Seltz is a web search API built for AI agents. We wrote the crawler, the index, and the retrieval models ourselves, in Rust, by a team that's spent years building web search at scale. In our tests, queries come back in under 200ms.
Efficiency was the first design principle. Search sits on the critical path: agents can't generate their first tokens or kick off the next tool call until results come back. When you run tens or hundreds of queries in parallel, every millisecond of tail latency compounds.
Most search APIs for agents are wrappers around Google or Bing. If your agent already has a Google tool, a second call to a Google-wrapped API returns the same ten documents. We run our own independent index, so you get different results and different rankings.
Coverage starts with US news. More verticals are coming.
There's a free tier with $100 in credits at the link. I'd love feedback from anyone building agents: how does it compare to what you're using, and where does it fall over?
amallia•1h ago
We benchmark against 10 other search APIs on fresh news queries. The median was around 1.2s; we came in around 166ms and scored highest on answer accuracy (89% vs 84% for the next cluster).
Latency matters because agents loop. A 1.2s first call eats the budget for follow-ups — you get one shot at framing the query. At sub-250ms the agent can actually search, read, reformulate, and search again.
Measuring this stuff carefully is something I've been at for a while. My ECIR 2019 paper (linked below) was an exhaustive study of 11 index compression methods across 5 query processing algorithms on standard collections — the codebase became PISA, which a lot of IR folks still use for research. Almost ten years later, the workload has changed completely (agents, not humans), but the benchmarking discipline is the same.
ECIR 2019 paper: https://www.antoniomallia.it/uploads/ECIR19c.pdf
Pisa Engine: https://github.com/pisa-engine/pisa
Full methodology and charts for Seltz: https://seltz.ai/blog/why-we-built-seltz