DB-GPT
Open-source agentic AI data assistant that writes SQL, runs code in a sandbox, and chats with your databases self-hosted
Pick DB-GPT when your data can't leave your infrastructure and you want agents, SQL generation, and RAG in one framework rather than a pile of libraries. AWEL and the Sandbox are the real differentiators: workflow-as-code plus isolated code execution. The tradeoff is genuine ops work — you supply the app server, vector database, and model runtime.
Verified 1h ago · liveness 80/100 · cite: rightaichoice.com/tools/db-gpt
- Platform and data-engineering teams building a custom internal data assistant
- Organizations that require self-hosted, on-prem data AI for compliance
- Developers who want SQL and code generation under their own control
- Data scientists combining RAG knowledge bases with database querying
- Non-technical users who want a managed SaaS they can log straight into
- Teams that need a vendor SLA behind their data assistant
- Analysts unwilling to run their own app server, vector DB, and LLM runtime
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Skip DB-GPT if you want a managed data assistant a non-engineer can log into tomorrow, or if nobody on your team will own a vector database, app server, and model runtime.
The software is free, but you pay for the infrastructure: an application server, a vector database, and an LLM runtime all have to be provisioned and run before you get a first answer.
DB-GPT costs $0/mo for the full framework — there is no license fee at any scale. Your real spend is infrastructure (app server, vector DB, model runtime) plus engineering time. That makes it cheap for a platform team with existing ops capacity and unexpectedly expensive for a small team without one, where a hosted text-to-SQL copilot's per-seat fee can be the cheaper path once you price in the on-call.
In short
DB-GPT — Open-source agentic AI data assistant that writes SQL, runs code in a sandbox, and chats with your databases self-hosted. Best for Platform and data-engineering teams building a custom internal data assistant, Organizations that require self-hosted, on-prem data AI for compliance, Developers who want SQL and code generation under their own control. Free to use.
No head-to-head comparison for DB-GPT yet — the alternatives below are the closest matches we list.
What's new in DB-GPT
Checked 4 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: October 2026
How we score →Key Features
- Autonomous AI agents for multi-step data tasks
- AWEL agentic workflow expression language for orchestration
- Retrieval-Augmented Generation with knowledge bases you build
- SMMF service-oriented multi-model management framework
- Qwen3 dense and MoE model support
- Thinking/non-thinking mode switching
- Multilingual handling for non-English schemas and questions
- Reusable Skills packages for domain-specific tasks
- DataSources connectors for databases, warehouses, and data platforms
- Sandboxed environments for isolated code execution
- Chat with Data natural-language database querying
- Data Analysis over CSV, Excel, and databases
- Visual AWEL Flows builder for agentic workflows
- CLI quick start and one-line installer
- Docker deployment and source build for self-hosting
About DB-GPT
DB-GPT is an open-source, self-hosted agentic AI data assistant for teams that need natural-language access to their own databases without shipping data to a SaaS vendor. You point it at databases, warehouses, and data platforms through DataSources connectors, then query them in plain English via Chat with Data or run AI-powered Data Analysis over CSV, Excel, and database tables. The architecture rests on four concepts the docs spell out: Agents for autonomous multi-step data tasks, AWEL (Agentic Workflow Expression Language) for orchestrating them, RAG for grounding answers in Knowledge bases you build, and SMMF for swapping model backends. On top sit the ready-made applications — Chat with Data, Data Analysis, and a visual AWEL Flows builder for wiring agentic workflows without hand-coding them. A Sandbox component runs generated code in an isolated environment before it touches production, and reusable Skills packages carry domain-specific tasks. Deployment is deliberately developer-first: a one-line installer, a CLI quick start, Docker for isolated setup, or a full source build. Docs are versioned (v0.8.1 and dev at time of writing). The April 2025 release added Qwen3 dense and MoE model support, bringing thinking/non-thinking mode switching and multilingual handling for non-English schemas and questions. There is no license cost, which puts DB-GPT in the self-hosted camp next to assembling LangChain plus a BI layer, or buying a hosted text-to-SQL copilot. It bundles the data-specific pieces — connectors, Skills, Knowledge bases, Sandbox — rather than leaving you to glue them together. Engineering-led teams that want to own the stack should start here.
Behind the Verdict
The case for DB-GPT is ownership. If a compliance rule or a security team says your schemas never leave the VPC, a hosted text-to-SQL copilot is off the table. DB-GPT is the open-source option that already bundles the data-specific plumbing — DataSources connectors, a Sandbox for generated code, Knowledge bases for RAG, and Skills packages — instead of leaving you to assemble each piece around a raw LLM API. We'd reach for it when a platform or data-engineering team wants to build a custom internal data assistant and expects to maintain it. AWEL matters here: you get agentic workflows as code you can version and review, and the visual AWEL Flows builder lets less code-heavy colleagues prototype against the same engine. Pass if nobody on the team wants to run an app server, a vector database, and a model runtime. That's the honest cost — the license is free, the operations are not. Teams expecting a vendor SLA behind their data assistant should look at a managed copilot instead. Qwen3 support (added April 2025) with thinking/non-thinking switching is worth flagging for anyone whose schemas or analyst questions aren't in English; multilingual handling was a real gap for text-to-SQL tools. If you're standardizing on a different model family, SMMF is the abstraction that keeps backends swappable. In practice, treat the Sandbox as the safety net it is: generated SQL and code should run there before it hits production. Budget setup time for connector configuration and knowledge-base building — the first working query is quick, a trustworthy assistant takes iteration. Compare against LangChain-plus-BI if you want maximum flexibility, or a hosted copilot if you'd rather buy a seat than run a stack.
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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 clone the repo, run the one-line installer or Docker deployment, connect a MySQL DataSource, and build a knowledge base for RAG so answers are grounded in your schema docs.
Outcome: A working Chat with Data instance on your own infrastructure where a domain team asks questions in natural language instead of filing SQL tickets.
You drop a CSV and an Excel workbook into Data Analysis, then wire a multi-step AWEL workflow that plans, writes SQL, and validates the result in the Sandbox before output.
Outcome: Repeatable analysis pipelines that run agentically and are isolated from production, with the orchestration captured as code rather than a one-off notebook.
You register Qwen3 dense or MoE models through SMMF and switch thinking versus non-thinking mode per task, with multilingual handling for non-English schemas.
Outcome: One data assistant that serves English and non-English teams and trades reasoning depth against latency per query without changing the app.
Use Cases
- Give a domain team natural-language access to a production MySQL database without building a BI tool from scratch.
- Build a data-analysis agent pipeline that plans, writes SQL, and validates output autonomously.
- Deploy an on-prem data assistant for a regulated industry where data cannot leave your network.
- Wire a Qwen3 model backend into an internal data Q&A tool with thinking/non-thinking control.
- Chain RAG over internal documentation with live database queries in one AWEL workflow.
Models Under the Hood
as of 2026-10-16
Limitations
- Self-hosting is real work: you provision and run a vector database, the application server, and an LLM runtime before you get anything useful, and sandboxed code execution assumes Docker is configured.
- Natural-language-to-SQL accuracy varies with schema quality — expect prompt and metadata engineering on complex or poorly documented datasets.
- Documentation and community discussion skew toward Chinese, so English-language worked examples for edge cases are thinner.
- This is a framework, not a finished managed product: you own uptime, upgrades, and security hardening.
as of 2026-10-18
Verification history
We have re-verified DB-GPT 22 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-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — 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-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 22 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
Ideal for
Platform and data engineering teams with existing ops capacity who need a self-hosted data assistant they fully control
What this tier adds
Starting tier — the full framework at no license cost: agents, AWEL, RAG, SMMF, DataSources connectors, Sandboxed execution, and CLI/Docker deployment
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 costs $0/mo for the full framework — there is no license fee at any scale. Your real spend is infrastructure (app server, vector DB, model runtime) plus engineering time. That makes it cheap for a platform team with existing ops capacity and unexpectedly expensive for a small team without one, where a hosted text-to-SQL copilot's per-seat fee can be the cheaper path once you price in the on-call.
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.
Engineers with Docker experience: a few hours to a working Chat with Data instance, longer if a vector database and model runtime aren't already available. Teams installing from source or tuning RAG over messy schemas should budget days. Non-technical users: this is not a same-day deployment.
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 a hosted text-to-SQL copilot: stand up DB-GPT, reconnect your databases through DataSources, and rebuild the schema metadata you relied on.
- →From LangChain plus a BI layer: replace the hand-wired chain with an AWEL workflow and move document grounding into Knowledge bases.
- →From ad-hoc notebooks: port recurring analyses into Data Analysis and AWEL Flows so they run on a schedule rather than by hand.
- ↗To a managed text-to-SQL copilot: export your schema metadata and prompts, then re-point the copilot at the same databases.
- ↗To a general agent framework: reimplement AWEL orchestration in the target framework's graph or chain primitives; the Sandbox and RAG pieces have no direct equivalent.
- ↗To a BI tool with native AI: keep DB-GPT for agentic pipelines and let the BI layer own dashboards for business users.
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
YouTube returned 6 videos for “DB-GPT”, and we withheld 6: 6 could not be judged, because “DB-GPT” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about DB-GPT.
Official links
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Frequently Asked Questions
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