DB-GPT
Open-source agentic AI data assistant for SQL, code, and reports
A powerful open-source framework for teams that need custom AI data assistants with SQL/code generation and sandbox execution. Qwen3 integration adds multilingual depth. Self-hosting requires DevOps effort and English docs lag behind Chinese. Best for developers who need on-prem control; skip it if you want a managed SaaS solution.
Verified 17d ago · liveness 87/100 · cite: rightaichoice.com/tools/db-gpt
- Developers building custom data AI assistants with SQL and code generation
- Teams needing open-source agentic workflows for data analysis and reporting
- Organizations wanting sandboxed execution for secure code running
- Data scientists integrating RAG with database knowledge bases
- Non-technical users seeking a fully managed SaaS solution
- Teams requiring polished UI with zero configuration
- Enterprise environments needing commercial support and SLAs
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Skip DB-GPT if you prefer a fully managed SaaS solution or lack the DevOps resources to self-host with Docker and a vector database.
Self-hosting incurs infrastructure costs for GPU LLM runtime, vector DB, and application server.
DB-GPT is free and open-source ($0/mo). The only cost is your own infrastructure. This makes it far cheaper than managed alternatives like Databricks AI Assistant (usage-based) or Julius AI (Pro tier at $20/mo). Best for teams that can handle self-hosting.
In short
DB-GPT — Open-source agentic AI data assistant for SQL, code, and reports. Best for Developers building custom data AI assistants with SQL and code generation, Teams needing open-source agentic workflows for data analysis and reporting, Organizations wanting sandboxed execution for secure code running. Free to use.
What's new in DB-GPT
Checked 18 days agoAcross the latest 1 update: 1 feature update.
Viability Score
How likely is DB-GPT to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Autonomous AI agents for complex data tasks
- AWEL agentic workflow expression language
- Retrieval-Augmented Generation with knowledge bases
- SMMF multi-model management framework
- Qwen3 model integration with thinking/non-thinking switching
- Multi-language support via Qwen3
- Reusable skill packages for domain tasks
- Connect to databases, warehouses, data platforms
- Sandboxed secure code execution environment
- Knowledge base management for RAG
- Chat with Data via natural language
- Data analysis of CSV, Excel, and databases
- Visual AWEL Flows builder
- One-line installer and CLI quick start
- Source code and Docker deployment
About DB-GPT
DB-GPT is an open-source agentic AI data assistant that connects to your data, writes SQL and code autonomously, runs skills in sandboxed environments, and turns analysis into reports, insights, and action. Designed for developers and data professionals, it enables natural language interaction with databases, AI-powered data analysis workflows, and agentic workflow deployment. Core features include autonomous AI agents, AWEL (Agentic Workflow Expression Language) for orchestration, Retrieval-Augmented Generation (RAG), and the Service-oriented Multi-model Management Framework (SMMF). DB-GPT now supports Qwen3 series models with seamless thinking/non-thinking mode switching and multi-language support. Users can connect to various data sources, build knowledge bases, create reusable skill packages, execute code in secure sandboxes, and use applications like Chat with Data, Data Analysis, and visual AWEL Flows. As a comprehensive open-source alternative to proprietary tools, it offers extensibility and customization for diverse data scenarios.
Behind the Verdict
DB-GPT is one of the more ambitious open-source projects bridging LLMs and databases. Its strength is modularity: you can swap models, define custom skills, and orchestrate multi-agent workflows via AWEL. The sandboxed code execution is a practical security feature many alternatives lack. However, setup isn't trivial—expect to invest time in Docker or source installation, and the English documentation is thinner than the Chinese docs. Qwen3 support adds solid multilingual capability, particularly for teams working with Chinese or other non-English data. We'd reach for DB-GPT when we want full control over an AI data assistant, avoid per-seat SaaS costs, and have the DevOps bandwidth to maintain it. It's less suited for teams that want a polished, zero-config dashboard or need enterprise SLAs. Compared to tools like LangChain or AutoGPT, DB-GPT is more focused on data tasks out of the box. The lack of native cloud warehouse integrations (Snowflake, BigQuery) means you'll need to connect via generic database drivers.
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Real-world workflow fit
Concrete scenarios for the personas DB-GPT actually fits — and what changes day-one when you adopt it.
You need to give your sales team natural-language access to a production PostgreSQL database to answer ad-hoc questions about pipeline data.
Outcome: You deploy DB-GPT via Docker, connect it to your PostgreSQL instance, and build a simple Chat with Data app. The sales team queries 'How many deals closed this month?' and gets an instant answer with SQL validation.
You regularly analyze CSV exports and Excel reports. You want to automate the process of generating summary statistics and visualizations.
Outcome: Upload a CSV to DB-GPT's Data Analysis app, ask 'Show me monthly sales trends by region,' and receive a chart with code you can audit. The sandbox ensures safe execution.
Your organization needs an on-prem AI assistant for a finance department with strict data governance rules.
Outcome: You set up DB-GPT with a local Qwen3 model, connect to internal databases, and configure sandboxed code execution. The team gets AI-powered analytics without data leaving the premises.
Use Cases
- Give a domain team natural-language access to a production MySQL database without writing a BI tool from scratch.
- Build a data-analysis agent pipeline that plans, writes SQL, and validates results autonomously.
- Deploy an on-prem AI data assistant for a regulated industry (finance, healthcare, government).
- Integrate a custom Chinese LLM (Qwen, ChatGLM) as the backbone of an internal data Q&A tool.
Models Under the Hood
as of 2026-07-14
Limitations
- Documentation and community are bilingual (Chinese / English) — English docs have improved but trail Chinese ones.
- Self-hosting demands real DevOps work (vector DB, app server, LLM runtime).
- NL-to-SQL accuracy varies by schema quality; expect prompt-engineering work for complex datasets.
- No hosted cloud version available.
- Sandboxed code execution requires Docker setup.
as of 2026-06-24
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DB-GPT tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Developers and teams who want free, self-hosted AI data assistant with full control, willing to manage their own infrastructure.
What this tier adds
Starting tier: free, open-source with full framework, Web UI, all connectors, AWEL flows, RAG support, and sandboxed execution.
Where the pricing makes sense
The company stage and team size where DB-GPT's pricing actually pencils out — and where peers do it cheaper.
DB-GPT is free and open-source ($0/mo). The only cost is your own infrastructure. This makes it far cheaper than managed alternatives like Databricks AI Assistant (usage-based) or Julius AI (Pro tier at $20/mo). Best for teams that can handle self-hosting.
Setup time & first value
How long it actually takes to get something useful out of DB-GPT — broken out by persona, not the marketing-page minute.
For developers: 10-15 minutes with the one-line installer on a Linux machine with Docker. Adding a database connection takes another 5 minutes. Sandbox setup requires Docker and adds 10-15 minutes. Total: 30-45 minutes to first query for a Docker-savvy developer. Non-Docker users: 1-2 hours.
Resources & Guides
- Quickstartdocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Resourcedocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Tutorialdocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Guidedocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Resourcedocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Resourcedocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Resourcedocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Learndocs.dbgpt.cn
DB-GPT
DB-GPT: Open-Source Agentic AI Data Assistant - Revolutionizing Database Interactions with Private LLM Technology
- Resourcedocs.dbgpt.cn
Blog
Blog
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