natural-language-to-sql

Self-hosted AI gateway for private RAG, natural-language data access, and tool-calling agents.

Open-source text-to-SQL engine you tune and measure on your own database

Open-source natural-language analytics platform that converts business questions into safe, explainable SQL.

ln2sql as a python package

Natural Language to SQL using Google's Gemini Pro Model

Finetuning Google's Gemma Model for Translating Natural Language into SQL

QueryWise: Natural-language SQL assistant with semantic context, glossary- aware query generation, and a React/FastAPI stack.

AI agent for answering SQL-based questions using LLaMA and FastAPI

An AI-first SQL assistant that turns natural language and voice into safe SQL and explains the results in plain English.

Open-source semantic compiler for reliable natural-language analytics over Postgres.

Open source natural language to SQL (NL2SQL) engine with schema validation and an MCP server for Claude and Cursor

A tool to query a database in natural language

SchemaMind turns plain-language questions into validated SQLite queries. Upload a .db, ask anything, and get back executable SQL plus the actual results — powered by FAISS schema retrieval, Gemini, and a strict safety layer.

An agentic chatbot to answer questions regarding a hotel's information and database.

Hybrid Natural Language to SQL (NL2SQL) system using Machine Learning and Rule-Based NLP to convert plain English questions into SQL queries from CSV & Excel datasets. Built for EWU CSE366 AI Project.

An interactive DataOps assistant using an LLM-powered agent architecture to automate data profiling, cleaning, feature engineering, and natural language-based querying & visualization.

Evaluation and observability for natural-language agents over dbt projects, with read-only SQL, execution-based evals, and evidence-backed answers.

LLM agent able to do database queries and mutations

SQL Natural Language Query Assistant is a powerful Streamlit application that lets you interact with tabular data using everyday language instead of complex SQL queries. This tool bridges the gap between data analysis and natural language processing, making data exploration accessible to everyone, regardless of their SQL expertise.

MCP-native autonomous data analyst. Natural language → validated SQL, with multi-turn conversation, statistical insight agent, proactive watchdog, dual-path verification, and a community registry of semantic models.