# <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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[](README.md)
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[**Documents**](http://docs.dbgpt.cn/docs/overview/) | [**Contact Us**](https://github.com/eosphoros-ai/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC) | [**Community**](https://github.com/eosphoros-ai/community) | [**Paper**](https://arxiv.org/pdf/2312.17449.pdf)
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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):
```bash
curl -fsSL https://raw.githubusercontent.com/eosphoros-ai/DB-GPT/main/scripts/install/install.sh | bash
```
Or specify a profile and API key directly:
```bash
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:
```bash
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:
```bash
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`:
```bash
OPENAI_API_KEY=sk-xxx \
bash scripts/install/install.sh --profile openai --repo-dir "$(pwd)" --yes
```
Or reuse your local repo with Kimi 2.5:
```bash
MOONSHOT_API_KEY=sk-xxx \
bash scripts/install/install.sh --profile kimi --repo-dir "$(pwd)" --yes
```
Or reuse your local repo with MiniMax:
```bash
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:
```bash
cd ~/.dbgpt/DB-GPT && uv run dbgpt start webserver --profile <profile>
```
Then open [http://localhost:5670](http://localhost:5670).
> **Prefer to review the script first?**
> ```bash
> 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
> ```
### Install via PyPI
Install DB-GPT from PyPI and start it with a single command — no source checkout required.
> **Prerequisites:** Python **3.10+** and [uv](https://docs.astral.sh/uv/getting-started/installation/) (recommended) or pip.
**1. Install**
```bash
# 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**
```bash
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.
**3. Open the Web UI**
Visit [http://localhost:5670](http://localhost:5670) — you're all set! 🎉
### Advanced Installation
For Docker, local GPU models (vLLM, llama.cpp), or manual source-code setup, see the full docs:
- [**Install**](http://docs.dbgpt.cn/docs/installation)
- [Docker](http://docs.dbgpt.cn/docs/installation/docker)
- [Source Code](http://docs.dbgpt.cn/docs/getting-started/deploy/source-code)
- [**Quickstart**](http://docs.dbgpt.cn/docs/overview)
- [**Application**](http://docs.dbgpt.cn/docs/use_cases)
- [Development Guide](http://docs.dbgpt.cn/docs/cookbook/app/data_analysis_app_develop)
- [AWEL Flow Usage](http://docs.dbgpt.cn/docs/application/awel_flow_usage)
- [**Debugging**](http://docs.dbgpt.cn/docs/operation_manual/advanced_tutorial/debugging)
- [**Advanced Usage**](http://docs.dbgpt.cn/docs/application/advanced_tutorial/cli)
- [SMMF](http://docs.dbgpt.cn/docs/application/advanced_tutorial/smmf)
- [Finetune](http://docs.dbgpt.cn/docs/application/fine_tuning_manual/dbgpt_hub)
- [AWEL](http://docs.dbgpt.cn/docs/awel/tutorial)
## 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 | ✅ |
| LLaMA-2 | ✅ |
| BLOOM | ✅ |
| BLOOMZ | ✅ |
| Falcon | ✅ |
| Baichuan | ✅ |
| Baichuan2 | ✅ |
| InternLM | ✅ |
| Qwen | ✅ |
| XVERSE | ✅ |
| ChatGLM2 | ✅ |
[More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub)
### 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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-0528">DeepSeek-R1-0528</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-V3-0324">DeepSeek-V3-0324</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1">DeepSeek-R1</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-V3">DeepSeek-V3</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B">DeepSeek-R1-Distill-Llama-70B</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B">DeepSeek-R1-Distill-Qwen-32B</a><br/>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen3-235B-A22B">Qwen3-235B-A22B</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen3-30B-A3B">Qwen3-30B-A3B</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen3-32B">Qwen3-32B</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/QwQ-32B">QwQ-32B</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct">Qwen2.5-Coder-32B-Instruct</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct">Qwen2.5-Coder-14B-Instruct</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/Qwen/Qwen2.5-72B-Instruct">Qwen2.5-72B-Instruct</a><br/>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/THUDM/GLM-Z1-32B-0414">GLM-Z1-32B-0414</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/THUDM/GLM-4-32B-0414">GLM-4-32B-0414</a><br/>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct">Meta-Llama-3.1-405B-Instruct</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct">Meta-Llama-3.1-70B-Instruct</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct">Meta-Llama-3.1-8B-Instruct</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct">Meta-Llama-3-70B-Instruct</a><br/>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/google/gemma-2-27b-it">gemma-2-27b-it</a><br>
🔥🔥🔥 <a href="https://huggingface.co/google/gemma-2-9b-it">gemma-2-9b-it</a><br>
🔥🔥🔥 <a href="https://huggingface.co/google/gemma-7b-it">gemma-7b-it</a><br>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/01-ai/Yi-1.5-34B-Chat">Yi-1.5-34B-Chat</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/01-ai/Yi-1.5-9B-Chat">Yi-1.5-9B-Chat</a><br/>
🔥🔥🔥 <a href="https://huggingface.co/01-ai/Yi-1.5-6B-Chat">Yi-1.5-6B-Chat</a><br/>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <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">✅</td>
<td>
🔥🔥🔥 <a href="https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3">Phi-3</a>
</td>
</tr>
</tbody>
</table>
- [More Supported LLMs](http://docs.dbgpt.cn/docs/modules/smmf)
### Privacy and Security
We protect data privacy and execution safety through private model deployment, proxy desensitization, and sandboxed execution mechanisms.
### Data Sources
- [Datasources](http://docs.dbgpt.cn/docs/modules/connections)
## 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](https://github.com/eosphoros-ai/DB-GPT/blob/main/CONTRIBUTING.md)
### 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](./DISCKAIMER.md)
## 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>
```bibtex
@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](https://github.com/eosphoros-ai/DB-GPT/issues) ⭐️：For questions about using GB-DPT, see the CONTRIBUTING.
- [Github Discussions](https://github.com/orgs/eosphoros-ai/discussions) ⭐️：Share your experience or unique apps.
- [Twitter](https://x.com/DBGPT_AI) ⭐️：Please feel free to talk to us.
[](https://star-history.com/#csunny/DB-GPT)