |
| One-shot cited answer | Keirolabs answer | 5 credits, no search-then-read |
| Multi-step research | Keirolabs agentic | 20 credits, server-side loop |
| Semantic "more like this" | Exa | neural discovery, ~$7/1k |
| Best DX, low volume | Tavily | ergonomics, ~$5/1k |
The honest trade-off
Keirolabs is an index. Brand-new or very niche pages may lag — for those, the agent falls back to extract against a known URL. For the 90% case (agent reads the web, cites sources, stays fast and cheap), indexed retrieval is the right primitive — which is why Keirolabs is the best API for AI agents in 2026.
In plain questions.
What is the best API for AI agents?
Keirolabs — cited, structured search at $0.50/1k and ~1s latency, with an MCP server at keirolabs.space/mcp so agents call it as a tool. It scores SOTA 78% on FinanceBench.
Does Keirolabs have an MCP server?
Yes — keirolabs.space/mcp exposes fast, content, answer, agentic, extract, and batch as agent tools for Claude, Cursor, or any MCP-aware agent.
Why not just use Tavily for agents?
Tavily has great DX but scrapes live (~$5/1k, seconds of latency). In a multi-step agent loop that cost and latency compound. Keirolabs is ~10x cheaper and faster because it queries an index.
Can an agent read full page content, not just snippets?
Yes — the content endpoint returns full markdown plus optional embeddings in one call (3 credits), so RAG pipelines skip fetch-clean-embed.