Knex.js

Type-safe SQL query builder for TS

Type-safe SQL query builder for TypeScript that provides auto-completion and compile-time type checking without the abstraction overhead of a traditional ORM. Write queries that map directly to SQL while getting full TypeScript inference from your database schema. Supports PostgreSQL, MySQL, and SQLite with migrations, transactions, and raw SQL escape hatches. Lightweight with zero dependencies. Ideal for developers who want SQL control with TypeScript safety. Growing Knex.js alternative.

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.

High-recall Postgres vector search at billion scale

VectorChord is a Postgres extension from the supervc-stack/VectorChord project that brings high-recall vector search to PostgreSQL. As the spiritual successor to pgvecto.rs, it combines IVF indexes with RaBitQ quantization to deliver Pinecone-class performance at billion-vector scale while keeping all data inside a single Postgres database — no separate vector store, no two-system sync, no rewrites when the workload grows.

AI-native database for hybrid RAG retrieval

Infinity is an AI-native database from InfiniFlow that unifies dense vectors, sparse vectors, tensors, and full-text search in a single engine. Built for retrieval-augmented generation (RAG) at scale, it powers hybrid search workflows where lexical matching, semantic similarity, and reranking all happen against one storage layer instead of four loosely coupled services.

Vector search extension for SQLite that runs anywhere

sqlite-vec is a lightweight vector search extension for SQLite written in pure C with zero dependencies. It brings nearest-neighbor search capabilities directly into SQLite databases, enabling AI applications to store and query embeddings without running a separate vector database. The extension works everywhere SQLite runs including Linux, macOS, Windows, WebAssembly in browsers, and even Raspberry Pi devices. Sponsored by Mozilla Builders, Fly.io, and Turso.

Enterprise RAG framework by Tencent

WeKnora is a Tencent-developed LLM-powered knowledge management and Q&A framework for enterprise document understanding and semantic retrieval. Supports 10+ document formats including PDF, Word, Excel, and images with seamless IM platform integration for WeCom, Feishu, Slack, and Telegram. Offers Quick Q&A mode using RAG pipelines and Intelligent Reasoning mode with ReACT agents for complex multi-step reasoning tasks across organizational knowledge bases.

Declarative multimodal AI data infrastructure

Pixeltable is a declarative data infrastructure for multimodal AI that stores video, audio, images, and documents as first-class column types. Define Python computed columns for inference and transformations, and Pixeltable auto-orchestrates execution with incremental updates. Built-in vector search eliminates the need for separate vector databases while supporting RAG and semantic search workflows.

Fast embeddable vector search engine

USearch is a high-performance vector search engine implementing HNSW algorithms for approximate nearest neighbor queries across C++, Python, JavaScript, Rust, Java, Go, and more. It supports user-defined distance metrics, memory-mapped persistence for datasets larger than RAM, and filtered search with predicates. Used by YugabyteDB and ScyllaDB as their production vector indexing backend.