This project connects to structured and unstructured data sources to perform agentic analysis, generate SQL and code, and produce reports via a web interface. It requires an external or local LLM provider to operate.
Project overview
The system breaks down natural language questions into multi-step analysis tasks, generates and executes SQL and code in sandboxed environments, and outputs decision-ready charts and dashboards.
Project type
AI Agent · RAG · Data Processing
Use cases
Knowledge Q&A · Data Analysis
Deployment
Refer to project documentation
License
MIT
Best for
Developers, data teams, and enterprise teams who need to perform text-to-SQL queries, agentic data analysis, and enterprise RAG across diverse data sources.
Key capabilities
The system can plan tasks, break work into steps, call tools, and complete analysis workflows end to end.
It generates SQL and code to query data, clean datasets, compute metrics, and produce outputs.
It works across structured and unstructured sources, including databases, spreadsheets, documents, and knowledge bases.
Users can package domain knowledge, analysis methods, and execution workflows into reusable skills.
The system runs code and tools in isolated environments for safer and more reliable analysis.
It produces charts, dashboards, HTML reports, and decision-ready outputs.
Limitations and risks
Core functionality requires an LLM provider via an OpenAI-compatible API or local model serving.
Using external LLM APIs introduces external data boundaries, although local serving and private model deployment mechanisms are available.
Telemetry configuration is not documented in the available facts.
Getting started
Setup involves running a one-line installer script or PyPI package, starting the server, and accessing the web GUI.
Alternatives and comparisons
Converts natural language questions into SQL queries, validates syntax, and executes them across multiple database types for conversational data analysis.
Agent framework that generates SQL, charts, dashboards, and reports from natural language with inspectable and shareable declarative pipelines.
Modular data processing system for cleaning, synthesizing, and analyzing raw data to create AI-ready datasets across the foundation model lifecycle.
GitHub project description: open-source agentic AI data assistant for the next generation of AI + Data products.
README: 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, wa…
README: db-gpt
README: Plan tasks, break work into steps, call tools, and complete analysis workflows end to end.
README: Generate SQL and code to query data, clean datasets, compute metrics, and produce outputs.