DB-GPT

Sweet spot: a data engineering team at an organisation where the data cannot leave the building, and the team has the platform skills to run a self-hosted stack. DB-GPT gives you far more than a wrapper around LangChain — the multi-agent analysis pattern is a real productivity gain once dialled in. Failure modes. Small teams under-estimate the operational cost of self-hosting this. If you do not already run a vector DB and have someone who can debug a Python agent runtime, the simpler choice is a hosted tool like Chat2DB or Hex. Feature breadth also means feature churn — pin versions and budget for quarterly upgrades. What to pilot. Deploy it on a dev database, wire in one LLM (OpenAI or a local model), and run five real analytical questions. Measure accuracy. If three or more are correct on first try, the framework is earning its keep; if not, invest in schema documentation and prompt tuning before rolling out further.

DB-GPT is an open-source framework for building AI applications on top of structured data. It bundles a natural-language-to-SQL engine, a multi-agent data-analysis pipeline (one agent plans, one writes SQL, one validates), a knowledge-base layer for unstructured context, and a visual web UI for non-technical users to query databases conversationally. The project ships connectors for 20+ databases (MySQL, Postgres, SQLite, ClickHouse, DuckDB, Spark, Doris, TDengine, and more), supports local and hosted LLMs (ChatGLM, Baichuan, Qwen, OpenAI, Anthropic), and runs fully on-prem — a stated design goal is to never require sending schema or data to external services. A Python SDK plus a Docker-deployable web app cover both integrator and end-user paths. DB-GPT is one of the most actively developed data-AI projects out of China (eosphoros-ai), with strong support for Chinese databases and LLMs. For teams that need on-prem NL-to-SQL plus agent-style data workflows, it is a more complete package than gluing LangChain + a SQL agent together manually.