drizzle-orm: Build Type-Safe SQL Applications

drizzle-orm: Build Type-Safe SQL Applications

Summary

Drizzle ORM is a TypeScript-first library for declaring database schemas and writing typed SQL-like or relational queries. It supports PostgreSQL, MySQL, and SQLite across JavaScript runtimes, with companion tools for migrations and data browsing.

At a glance

Language
TypeScript
License
Apache-2.0
Stars
35.9k
Forks
1.7k
Added to OSRepos
November 25, 2025
Last analyzed
October 3, 2026
View on GitHub

Topics

Click on any tag to explore related repositories

Use at your own risk

OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.

Overview

Drizzle ORM is a lightweight, TypeScript-first layer for working with PostgreSQL, MySQL, and SQLite. It lets application developers define schemas and query databases with type checking while keeping SQL central to the development model.

It is suited to teams that want typed database access across traditional and serverless JavaScript environments without adding a data proxy. Drizzle Kit handles migration workflows, and Drizzle Studio provides a way to browse and manipulate database data.

Key Features

  • Declare database schemas in TypeScript.
  • Build SQL-like queries and relational queries with type safety.
  • Support PostgreSQL, MySQL, and SQLite databases.
  • Run in JavaScript environments including Node.js, Bun, Deno, Cloudflare Workers, and browsers.
  • Use Drizzle Kit to generate SQL migrations or apply schema changes directly.
  • Use Drizzle Studio to browse and manipulate database data.
  • Keep a lightweight, tree-shakeable ORM layer with no dependencies, as described by the project.

Use Cases

  • TypeScript application teams that want compile-time guidance while querying relational databases.
  • Developers building serverless or edge applications that need database access from JavaScript runtimes.
  • Projects using PostgreSQL, MySQL, or SQLite that want schema definitions and query code in one typed workflow.
  • Teams that need a companion CLI to generate or apply database migrations.
  • Developers who want a browser-based way to inspect and manipulate database data through Drizzle Studio.

Project Facts

  • Language: TypeScript
  • License: Apache-2.0
  • Stars: 35.9k
  • Forks: 1.7k
  • Topics: bunjs, mysql, nodejs, orm, postgres, postgresql, sql, sqlite, turso, typescript
  • Archived: false

Getting Started

Install the ORM package:

npm install drizzle-orm

See the README and documentation for database-specific setup and usage.

Considerations

Drizzle is a typed layer over SQL, so familiarity with relational databases and SQL concepts is useful. Database connectivity and setup depend on the chosen database and runtime. The project describes broad runtime and database support, but users should consult the documentation for the correct driver and configuration for their environment.

Source repository

Open the original repository on GitHub.

17 counted GitHub visits

View on GitHub

Related repositories

Similar repositories that may be relevant next.

OrcaReplay: Time Travel for AI Agents, Debugging and Evaluation

OrcaReplay: Time Travel for AI Agents, Debugging and Evaluation

October 2, 2026

OrcaReplay introduces "time travel" capabilities for AI agents, allowing developers to record, replay, fork, and debug any agent run with any model. It addresses the challenges of AI agent debugging by providing byte-for-byte reproducibility, offline analysis, and the ability to compare different models from specific checkpoints. This tool, built by the OrcaRouter.ai team, enhances observability and control over complex agent behaviors.

Agent DebuggingAI AgentsLLM Agents
OpenMake LLM: Self-Hosted AI Workspace for Local and Open-Weight LLMs

OpenMake LLM: Self-Hosted AI Workspace for Local and Open-Weight LLMs

October 1, 2026

OpenMake LLM is an open-source, self-hosted AI workspace for local and open-weight LLMs. It coordinates specialized models, autonomous agents, and tools for deep research and artifact generation. This platform supports vLLM, LiteLLM, and BYOK providers, offering a robust environment for managing AI workloads.

AI AgentsAI WorkspaceSelf Hosted AI
ZenNotes: Keyboard-First Markdown Notes with Vim, Diagrams, and MCP Integration

ZenNotes: Keyboard-First Markdown Notes with Vim, Diagrams, and MCP Integration

October 1, 2026

ZenNotes is a versatile, keyboard-first Markdown notes app designed for speed and flexibility. It stores notes as plain Markdown files, offering Vim-friendly editing, diagram support, and integration with MCP tools. Available as a desktop app (Electron) and a self-hosted web app, ZenNotes provides a powerful solution for organizing your thoughts.

ElectronLocal FirstMarkdown
lat.md: A Knowledge Graph for Your Codebase, Written in Markdown

lat.md: A Knowledge Graph for Your Codebase, Written in Markdown

September 26, 2026

lat.md is an innovative tool that transforms your codebase knowledge into an interconnected graph of markdown files. It helps both AI agents and human developers quickly understand project architecture, business logic, and design decisions. By integrating directly into your project, lat.md ensures documentation remains consistent and up-to-date.

TypeScriptDocumentationKnowledge Graph
OS
OSRepos

Analysis and discovery of open source repositories. Find interesting projects and follow their updates.

Monitor your website with YourWebsiteScore

OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of third-party repository code is at your own risk. Always review source code, dependencies, licenses, and security implications before running anything.

© 2025 OSRepos. Built with Nuxt 3 and lots of ❤️