DB-GPT vs LlamaGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source AI assistant that writes SQL and code to analyze your databases and files.
Open source · MIT
A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine.
The short answer
Choose DB-GPT if you need…
- Analysts who want to ask questions of CSV or Excel files in plain language
- Teams exploring a database without writing SQL by hand
- Developers building AI-powered data assistants or agent workflows
- People who want to reuse domain-specific analysis steps as skills
Choose LlamaGPT if you need…
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
Side-by-side
| DB-GPT | LlamaGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 20.2k | 10.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | TypeScript |
| Last code update | 4 days ago | 3 years ago |
What each one is
DB-GPT
DB-GPT is an open-source AI data assistant. You connect it to databases, CSV or Excel files, warehouses, or knowledge bases, then ask questions in plain language. It can write SQL and Python code to analyze the data and produce charts, dashboards, HTML reports, and written summaries. It also works as a platform for building custom data agents and workflows.
LlamaGPT
LlamaGPT is a ChatGPT-style chat interface that runs open Llama 2 and Code Llama models on your own hardware instead of a cloud service. The project says no data leaves your device. It is packaged with Docker, so you start it with a script and then use it in a browser at localhost:3000.
Best for
DB-GPT
- Analysts who want to ask questions of CSV or Excel files in plain language
- Teams exploring a database without writing SQL by hand
- Developers building AI-powered data assistants or agent workflows
- People who want to reuse domain-specific analysis steps as skills
LlamaGPT
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
Strengths
DB-GPT
- Covers the full path from connecting data to generating reports in one tool
- Runs code and tools in sandboxed environments, which the README presents as safer execution
- Released under the permissive MIT license with a large community (about 20,000 GitHub stars)
LlamaGPT
- Runs locally with no data sent out, as the README states
- Supports several model sizes, from 7B to 70B, plus Code Llama variants
- Offers an OpenAI-compatible API and one-command Docker setup
Not ideal for
DB-GPT
- Anyone who needs guaranteed-correct numbers without reviewing the generated SQL and code, since AI-written queries can be wrong
- Users without an LLM API key or local model setup, since the assistant needs a model to run
- Teams needing a small, simple tool, since the README describes a broad platform with many components
LlamaGPT
- People with limited RAM or no capable CPU or GPU, since the larger models need 6GB to over 40GB of memory per the README table and run slowly on small devices
- Anyone who needs the latest hosted models or top-quality answers, because these are smaller open models and the README does not compare their quality to GPT-4
- Users who need to load their own custom models today, since the README lists that feature as not yet done
Trade-offs
DB-GPT
- Output depends on the underlying AI model, so results need human checking
- The README is long and covers many features, so the setup and scope may take time to understand
- Some install and usage details are only in the external documentation site, not the README
LlamaGPT
- Speed varies a lot by hardware; the README benchmarks range from 0.9 to 54 tokens per second
- Large models need a lot of memory and disk space
- Custom models and switching models in the UI are still on the roadmap
Getting started
DB-GPT
Install from PyPI with uv (recommended)
uv pip install dbgpt-appInstall from PyPI with pip
pip install dbgpt-appLlamaGPT
Clone the repository
git clone https://github.com/getumbrel/llama-gpt.gitEnter the project folder
cd llama-gptFrequently asked questions
What is the difference between DB-GPT and LlamaGPT?
DB-GPT: Open-source AI assistant that writes SQL and code to analyze your databases and files. LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. DB-GPT suits analysts who want to ask questions of csv or excel files in plain language, while LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider.
Are DB-GPT and LlamaGPT free?
DB-GPT: Free and open source. LlamaGPT: Free and open source.
Is DB-GPT or LlamaGPT more popular?
DB-GPT has more GitHub stars (20.2k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DB-GPT and LlamaGPT?
Other options people compare them with include LangChain, LlamaIndex, MindsHub, Ollama, CowAgent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.