DB-GPT
Open-source agentic AI data assistant for SQL, code, and reports
DB-GPT is a solid open-source framework for teams that need custom AI data assistants with SQL/code generation and sandbox execution. Qwen3 integration adds multilingual depth and thinking/non-thinking mode switching. 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 10d ago · liveness 80/100 · 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 need a managed, zero-ops SaaS data assistant or lack the DevOps resources to self-host and maintain the infrastructure.
Self-hosting requires significant DevOps effort: you must provision and maintain a vector DB, application server, and LLM runtime, which can add infrastructure and maintenance costs.
DB-GPT is free and open-source, making it cost-effective for teams comfortable with self-hosting. It's noticeably cheaper than managed SaaS alternatives like Databricks SQL Assistant or Snowflake Cortex, which charge per credit or seat. However, you trade off the convenience of managed infrastructure for the cost savings.
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 10 days agoAcross the latest 1 update: 1 feature update.
What people actually say about DB-GPT — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
20 mentions across 4 sources (Hacker News, YouTube, Product Hunt, GitHub) · researched Aug 19, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Open-source and free with no licensing costs
- +Extensible architecture via AWEL and visual flows
- +Supports multiple LLMs including Qwen3 with mode switching
- +Integrates RAG for private knowledge base queries
- +Natural language SQL generation for databases
- −Installation errors and missing file issues reported
- −Text2SQL model setup is difficult for beginners
- −UI and documentation coverage is lacking
- −High number of open issues implies instability
- −Requires intermediate-to-advanced technical skills
- • Infrastructure costs for hosting models and databases
- • Time investment for setup and maintenance
- • Potential need for paid support or consulting
Viability Score
How well maintained and how widely used is DB-GPT? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Autonomous AI agents for complex data tasks
- AWEL agentic workflow expression language
- Retrieval-Augmented Generation (RAG) with knowledge bases
- Service-oriented Multi-model Management Framework (SMMF)
- 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 stands out as an open-source, self-hosted AI data assistant that gives you full control over your data and models. It's built for developers and data professionals who want to deploy AI-powered data workflows within their own infrastructure, particularly in regulated industries where data cannot leave the premises. The core value lies in its agentic architecture: you can build autonomous agents that plan, write SQL, and validate results, all orchestrated with AWEL, a visual workflow builder. The integration with Qwen3 models adds multilingual support and a thinking/non-thinking mode switch, which enhances reasoning capabilities. However, this is not a plug-and-play tool. Self-hosting demands real DevOps effort: you'll need to set up a vector database, an app server, and an LLM runtime, and the sandboxed code execution requires Docker. English documentation is less complete than Chinese, which can slow onboarding for non-Chinese-speaking teams. The NL-to-SQL accuracy varies with schema quality; expect to invest time in prompt engineering for complex datasets. There is no hosted cloud version, so you must manage everything yourself. In short, DB-GPT is a powerful framework for teams that prioritize data sovereignty and are willing to invest in setup and customization, but it's not for non-technical users or those seeking a quick managed solution.
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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.
Needs to allow analysts to query a production PostgreSQL database without writing SQL by hand.
Outcome: Sets up DB-GPT with the database connection and Chat with Data, enabling analysts to ask natural-language questions and get answers with generated SQL, reducing ad-hoc query workload.
Wants to build a reusable data analysis pipeline that can be triggered automatically.
Outcome: Uses AWEL Flows to design a workflow that fetches data, applies analysis skills, and generates a report, automating routine data tasks and saving time.
Needs an AI assistant for data analysis but cannot use external cloud services due to compliance.
Outcome: Deploys DB-GPT on-prem with Docker, connects to the internal data warehouse, and uses sandboxed execution to ensure data never leaves the infrastructure.
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 2026-08-31
Limitations
- 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 2026-08-28
Verification history
We have re-verified DB-GPT 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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 comfortable with self-hosting who want full control over their AI data infrastructure without subscription costs.
What this tier adds
Free entry point with all core features included, no paywall for agents, AWEL, RAG, or SMMF.
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, making it cost-effective for teams comfortable with self-hosting. It's noticeably cheaper than managed SaaS alternatives like Databricks SQL Assistant or Snowflake Cortex, which charge per credit or seat. However, you trade off the convenience of managed infrastructure for the cost savings.
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 a developer familiar with Docker, you can have a basic DB-GPT instance running via the one-line installer in about 30 minutes. However, configuring a production-ready setup with a vector DB, proper model serving, and sandboxed execution may take a few hours.
Switching to or from DB-GPT
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From SQL Chat or other NL-to-SQL tools: Connect your database directly to DB-GPT and use its agentic capabilities for more autonomous workflows.
- ↗To a managed SaaS like Databricks SQL Assistant: Export your AWEL flows and knowledge bases, then manually rebuild them on the SaaS platform.
Integrations
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
Tutorials & Learning
Official links
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Frequently Asked Questions
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