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README.md
<img src="./assets/LOGO_SMALL.png" alt="Logo" style="vertical-align: middle; height: 24px;" /> DB-GPT: Open-Source Agentic AI Data Assistant
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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.
!csv_data_analysis_demo_en
2. Autonomous SQL + code execution
Generate SQL and code to query data, clean datasets, compute metrics, and produce outputs.
!agentic_write_code
!sql_query
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.
!sandbox
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):
curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh | bashOr 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 openaiFor 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 kimiFor 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 minimaxAlready 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 "$(pwd)" --yesOr reuse your local repo with Kimi 2.5:
MOONSHOT_API_KEY=sk-xxx \
bash scripts/install/install.sh --profile kimi --repo-dir "$(pwd)" --yesOr reuse your local repo with MiniMax:
MINIMAX_API_KEY=sk-xxx \
bash scripts/install/install.sh --profile minimax --repo-dir "$(pwd)" --yesAfter installation, start the server with the generated profile config:
cd ~/.dbgpt/DB-GPT && uv run dbgpt start webserver --profile <profile>Then open http://localhost:5670.
Prefer to review the script first?
``bashcurl -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
``
Install via PyPI
Install DB-GPT from PyPI and start it with a single command โ no source checkout required.
Prerequisites: Python 3.10+ and uv (recommended) or pip.
1. Install
Recommended: use uv
uv pip install dbgpt-appOr with pip
pip install dbgpt-appThe 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 startOn 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.
3. Open the Web UI
Visit http://localhost:5670 โ you're all set! ๐
Advanced Installation
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
Core Capabilities
Agentic Analysis
- task planning
- step-by-step execution
- tool use
- iterative reasoning
SQL + Code Execution
- natural language to SQL
- Python-based analysis and transformation
- metric calculation
- chart generation
Multi-Source Data Access
- relational databases
- CSV / Excel
- documents
- knowledge bases
- mixed-source workflows
Skills and Agents
- reusable skills
- domain workflows
- agent orchestration
- customizable execution flows
Reporting and Decision Support
- database profiling reports
- financial analysis reports
- visual reports and dashboards
- summaries and business insights
Safe Execution
- sandboxed code execution
- controlled tool use
- reproducible outputs and artifacts
#### Text2SQL Finetune
| LLM | Supported |
|:-----------:|:-----------:|
| LLaMA | โ
|
| LLaMA-2 | โ
|
| BLOOM | โ
|
| BLOOMZ | โ
|
| Falcon | โ
|
| Baichuan | โ
|
| Baichuan2 | โ
|
| InternLM | โ
|
| Qwen | โ
|
| XVERSE | โ
|
| ChatGLM2 | โ
|
More Information about Text2SQL finetune
Supported Models
<table>
<thead>
<tr>
<th>Provider</th>
<th>Supported</th>
<th>Models</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" valign="middle">DeepSeek</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-0528">DeepSeek-R1-0528</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-V3-0324">DeepSeek-V3-0324</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1">DeepSeek-R1</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-V3">DeepSeek-V3</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B">DeepSeek-R1-Distill-Llama-70B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B">DeepSeek-R1-Distill-Qwen-32B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct">DeepSeek-Coder-V2-Instruct</a><br/>
</td>
</tr>
<tr>
<td align="center" valign="middle">Qwen</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen3-235B-A22B">Qwen3-235B-A22B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen3-30B-A3B">Qwen3-30B-A3B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen3-32B">Qwen3-32B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/QwQ-32B">QwQ-32B</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct">Qwen2.5-Coder-32B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct">Qwen2.5-Coder-14B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen2.5-72B-Instruct">Qwen2.5-72B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Qwen/Qwen2.5-32B-Instruct">Qwen2.5-32B-Instruct</a><br/>
</td>
</tr>
<tr>
<td align="center" valign="middle">GLM</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/THUDM/GLM-Z1-32B-0414">GLM-Z1-32B-0414</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/THUDM/GLM-4-32B-0414">GLM-4-32B-0414</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/THUDM/glm-4-9b-chat">Glm-4-9b-chat</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Llama</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct">Meta-Llama-3.1-405B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct">Meta-Llama-3.1-70B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct">Meta-Llama-3.1-8B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct">Meta-Llama-3-70B-Instruct</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct">Meta-Llama-3-8B-Instruct</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Gemma</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/google/gemma-2-27b-it">gemma-2-27b-it</a><br>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/google/gemma-2-9b-it">gemma-2-9b-it</a><br>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/google/gemma-7b-it">gemma-7b-it</a><br>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/google/gemma-2b-it">gemma-2b-it</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Yi</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/01-ai/Yi-1.5-34B-Chat">Yi-1.5-34B-Chat</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/01-ai/Yi-1.5-9B-Chat">Yi-1.5-9B-Chat</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/01-ai/Yi-1.5-6B-Chat">Yi-1.5-6B-Chat</a><br/>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/01-ai/Yi-34B-Chat">Yi-34B-Chat</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Starling</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/Nexusflow/Starling-LM-7B-beta">Starling-LM-7B-beta</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">SOLAR</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0">SOLAR-10.7B</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Mixtral</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1">Mixtral-8x7B</a>
</td>
</tr>
<tr>
<td align="center" valign="middle">Phi</td>
<td align="center" valign="middle">โ
</td>
<td>
๐ฅ๐ฅ๐ฅ <a href="https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3">Phi-3</a>
</td>
</tr>
</tbody>
</table>
Privacy and Security
We protect data privacy and execution safety through private model deployment, proxy desensitization, and sandboxed execution mechanisms.
Data Sources
- Datasources
Vision
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.
Contribution
- To check detailed guidelines for new contributions, please refer how to contribute
Contributors Wall
<a href="https://github.com/eosphoros-ai/DB-GPT/graphs/contributors">
<img src="https://contrib.rocks/image?repo=eosphoros-ai/DB-GPT&max=200" />
</a>
Licence
The MIT License (MIT)
DISCKAIMER
- disckaimer
Citation
If you want to understand the overall architecture of DB-GPT, please cite <a href="https://arxiv.org/abs/2312.17449" target="_blank">Paper</a> and <a href="https://arxiv.org/abs/2404.10209" target="_blank">Paper</a>
If you want to learn about using DB-GPT for Agent development, please cite the <a href="https://arxiv.org/abs/2412.13520" target="_blank">Paper</a>
@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 โญ๏ธ๏ผFor questions about using GB-DPT, see the CONTRIBUTING.
- Github Discussions โญ๏ธ๏ผShare your experience or unique apps.
- Twitter โญ๏ธ๏ผPlease feel free to talk to us.
