# Dataherald/dataherald

Interact with your SQL database, Natural Language to SQL using LLMs

Repository: https://github.com/Dataherald/dataherald
Canonical: https://ross.abutalabs.com/products/dataherald
Homepage: https://dataherald.readthedocs.io/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, database, finetuning, llm, nl-to-sql, rag, sql, text-to-sql
Last push: 2024-07-24T17:37:41+00:00

## Health v2 (maintenance only)
Score: 19/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 83
- inputs: {"age_days": 1170, "days_push": 770, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3646, forks 261 (observed 2026-08-28T04:08:12.919653+00:00)

## What it is
Dataherald is an open-source natural language-to-SQL engine for enterprise question answering over relational databases, exposing an API that answers plain-English questions with generated SQL. It ships as a monorepo with an engine, an enterprise API layer with auth, an admin console, and a Slackbot, deployable via Docker Compose.

## Use cases
- let business users query a data warehouse in plain English without an analyst
- add natural language Q&A over a production SQL database to a SaaS app
- build a ChatGPT plugin on top of proprietary relational data
- convert natural language questions into SQL queries with an LLM
- set up a Slack bot that answers data questions from a channel
- fine-tune text-to-SQL generation for a specific database schema

## When to choose
- you need a self-hosted, API-first NL-to-SQL engine over relational data
- you want enterprise features like organizations, users, and authentication around text-to-SQL
- you want a GUI admin console and Slack integration out of the box
- you need fine-tuning support to improve SQL generation accuracy on your schema

## When to avoid
- you need a lightweight library to embed in an existing app rather than a multi-service deployment
- your data is not in a relational/SQL database
- you need a no-code BI dashboard rather than a question-answering API
- you require actively maintained software - the latest release dates to mid-2024

## Facets
- artifact type: service
- maturity: maintenance
- function: rag, llm-inference, api-framework, database, chatbot
- domain: databases, large-language-models, data-science, apis, self-hosted
- platform: python, self-hosted, cross-platform
- tags: text-to-sql, nl2sql, text-to-query, data-warehouse, business-intelligence, enterprise, slackbot, admin-console, retrieval-augmented-generation, docker, web-server

## Member repositories
- Dataherald/dataherald (main) score 19

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:12.919653+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:31:44.025090+00:00, confidence not recorded.
  - readme: https://github.com/Dataherald/dataherald (fetched 2026-08-28T04:08:12.919653+00:00, sha 249b671a5225)
- Data as of 2026-08-30T08:39:29.467469+00:00.