Report card

Do AI agents pick Elasticsearch?

How often agents choose Elasticsearch when a developer needs search — measured across Claude, GPT, Gemini.

Elasticsearch

#3 of 5 search
8%Pick Rate
95% CI 5%14%

When developers ask an AI agent for search, Elasticsearch is picked 8% of the time — ranking #3 of 5 measured across Claude, GPT, Gemini.

Picked 8% Named, not picked 56% Never surfaced 36%

Beaten by Meilisearch (17%), Algolia (14%).

Awareness

2.2M package downloads / week

Known but not preferred — widely used, but agents pick competitors. That's a preference problem, not awareness.

25 docs in FineWeb (10BT sample) · 2.0K URLs in Common Crawl

Present in the raw crawl and the filtered corpus models train on — solid corpus footprint.

AdoptionDid the pick convert to installs? Public Adoption = npm downloads of your packages. The proof half of the funnel.

8.0M installs / month ▼ 10% MoM

Installs are declining. Worth pairing with the Pick Rate trend to find the leak.

Get the full report

The per-model and per-surface breakdown for Elasticsearch — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.

About Elasticsearch

A distributed search and analytics engine built on Apache Lucene.

Elasticsearch stores, searches, and analyzes large volumes of data in near real time. It supports full-text search, structured queries, vector search, and aggregations, making it used by developers building search features, log analysis pipelines, and observability platforms. It is the core engine behind the Elastic Stack, often paired with Kibana for visualization and Logstash or Beats for data ingestion.

Where to start

Set up an Elasticsearch cluster (locally via Docker or through Elastic Cloud), then use the official Node.js client to index documents and run queries against your data.

Install

Links and summary verified from public sources.

The rest of the Search ranking

← Full Search leaderboard · How this is measured