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.

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.

Plan tasks, break work into steps, call tools, and complete analysis workflows end to end.

Generate SQL and code to query data, clean datasets, compute metrics, and produce outputs.

Work across structured and unstructured sources, including databases, spreadsheets, documents, and knowledge bases.

Package domain knowledge, analysis methods, and execution workflows into reusable skills.

Run code and tools in isolated environments for safer, more reliable analysis.

• 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

Connect files, databases, and knowledge bases in one workspace.

Let AI reason through the task, write SQL and code, and execute step by step.

Load reusable skills for repeatable business analysis workflows.

Produce charts, dashboards, HTML reports, and decision-ready outputs.

Get DB-GPT running in minutes with the one-line installer (macOS & Linux):

curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh | bash

Or specify a profile and API key directly:

curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh \
  | OPENAI_API_KEY=sk-xxx bash -s -- --profile openai

For Kimi 2.5 via Moonshot API:

curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh \
  | MOONSHOT_API_KEY=sk-xxx bash -s -- --profile kimi

For MiniMax via the OpenAI-compatible API:

curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh \
  | MINIMAX_API_KEY=sk-xxx bash -s -- --profile minimax

Already have a local DB-GPT checkout? Reuse it instead of cloning ~/.dbgpt/DB-GPT:

OPENAI_API_KEY=sk-xxx \
  bash scripts/install/install.sh --profile openai --repo-dir "$(pwd)" --yes

Or reuse your local repo with Kimi 2.5:

MOONSHOT_API_KEY=sk-xxx \
  bash scripts/install/install.sh --profile kimi --repo-dir "$(pwd)" --yes

Or reuse your local repo with MiniMax:

MINIMAX_API_KEY=sk-xxx \
  bash scripts/install/install.sh --profile minimax --repo-dir "$(pwd)" --yes

After installation, start the server with the generated profile config:

cd ~/.dbgpt/DB-GPT && uv run dbgpt start webserver --config ~/.dbgpt/configs/<profile>.toml

Then open http://localhost:5670.

Prefer to review the script first?

curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh -o install.sh
less install.sh
bash install.sh --profile openai

For Docker, local GPU models (vLLM, llama.cpp), or manual source-code setup, see the full docs:

• Install
  • Docker
  • Source Code
• Quickstart
• Application
  • Development Guide
  • App Usage
  • AWEL Flow Usage
• Debugging
• Advanced Usage
  • SMMF
  • Finetune
  • AWEL

• task planning
• step-by-step execution
• tool use
• iterative reasoning

• natural language to SQL
• Python-based analysis and transformation
• metric calculation
• chart generation

• relational databases
• CSV / Excel
• documents
• knowledge bases
• mixed-source workflows

• reusable skills
• domain workflows
• agent orchestration
• customizable execution flows

• database profiling reports
• financial analysis reports
• visual reports and dashboards
• summaries and business insights

• sandboxed code execution
• controlled tool use
• reproducible outputs and artifacts

DB-GPT is also a platform for building AI-native data systems.

• AWEL for agentic workflow orchestration
• Agents for autonomous task execution
• RAG for knowledge-enhanced reasoning
• SMMF for multi-model management
• DB-GPT-Hub for Text2SQL and finetuning workflows
• dbgpts for apps, workflows, operators, and templates
• DB-GPT-Plugins for plugin-based extension
• GPT-Vis for visualization protocols

• DB-GPT
• DB-GPT-HUB
• dbgpts

More Information about Text2SQL finetune

• More Supported LLMs

We protect data privacy and execution safety through private model deployment, proxy desensitization, and sandboxed execution mechanisms.

• Datasources

We believe the future of data products goes beyond dashboards.

The next generation of AI + Data products will be:

• agentic
• multi-source
• skill-driven
• sandboxed
• capable of writing SQL and code
• able to turn analysis into reports, decisions, and action

DB-GPT aims to help developers and enterprises build that future.

🌐 AutoDL Image

• To check detailed guidelines for new contributions, please refer how to contribute

The MIT License (MIT)

• disckaimer

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}
}

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.
• Github Discussions ⭐️：Share your experience or unique apps.
• Twitter ⭐️：Please feel free to talk to us.