DB-GPT — Open-source agentic AI data assistant for autonomous SQL, analysis, and reports. Best for Data teams needing an open-source autonomous SQL and analysis assistant, Developers building agentic data workflows with sandboxed code execution, Organizations self-hosting AI to maintain data privacy and control. Free to use.
Data teams needing an open-source autonomous SQL and analysis assistantDevelopers building agentic data workflows with sandboxed code executionOrganizations self-hosting AI to maintain data privacy and control
Not ideal for
Users seeking a fully managed SaaS data assistant with no deployment overheadTeams wanting polished dashboards or visual analytics without codingNon-technical users needing a simple chat interface without setup
A powerful open-source play for teams wanting agentic data workflows with code execution sandboxing. Not for those seeking a polished, SaaS-like UI out of the box.
DB-GPT is a strong contender for AI-native data teams that value open source and customizability. Its key strengths lie in agentic workflows (AWEL) and sandboxed code execution, which are rare in the open-source data assistant space. Choose DB-GPT if you need an autonomous SQL/code generator that can operate on multiple databases and incorporate RAG from your own knowledge bases. It's ideal for developers building internal tools or automation pipelines. Skip it if you want a no-code, plug-and-play chat interface like those offered by commercial tools (e.g., Databricks AI Assistant or Tableau Pulse), as DB-GPT requires deployment and some scripting. Compared to tools like LangChain, DB-GPT is more data-centric with built-in database connectors and a ready-made chat UI. However, documentation can be sparse for advanced scenarios, and community support is still growing. Real-world usage caveats include potential complexity in setting up multi-model frameworks and the need for GPU resources for local models.
Skip DB-GPT if Skip DB-GPT if you need a zero-setup, fully managed AI data assistant with a polished UI and no DevOps involvement.
How likely is DB-GPT to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
40
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About DB-GPT
DB-GPT is an open-source agentic AI data assistant that connects to your databases, enabling natural language queries, autonomous SQL and code generation, and data analysis. It is designed for data professionals, developers, and analysts who need to interact with data through conversational AI, run skills in sandboxed environments, and produce reports, insights, and actions from structured and unstructured sources. Core capabilities include building autonomous AI agents via the Agentic Workflow Expression Language (AWEL), Retrieval-Augmented Generation (RAG) for knowledge-enhanced AI, and a Service-oriented Multi-model Management Framework (SMMF) for managing multiple AI models. The tool offers a chat interface for querying databases, AI-powered data analysis on CSV, Excel, and databases, and visual workflow building for agentic processes. It can be deployed via source code, CLI, or Docker, making it flexible for various environments. Compared to proprietary data assistants, DB-GPT stands out as a fully open-source, self-hosted alternative with strong agentic and sandboxing features.
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Key Features
Connect to databases via DataSources
Natural language SQL generation (Chat with Data)
AI-powered analysis of CSV and Excel files
Build autonomous AI agents with AWEL
Retrieval-Augmented Generation (RAG) from Knowledge
Multi-model management with SMMF
Sandboxed code execution via Sandbox
Reusable skill packages (Skills)
Visual workflow builder for AWEL Flows
One-line installer for quick setup
Source code installation for full control
Docker deployment for isolated environments
Real-world workflow fit
Concrete scenarios for the personas DB-GPT actually fits — and what changes day-one when you adopt it.
Data engineer at a mid-size SaaS company
You need to give your product team natural-language access to the production MySQL database without building a BI layer.
Outcome: Install DB-GPT via one-line installer, configure the MySQL datasource, and deploy a Chat with Data interface. The team can ask questions like 'What was the MRR churn last quarter?' and get SQL-generated answers.
AI researcher building a custom analysis agent
You want an agent that can query a local CSV, run Python analysis, and generate a report — all in a sandbox for safety.
Outcome: Use DB-GPT agents with sandboxed code execution. Upload CSV, write a skill package for analysis, and run via CLI or Web UI. The agent outputs a summary with charts.
Integrate a custom Chinese LLM (Qwen, ChatGLM) as the backbone of an internal data Q&A tool.
Models Under the Hood
Qwen3 (dense and MoE)DeepSeek R1QWQLlama 3.1Qwen2.5ChatGLMGPT-4ClaudeBGE embeddings
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.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
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 willing to self-host and manage infrastructure; anyone wanting full control without per-seat fees.
What this tier adds
Free and open-source — no pricing tiers. All features are available in the community edition.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Self-hosting requires your own infrastructure: vector DB (e.g., Chroma, Milvus), application server, and LLM runtime — costs vary widely.
•LLM API costs if using hosted models like GPT-4 or Claude; open-source models require GPU compute.
•No paid support tier — community support only via GitHub and Discord.
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 — no licensing fees. For teams with existing infrastructure, it's cheaper than proprietary BI tools like Tableau or Power BI with AI add-ons. However, you bear infrastructure costs for self-hosting. Compare with LangChain (also free) but DB-GPT is more specialized for data tasks.
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: one-line installer gets a basic deployment in ~10 minutes. Docker deployment ~20 minutes. Full production setup with vector DB and LLM runtime can take a few hours. Non-developers may need a day to learn the concepts and deploy.
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.
Migrating in
→From LangChain: DB-GPT offers a more data-focused framework with built-in SQL agents and sandboxed execution — you can reuse custom LLM calls.
→From custom scripts: Replace ad-hoc SQL generators with DB-GPT agents and AWEL workflows.
Migrating out
↗To LangChain: If you need a general-purpose agent framework, LangChain has broader ecosystem integrations.
↗To DataChat: For non-technical users, DataChat provides a no-code managed platform.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2025-04-29: DB-GPT now supports Qwen3 series models with thinking/non-thinking mode switching (blog post).
•2025-03-24: DB-GPT v0.7.0 released with MCP protocol support, DeepSeek R1 integration, GraphRAG enhancements, and module restructuring.
•2025-01-15: DB-GPT added support for Meta Llama 3.1 series models.