DB-GPT vs LlamaIndex
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
Python framework for building LLM apps that query your own documents and data.
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 LlamaIndex if you need…
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Side-by-side
| DB-GPT | LlamaIndex | |
|---|---|---|
| Type | App | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 20.2k | 52.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 10 days 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.
LlamaIndex
LlamaIndex is an open-source Python framework for connecting large language models to your own data, such as PDFs, documents, APIs and SQL databases. It provides connectors to load data, ways to structure it into indexes, and retrieval tools that feed relevant context to an LLM. The README also notes that the company's main focus has shifted toward its commercial LlamaParse document platform, with the open-source framework still available.
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
LlamaIndex
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
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)
LlamaIndex
- Large ecosystem of over 300 integration packages, per the README
- Modular design lets you customize or replace components like retrievers and query engines
- Offers both a high-level API for quick starts and lower-level APIs for advanced control
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
LlamaIndex
- Non-programmers, since it is a Python library that requires writing code
- Projects that need a fully managed hosted service, because the README points to LlamaParse for the hosted offering and the framework is a toolkit you run yourself
- Users who only need simple one-off document parsing, where a standalone parser may be simpler
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
LlamaIndex
- The README says the company's primary focus has shifted to LlamaParse, so the open-source framework is secondary to its commercial product
- The README is not updated as often as the documentation, so you should check the docs for current details
- Many separate integration packages means you must choose and install the ones you need
Getting started
DB-GPT
Install from PyPI with uv (recommended)
uv pip install dbgpt-appInstall from PyPI with pip
pip install dbgpt-appLlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
What is the difference between DB-GPT and LlamaIndex?
DB-GPT: Open-source AI assistant that writes SQL and code to analyze your databases and files. LlamaIndex: Python framework for building LLM apps that query your own documents and data. DB-GPT suits analysts who want to ask questions of csv or excel files in plain language, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are DB-GPT and LlamaIndex free?
DB-GPT: Free and open source. LlamaIndex: Free and open source.
Is DB-GPT or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than DB-GPT (20.2k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DB-GPT and LlamaIndex?
Other options people compare them with include LangChain, LlamaGPT, 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.