drizzle-team/drizzle-orm

ORM

View on GitHub ↗Jump to charts ↓

Summary Information

Updated 8 minutes ago
Added to GitGenius on September 1st, 2026
Created on June 24th, 2021
Open Issues & Pull Requests: 2,011 (+0)
GitHub issues: Enabled
Number of forks: 1,588
Total Stargazers: 35,708 (+1)
Total Subscribers: 94 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 10.7 days
Mean response time: 78.0 days
90th percentile: 261.7 days
Tracked items: 2,488

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 98% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "bug" is answered fastest, typically in about 8 days, while "db/mysql" waits about 7 weeks. 51% of tracked open issues have had no activity in three months. Only 2% of issues opened in the past year have been closed.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 1,369
New in 7 days: 18
Closed in 7 days: 23
Avg open age: 575 days
Stale 30+ days: 1,144
Stale 90+ days: 880

Recent activity

Opened in 7 days: 12
Closed in 7 days: 20
Comments in 7 days: 2
Events in 7 days: 3

Top labels

  • bug (1,728)
  • enhancement (750)
  • priority (732)
  • drizzle/kit (668)
  • bug/fixed-in-beta (434)
  • db/postgres (297)
  • qb/crud (236)
  • db/sqlite (158)

Detailed Description

Drizzle ORM is a TypeScript ORM that provides type-safe database access with a focus on SQL-first development.

The tool addresses the need for database interaction that preserves SQL's power while adding TypeScript's type safety. Rather than abstracting SQL away, Drizzle keeps developers close to the underlying queries. It generates types from your database schema and validates queries at compile time, catching errors before runtime. The approach combines schema definition, query building, and type inference so that the database layer remains explicit and auditable.

Drizzle suits teams that value SQL literacy and want to avoid the performance surprises that can come from heavy abstraction layers. It works across multiple databases including PostgreSQL, MySQL, and SQLite, making it viable for projects with different persistence requirements. The tool is particularly relevant for applications where query performance matters and developers need visibility into what SQL actually executes. If your team prefers writing queries that look like SQL rather than chaining method calls, or if you need fine-grained control over query structure, this approach aligns with that philosophy.

The project shows sustained development activity with regular updates across its codebase. The maintainers actively address issues and incorporate user feedback into the tool's evolution. Pull requests receive timely review and integration. The project maintains comprehensive documentation and examples that reflect current functionality. Development remains focused on expanding database support and improving the developer experience around type safety and query building.