Do AI agents pick pgvector?
How often agents choose pgvector when a developer needs vector database — measured across Claude, GPT, Gemini.
95% CI 9%–18%
When developers ask an AI agent for vector db, pgvector is picked 13% of the time — ranking #2 of 5 measured across Claude, GPT, Gemini.
0 docs in FineWeb · 1 URLs in Common Crawl
1.5M installs / month ▲ 11% MoM
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The per-model and per-surface breakdown for pgvector — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.
About pgvector
A PostgreSQL extension that adds vector storage and similarity search to your database.
pgvector lets you store vector embeddings directly in PostgreSQL and query them using distance metrics like L2, cosine, and inner product. It supports approximate nearest-neighbor indexes (HNSW and IVFFlat) for fast similarity search at scale. It's aimed at developers building search, recommendation, or AI-backed features who want to keep vector data alongside relational data in Postgres.
Where to start
Install the pgvector extension in your PostgreSQL database, then use the pgvector-node package with your existing Node.js database library (node-postgres, Prisma, Drizzle, Sequelize, and others are supported) to store and query vectors.