Kysely

SQL query builder for Node.js

Flexible SQL query builder for Node.js supporting PostgreSQL, MySQL, SQLite, Oracle, and MSSQL. Chainable API for constructing queries with parameterized bindings. Features schema builder, migration system, seed files, transaction support, and connection pooling. Foundation for ORMs like Bookshelf.js and Objection.js. One of the most established query builders in the Node.js ecosystem.

ORM for TypeScript and JavaScript

Full-featured ORM for TypeScript and JavaScript supporting Active Record and Data Mapper patterns. Works with PostgreSQL, MySQL, MariaDB, SQLite, Oracle, SQL Server, and MongoDB. Features decorator-based entity definitions, migrations, relations (one-to-one, many-to-many), lazy/eager loading, query builder, transactions, and caching. Supports both Node.js and browser runtimes. 34K+ GitHub stars. Mature but losing mindshare to Prisma and Drizzle for new TypeScript projects.

Promise-based Node.js ORM

Mature, promise-based ORM for Node.js supporting PostgreSQL, MySQL, MariaDB, SQLite, and SQL Server. Features model definitions with validations, eager/lazy loading, transactions, migrations, raw queries, and lifecycle hooks. Supports soft deletes, scopes, and virtual fields. One of the oldest and most battle-tested Node.js ORMs, widely used in enterprise apps though increasingly succeeded by Prisma and Drizzle in new projects.

Edge-native vector database for Workers and AI applications

Cloudflare Vectorize is Cloudflare’s managed vector database for Workers and edge AI applications. It is distinct from the existing Cloudflare Workers tool page: Workers is the compute runtime, while Vectorize is the embedding index and vector-query layer used to add semantic retrieval to Cloudflare-hosted apps.

Serverless vector database with pay-as-you-go API pricing

Upstash Vector is a managed serverless vector database for RAG, semantic search, and embedding lookup. It is separate from the existing Upstash platform record in the aicoolies catalog: this slug covers the Vector product line, not the broader Redis, Kafka, or QStash platform.

Open-source search engine with vector and hybrid retrieval

OpenSearch is an Apache-2.0 distributed search engine with native vector-search support for teams that want BM25, filters, aggregations, and k-NN retrieval in the same search stack. It is distinct from Elasticsearch in the aicoolies catalog: OpenSearch is the AWS-backed open fork with its own docs, plugin path, and serverless deployment options.

AI data runtime for multimodal datasets and vector search

Deep Lake is an open-source AI data runtime from Activeloop for storing, versioning, and querying multimodal data and embeddings. It fits teams building RAG, training, evaluation, or dataset-heavy agent workflows that need a bridge between vector search, structured metadata, and large image, text, audio, or video collections.

AI-native state store with hybrid vector and full-text search

SeekDB is an open-source AI-native state store from the OceanBase ecosystem that combines MySQL-compatible data access with hybrid vector and full-text retrieval. It targets agent and AI application teams that need embedded or server deployment, copy-on-write style sandboxes, and searchable state without gluing together several separate storage layers.

DiskANN-powered vector search extension for PostgreSQL

pgvectorscale is an open-source PostgreSQL extension from Timescale that complements pgvector with DiskANN-based approximate vector search. It is useful for teams that want faster embedding retrieval while keeping vectors, filters, and application data inside the Postgres ecosystem instead of adopting a separate hosted vector database.