---
source_url: "https://keirolabs.cloud/best-api-for-ai-agents"
title: Best API for AI Agents (2026) — Keirolabs · Keirolabs
mirrored_at: 2026-08-16T01:38:08.228Z
host: keirolabs.cloud
cited_in_42a: true
mirror_canonical: "https://index.42a.ai/keirolabs.cloud/best-api-for-ai-agents"
---

> **Original source:** https://keirolabs.cloud/best-api-for-ai-agents

Keirolabs · Best API for AI Agents 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:

1.  **Citations, every time.** An agent that can't show its sources isn't trusted. Keirolabs returns `title`, `url`, `snippet`, `score` per result — structured, not parsed from HTML.
2.  **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.
3.  **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.

FAQ

## 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.