Chat2DB

Open source alternative to DataGrip, TablePlus, Navicat and DbVisualizer

28.3K stars
Apache-2.0 with additional commercial restrictions (LicenseRef-Chat2DB)
Active this week
Last commit 2 days ago

Last verified Oct 21, 2026

Query 40+ databases with natural language or SQL using a self-hosted, cross-platform client that connects to your own AI model and keeps data local.

Visit website GitHub28.3K Deploy on Hostinger

Read the full breakdownWho it is for, how it works, trade-offs, the quick start and the FAQ

who it's for

Who Chat2DB is for#

Developers and DBAs working across multiple database engines

Chat2DB replaces the need to maintain separate database clients for different engines. Connect MySQL, PostgreSQL, ClickHouse, MongoDB, and other databases from one interface and use the AI assistant to write correct queries in unfamiliar SQL dialects.

Skip if:

If you work with a single database type and have an established workflow with a dedicated client like psql or mysql-cli, adding Chat2DB introduces a heavier GUI without clear gains.

Teams with data residency or compliance requirements

The local-first processing model means schema data and query results stay within your network. Pointing the AI assistant at a locally hosted model runs the entire text-to-SQL stack without any external network calls. Suitable for environments where database schema cannot leave the premises.

Skip if:

Not a fit for multi-user team environments that need access controls enforced at the client layer. The Community edition has no user-level permissions; that requires the commercial Enterprise tier.

Data analysts without deep SQL expertise

The text-to-SQL feature converts plain English questions into executable queries, making database access practical for analysts who understand the data model but not SQL syntax. The BI dashboard turns query results into charts without requiring a separate visualization tool.

Skip if:

Skip if your analysts need to share live dashboards with non-technical stakeholders or require scheduled reports. Community edition dashboards are local and not externally shareable without the commercial tier.

Engineering teams building LLM agent pipelines with database access

The open-source CLI includes MCP support, letting AI agent frameworks with MCP client capabilities invoke Chat2DB database tools as part of structured agentic workflows. The local-first design avoids routing live database schemas through cloud intermediaries.

Skip if:

If your agent infrastructure runs entirely on managed cloud services, the local-first Docker deployment model may add operational complexity. Evaluate whether the Docker path fits your pipeline architecture before building.

the problem

The problem it solves#

Database client tooling for professional use is dominated by paid software. DataGrip charges per-user subscription fees; Navicat and TablePlus require one-time or perpetual licenses; DbVisualizer limits free-tier features. Teams connecting to multiple database engines typically pay for several tools or settle for clients that handle only one engine well.

The harder problem is AI query assistance: commercial clients that offer AI-generated SQL send your database schema to their own hosted services. For teams with sensitive data or compliance requirements, this means either accepting cloud exposure or going without AI help. Getting an AI SQL generator that uses your own LLM of choice, keeps the schema local, and works across dozens of database engines is not solved by any major paid client today.

how Chat2DB solves it

How it solves it#

40+ database connections

Connects to MySQL, PostgreSQL, Oracle, SQL Server, ClickHouse, MongoDB, Redis, SQLite, MariaDB, TiDB, Hive, DB2, Snowflake, BigQuery, Elasticsearch, and more. New JDBC databases can be added through configuration without code changes, so the list extends as needed without a client update.

Bring-your-own-model AI text-to-SQL

Type a question in natural language and your configured AI model generates the SQL query. The AI layer also explains and optimizes existing queries. You supply your own model, so your API key and model choice stay under your control and nothing is routed through Chat2DB's servers.

Full SQL workspace

Includes syntax completion, query formatting, execution with result display, saved SQL snippets, and execution history. Multiple tabs handle parallel queries across different connections. The editor targets professional SQL workflows, not just a basic query console.

Visual data management with ER diagrams

Browse table metadata, edit data in place, and manage DDL and DML operations through a GUI without writing SQL. ER diagrams are generated from any connected database, giving a visual overview of schema relationships useful for onboarding and documentation.

Data import, export, and migration

Import and export CSV, XLSX, XLS, and SQL files; the export path handles millions of rows. Schema synchronization and data migration move structures and data between different database instances, covering cross-engine migrations with the same tool used for daily queries.

MCP-enabled CLI

The open-source Chat2DB CLI includes Model Context Protocol support, letting AI agents with MCP client capabilities call Chat2DB database tools as part of agentic workflows. Useful for teams building LLM-powered data pipelines where the agent needs to query production databases through a structured tool interface.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Local-first data privacyDatabase query processing and result data stay on your machine. The AI integration sends only schema metadata (table and column names) to your configured AI model to generate SQL; actual query results never leave your local environment. For teams with compliance requirements around data residency, this is a concrete difference from cloud-hosted database tools.
28,000+ stars with active developmentWith 28,315 GitHub stars and 3,054 forks, Chat2DB is among the most widely adopted open source database clients. The repo received its most recent push on October 14, 2026, indicating ongoing active maintenance. The contributor community and issue tracker provide a channel for reporting bugs and requesting new database support.
Cross-platform desktop and DockerAvailable as a native desktop installer for Windows, macOS, and Linux with no prerequisites beyond the installer. The Docker path serves headless or server deployments with persistent data stored in a local volume. Both modes share the same feature set.
No cloud account or vendor dependencyThe Community edition requires no account, no phone-home registration, and no dependency on Chat2DB's own servers for core database operations. The bring-your-own-model design means you are not locked into a specific AI provider and can switch models without changing the client.

Trade-offs

-Source-available license restricts SaaS and resaleThe Community edition (v5.3.0 and later) uses a source-available license based on Apache 2.0 with additional restrictions. Internal self-hosting is permitted, including commercial internal use. Offering Chat2DB as a managed service, embedding it in a product for external users, or distributing object-form binaries to independent parties requires written authorization. Teams building on Chat2DB for external deployment should review the license before building.
-Community edition is single-user onlyThe Community edition has no user accounts, role-based access controls, or authorization boundaries between users. The README explicitly states the HTTP service should stay bound to localhost and not be exposed to other network users. Multi-user access, team governance, audit logs, and cloud sync require the commercial Pro or Enterprise tier.
-Docker setup requires an encryption key init stepThe Docker deployment path is not a single docker run command. You first clone the repository to run the bundled init script, generate a per-installation AES-256-GCM key, then start the container with the key file mounted. Losing or replacing this key makes previously stored datasource passwords and AI model API keys unreadable. Desktop mode creates a missing key automatically; headless Docker does not.

versus alternatives

Chat2DB vs alternatives#

Chat2DB vs DataGrip

DataGrip is JetBrains' dedicated database IDE, with deep IDE integration and per-user subscription pricing. Chat2DB is free to self-host for internal use and operates as a standalone client outside any IDE ecosystem.

Feature	Chat2DB Community	DataGrip
License	Source-available (Apache 2.0 + conditions)	Proprietary subscription
Self-hosting	Yes	No
Price	Free for internal use	~$229/user/year
AI query generation	Bring-your-own model	JetBrains AI service
Multi-user access	No (Community)	Yes
Platform	Windows, macOS, Linux, Docker	Windows, macOS, Linux

Chat2DB fits when your team needs a standalone multi-database client at no license cost, wants AI query processing through its own model, or works outside the JetBrains ecosystem. DataGrip is worth it for teams already on IntelliJ IDEA or other JetBrains IDEs who need tight code-to-database integration and a polished IntelliJ-style UX.

Chat2DB vs TablePlus

TablePlus is a native database GUI for macOS and Windows targeting individual developers with a clean interface and one-time license model. Chat2DB adds AI text-to-SQL and broader database coverage at no license cost for internal use; TablePlus requires a paid license for full features.

Feature	Chat2DB Community	TablePlus
License	Source-available (free internal use)	Proprietary (paid)
AI text-to-SQL	Yes (bring-your-own model)	No
ER diagrams	Yes	No
Database coverage	40+	~30+
Platform	Windows, macOS, Linux, Docker	macOS, Windows, iOS
Self-hosting	Yes	No (desktop client, local)

Chat2DB is the better fit when you need AI query generation, Linux support, or a Docker-deployable server path. TablePlus is worth considering for macOS-focused developers who want a fast, polished native interface without the encryption key setup that Chat2DB's Docker path requires.

install · quick start

Quick start#

bash

Self-hosting with Docker requires generating an encryption key first, then starting the container with Docker Compose.
```bash
git clone https://github.com/OtterMind/Chat2DB.git && cd Chat2DB
./script/security/init-community-encryption-key.sh
docker compose --file docker/docker-compose.yml up --detach
```

tech stack · detected from GitHub

What it's built on#

Languages: JavaTypeScript
Frameworks: React
Databases: ClickHouseMongoDBMySQLPostgreSQLSQLite
Cache: Redis

frequently asked

FAQ#

Is Chat2DB free to use?

The Community edition is free to download and self-host. The license (source-available, based on Apache 2.0 with additional conditions) permits internal use for your organization at no cost, including commercial internal operations. Offering it as a managed service to external parties or embedding it in a product for third parties requires written authorization. Commercial Pro and Enterprise tiers add hosted AI, multi-device sync, and team collaboration at a paid price.

Which databases does Chat2DB support?

Chat2DB connects to 40+ databases including MySQL, PostgreSQL, Oracle, SQL Server, ClickHouse, MongoDB, Redis, SQLite, MariaDB, TiDB, Hive, DB2, Snowflake, BigQuery, and Elasticsearch. New JDBC databases can be added through configuration without code changes, so the supported list extends beyond what ships in the default build.

Does Chat2DB send my database data to the cloud?

Database query processing and results stay local. The AI integration sends only schema metadata (table and column names) to your configured AI model to generate SQL; actual data from your tables is never transmitted. If you configure a locally hosted AI model, the entire workflow runs air-gapped with no external network calls.

Is Chat2DB a replacement for DataGrip or Navicat?

For developers and small teams wanting a self-hosted, multi-database client with AI query generation, Chat2DB covers the core use case of DataGrip and Navicat at no license cost. It lacks DataGrip's deep JetBrains IDE integration and Navicat's long-established enterprise feature set. The Community edition also lacks multi-user access controls, which both DataGrip and Navicat support through team licensing.

Can Chat2DB run offline or in an air-gapped environment?

Yes. The desktop and Docker community editions run fully offline for core database operations; they have no dependency on Chat2DB's servers. For the AI text-to-SQL feature to function without internet access, configure a locally hosted AI model as the backend. The desktop installer is available directly from GitHub Releases with no online activation step.

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Find open source alternatives to paid software

Chat2DB vs DataGrip

Feature	Chat2DB Community	DataGrip
License	Source-available (Apache 2.0 + conditions)	Proprietary subscription
Self-hosting	Yes	No
Price	Free for internal use	~$229/user/year
AI query generation	Bring-your-own model	JetBrains AI service
Multi-user access	No (Community)	Yes
Platform	Windows, macOS, Linux, Docker	Windows, macOS, Linux

Chat2DB vs TablePlus

Feature	Chat2DB Community	TablePlus
License	Source-available (free internal use)	Proprietary (paid)
AI text-to-SQL	Yes (bring-your-own model)	No
ER diagrams	Yes	No
Database coverage	40+	~30+
Platform	Windows, macOS, Linux, Docker	macOS, Windows, iOS
Self-hosting	Yes	No (desktop client, local)

Who Chat2DB is for#

Developers and DBAs working across multiple database engines

Skip if:

Teams with data residency or compliance requirements

Skip if:

Data analysts without deep SQL expertise

Skip if:

Engineering teams building LLM agent pipelines with database access

Skip if:

The problem it solves#

How it solves it#

40+ database connections

Bring-your-own-model AI text-to-SQL

Full SQL workspace

Visual data management with ER diagrams

Data import, export, and migration

MCP-enabled CLI

Strengths and trade-offs#

Strengths

Trade-offs

Chat2DB vs alternatives#

Chat2DB vs DataGrip

Chat2DB vs TablePlus

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

AI & Machine Learning

Databases & Storage

Developer Tools

Data & Analytics

Find open source alternatives to paid software

Who Chat2DB is for#

Developers and DBAs working across multiple database engines

Skip if:

Teams with data residency or compliance requirements

Skip if:

Data analysts without deep SQL expertise

Skip if:

Engineering teams building LLM agent pipelines with database access

Skip if:

The problem it solves#

How it solves it#

40+ database connections

Bring-your-own-model AI text-to-SQL

Full SQL workspace

Visual data management with ER diagrams

Data import, export, and migration

MCP-enabled CLI

Strengths and trade-offs#

Strengths

Trade-offs

Chat2DB vs alternatives#

Chat2DB vs DataGrip

Chat2DB vs TablePlus

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

AI & Machine Learning

Databases & Storage

Developer Tools

Data & Analytics

Find open source alternatives to paid software