你电脑里装一堆 Navicat / DBeaver / DataGrip，真的已经过时了。

最近我试了一个新工具：DBX，看起来很“轻”，但行为很“狂”。

15MB 直接打包 50+ 数据库支持 还顺手把 AI 助手塞进去了

最离谱的点是这个：

以前你的工作流是：MySQL 一个客户端， PostgreSQL 一个客户端，Redis 再一个，MongoDB 再一个，ClickHouse 再一个 而DBX 的逻辑很简单粗暴：“为什么不能一个软件全干了？”于是它做了 MySQL / PG / SQLite Redis / MongoDB ClickHouse / SQL Server DuckDB 等等 并且内置 AI（直接帮你写/查 SQL），支持 Docker 自托管，三端通吃：Win / Mac / Linux，体积只有某些客户端插件的零头 一句话总结：它不是在优化数据库管理工具，而是在“砍掉一整堆工具”。

如果一个工具真的能覆盖 80% 数据库场景—— 你还会坚持用“专业单一客户端”吗？

链接： GitHub：https://github.com/t8y2/dbx 官网：https://dbxio.com/cn #源码 #数据库 #开发工具 #程序员 #效率工具

t8y2/dbx

Source: https://github.com/t8y2/dbx

Why DBX?

Features

60+ Databases, One Tool

MySQL, PostgreSQL, SQLite, Redis, MongoDB, DuckDB, ClickHouse, SQL Server, Oracle, Elasticsearch, Qdrant, Milvus, Weaviate, MariaDB, TiDB, OceanBase, openGauss, GaussDB, KWDB, KingBase, Vastbase, GoldenDB, Doris, SelectDB, StarRocks, Manticore Search, Redshift, DM, TDengine, XuguDB, CockroachDB, Access, HighGo, and more. Agent/JDBC-oriented profiles extend DBX to H2, Snowflake, Trino, PrestoSQL, Hive, DB2, Informix, Neo4j, Cassandra, BigQuery, Kylin, SunDB, and custom JDBC connections. New native and agent-driven drivers also cover Databricks, SAP HANA, Teradata, Vertica, Firebird, Exasol, YashanDB, GBase 8a/8s, Databend, RQLite, Turso, InfluxDB, QuestDB, IoTDB, etcd, ZooKeeper, Nacos, IRIS, and more. Message queue admin is also available for Pulsar, Kafka, and RocketMQ. All in a single ~15 MB app. No bundled Chromium.

Query Editor

CodeMirror 6 with SQL syntax highlighting, metadata-aware autocomplete, Cmd+Enter execution, selected SQL execution, SQL formatting, diagnostics, and 9 editor themes. Persistent query history, saved SQL snippets, tab restore, and SQL file execution keep repeat work close at hand.

AI SQL Assistant

Describe what you want in plain language — get SQL back. DBX can explain queries, optimize SQL, fix errors, and run AI-generated SQL through built-in safety checks. Works with Claude, OpenAI, local models, or any OpenAI-compatible endpoint.

Data Grid

Virtual-scrolled table that handles large result sets. Inline editing, SQL preview before save, WHERE / ORDER BY controls, DataGrip-style filters, LIKE / NOT LIKE context filters, sorting, full-text search, pagination, column resize, auto-fit, row numbers, zebra stripes, and full cell details. Export or copy as CSV, JSON, Markdown, XLSX, or INSERT statements.

Schema Tools

• Schema browser — databases, schemas, tables, columns, indexes, foreign keys, triggers, with sidebar search & pin
• Object browser — grouped procedures, functions, views, and source editing where supported
• Table structure editor — reviewable column and index changes for supported engines
• ER diagram — visualize table relationships
• Schema diff — compare structures across connections
• Explain plan — visual query execution plan
• Field lineage — column-level lineage analysis
• Database search — find objects across large schemas

Data Operations

• Table import — CSV, Excel
• Data transfer — migrate between databases
• Database export — full database dump
• Data compare — compare table data and review synchronization output
• SQL file execution — run .sql files directly
• File preview — drag & drop Parquet, CSV, JSON to preview instantly (powered by DuckDB)
• Connection import — bring connection profiles from DBeaver or Navicat

Specialized Browsers

• Redis — key pattern search, batch key operations, command runner, TTL editing, and all data types (String, Hash, List, Set, ZSet, Stream)
• MongoDB — document CRUD with pagination, Atlas & replica set URL connection

Safety & Connectivity

SSH tunnel (key & password) · database and AI proxy settings · auto-reconnect on connection loss · confirmation dialogs for destructive operations · encrypted config export/import · color-coded connections · driver store and optional JDBC plugin

Polished UI

Dark mode with native title bar sync · 9 editor themes · English, 简体中文 & Español · layout preferences · built-in auto-update

AI Agent Integration (MCP)

DBX provides an MCP server that lets AI coding agents query your databases using connections already configured in DBX.

npx @dbx-app/mcp-server

Add to your .mcp.json:

{
  "mcpServers": {
    "dbx": { "command": "npx", "args": ["-y", "@dbx-app/mcp-server"] }
  }
}

Works with Claude Code, Cursor, Windsurf, and any MCP-compatible agent. Supports listing connections, browsing tables, executing SQL, and opening tables directly in DBX’s UI.

DBX also provides a dedicated CLI package for terminal, script, and Codex workflows:

npm install -g @dbx-app/cli
# or via Homebrew
brew tap t8y2/dbx && brew install dbx-cli
dbx connections list --json
dbx query local "select 1" --json

See the MCP server README and CLI README for details.

Install

Download the latest release from the Releases page.

Homebrew (macOS):

brew install --cask dbx

Scoop (Windows):

scoop bucket add dbx https://github.com/t8y2/scoop-bucket
scoop install dbx

WinGet (Windows):

winget install t8y2.dbx

Self-Hosted (Docker)

DBX provides a web version that can be deployed via Docker.

docker run -d --name dbx -p 4224:4224 -v dbx-data:/app/data t8y2/dbx

Or with Docker Compose. A ready-to-use example lives at deploy/docker-compose.yml:

services:
  dbx:
    image: t8y2/dbx
    ports:
      - "4224:4224"
    volumes:
      - dbx-data:/app/data
    restart: unless-stopped

volumes:
  dbx-data:

Open http://localhost:4224 in your browser. Multi-arch images (amd64 / arm64) are available.

To publish DBX under a reverse-proxy context path such as /dbx, set the runtime base path and proxy the same prefix to the container:

environment:
  - DBX_PUBLIC_BASE_PATH=/dbx

When building the frontend yourself with an absolute asset base, set VITE_DBX_BASE_PATH=/dbx/ before pnpm build.

Getting Started

Prerequisites

• Node.js >= 18
• pnpm
• Rust >= 1.77

System Dependencies

macOS:

No additional dependencies required.

Linux (Ubuntu/Debian):

sudo apt-get install -y libwebkit2gtk-4.1-dev libgtk-3-dev libappindicator3-dev librsvg2-dev patchelf libssl-dev

Windows:

No additional dependencies required.

Development

make

make installs root dependencies when needed and starts the local Tauri desktop development environment.

DuckDB compilation takes a while. If you’re not working on DuckDB features, skip it to speed up local builds:

# Fast checks (skip DuckDB)
make cargo-check-fast
make cargo-test-fast

# Tauri dev without DuckDB
make dev-fast

The --no-default-features flag only affects local development. Release builds (pnpm tauri build) always include DuckDB.

Web version:

make dev-web       # frontend
make dev-backend   # backend

Documentation site:

make docs

The official DBX documentation site lives in docs/. If you want to improve the website content or documentation pages, edit the files under docs/ and run make docs to preview the site locally.

JDBC agent driver development projects live in agents/:

cd agents
./gradlew test

Build artifacts from agents/drivers/<db-type>/build/libs/ are picked up by local driver install flows when available.

Build

make package

The installer will be in src-tauri/target/release/bundle/.

Tech Stack

Community

FAQ

Contributors

Star History

License

Apache-2.0