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An open-source AI data assistant that connects to your data, writes SQL and code, runs skills in sandboxed environments, and turns analysis into reports, insights, and action.
What is DB-GPT?
DB-GPT is an open-source agentic AI data assistant for the next generation of AI + Data products.
It helps users and teams:
connect to databases, CSV / Excel files, warehouses, and knowledge bases
ask questions in natural language and let AI write SQL autonomously
run Python- and code-driven analysis workflows
load and execute reusable skills for domain-specific tasks
generate charts, dashboards, HTML reports, and analysis summaries
execute tasks safely in sandboxed environments
DB-GPT is also a platform for building AI-native data agents, workflows, and applications with agents, AWEL, RAG, and multi-model support.
Why DB-GPT?
1. Agentic data analysis
Plan tasks, break work into steps, call tools, and complete analysis workflows end to end.
2. Autonomous SQL + code execution
Generate SQL and code to query data, clean datasets, compute metrics, and produce outputs.
3. Multi-source data access
Work across structured and unstructured sources, including databases, spreadsheets, documents, and knowledge bases.
4. Skills-driven extensibility
Package domain knowledge, analysis methods, and execution workflows into reusable skills.
5. Sandboxed execution
Run code and tools in isolated environments for safer, more reliable analysis.
What you can do with DB-GPT
Analyze CSV / Excel files and generate visual reports
Connect to databases and produce profiling reports
Ask business questions in natural language and let AI write SQL automatically
Perform financial report analysis with code, charts, and narrative summaries
Create and reuse SQL analysis skills and domain workflows
Combine code, SQL, retrieval, and tools in a single agentic workflow
Build next-generation AI + Data assistants for your team or product
Product Workflow
Explore data
Connect files, databases, and knowledge bases in one workspace.
Plan and execute
Let AI reason through the task, write SQL and code, and execute step by step.
Use skills
Load reusable skills for repeatable business analysis workflows.
Generate reports
Produce charts, dashboards, HTML reports, and decision-ready outputs.
Quick Start
Get DB-GPT running in minutes with the one-line installer (macOS & Linux):
Install DB-GPT from PyPI and start it with a single command — no source checkout required.
Prerequisites: Python 3.10+ and uv (recommended) or pip.
1. Install
# Recommended: use uv
uv pip install dbgpt-app
# Or with pip
pip install dbgpt-app
The default installation includes the core framework (CLI, FastAPI, Agent), OpenAI-compatible LLM support, DashScope / Tongyi support, RAG document parsing, and ChromaDB vector store.
2. Start
dbgpt start
On first run, an interactive setup wizard will guide you through choosing an LLM provider and entering your API key. Once complete, the web server starts automatically.
If you want to understand the overall architecture of DB-GPT, please cite Paper and Paper
If you want to learn about using DB-GPT for Agent development, please cite the Paper
@article{xue2023dbgpt,
title={DB-GPT: Empowering Database Interactions with Private Large Language Models},
author={Siqiao Xue and Caigao Jiang and Wenhui Shi and Fangyin Cheng and Keting Chen and Hongjun Yang and Zhiping Zhang and Jianshan He and Hongyang Zhang and Ganglin Wei and Wang Zhao and Fan Zhou and Danrui Qi and Hong Yi and Shaodong Liu and Faqiang Chen},
year={2023},
journal={arXiv preprint arXiv:2312.17449},
url={https://arxiv.org/abs/2312.17449}
}
@misc{huang2024romasrolebasedmultiagentdatabase,
title={ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning},
author={Yi Huang and Fangyin Cheng and Fan Zhou and Jiahui Li and Jian Gong and Hongjun Yang and Zhidong Fan and Caigao Jiang and Siqiao Xue and Faqiang Chen},
year={2024},
eprint={2412.13520},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2412.13520},
}
@inproceedings{xue2024demonstration,
title={Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models},
author={Siqiao Xue and Danrui Qi and Caigao Jiang and Wenhui Shi and Fangyin Cheng and Keting Chen and Hongjun Yang and Zhiping Zhang and Jianshan He and Hongyang Zhang and Ganglin Wei and Wang Zhao and Fan Zhou and Hong Yi and Shaodong Liu and Hongjun Yang and Faqiang Chen},
year={2024},
booktitle = "Proceedings of the VLDB Endowment",
url={https://arxiv.org/abs/2404.10209}
}
Contact Information
Thanks to everyone who has contributed to DB-GPT! Your ideas, code, comments, and even sharing them at events and on social platforms can make DB-GPT better.
We are working on building a community, if you have any ideas for building the community, feel free to contact us.
Github Issues ⭐️：For questions about using GB-DPT, see the CONTRIBUTING.
