NLP to SQL Open Source: 5 Tools Compared (2026)

You want people to ask a database a normal question and get a trustworthy answer. The open-source route can give you control over models, prompts, metadata, and hosting, but it also makes your team responsible for accuracy, permissions, evaluation, and maintenance.

This comparison covers five NLP to SQL open source options that teams still encounter in 2026: Wren AI, DB-GPT, PremSQL, Vanna, and Dataherald. The practical answer is blunt: Wren AI and DB-GPT are the strongest starting points for a new build; PremSQL suits local experiments; Vanna and Dataherald now require a careful maintenance decision.

Quick answer: which open-source NLP-to-SQL tool should you choose?

If the people asking questions are operators, customer success leads, or product managers rather than engineers, a managed product is usually faster. AI for Database connects to your database, accepts plain-English questions, turns answers into refreshing dashboards, and can trigger email, Slack, or webhook actions without your team building the application layer.

What does NLP to SQL open source actually include?

NLP to SQL, also called text-to-SQL, converts a question such as 'Which trial accounts used the export feature but did not upgrade?' into a database query. Generating syntactically valid SQL is only the first step. A useful system must understand table relationships, company-specific metric definitions, permissions, ambiguous language, and the database dialect.

Open source can mean a model, a Python library, a semantic layer, or a complete self-hosted application. Those are not interchangeable. A model may generate SQL but leave you to build schema retrieval, authentication, query execution, result rendering, dashboards, audit logs, and user feedback. A full application covers more of that path but demands more infrastructure.

How these five tools were evaluated

The useful criteria are not demo screenshots or GitHub stars. They are the work required to make answers dependable for real users.

Repository status in this article was checked on August 20, 2026. Open-source projects change quickly, so verify the official repository and license before committing to one.

1. Wren AI: best for governed business metrics

Wren AI is the strongest choice when the hard problem is meaning rather than SQL syntax. Its context layer lets you describe models, columns, relationships, calculations, and business rules. The agent works from that governed context instead of guessing joins from raw table names.

The project supports more than 20 data sources and includes a Python SDK and command-line workflow. Its memory can retain confirmed natural-language and SQL pairs, which helps repeat common questions consistently. That makes it a good fit for analytics teams that already treat metric definitions as code.

The tradeoff is setup. Someone must create and maintain the modeling layer, resolve conflicting definitions, and review generated queries. Wren AI reduces semantic ambiguity; it does not remove data governance work. Its current core paths use the Apache 2.0 license, but the repository contains a path-based license map, so inspect the license for the components you deploy.

Official source: https://github.com/Canner/WrenAI

2. DB-GPT: best for a broader data agent

DB-GPT goes beyond a narrow SQL generator. It can connect to databases and files, plan an analysis, write SQL and Python, execute steps in a sandbox, and produce charts or reports. Pick it when your target experience is an analyst-like agent rather than a single question-to-query endpoint.

Its breadth is also the cost. More agent tools and execution paths mean more configuration, more dependencies, and a larger security surface. You need explicit controls for credentials, tool access, query timeouts, result size, and what code may run. Do not rely on 'read only' in a prompt; enforce it with database roles and network policy.

DB-GPT was active when checked and uses the MIT license. It is a sensible foundation for an engineering team that wants to customize the entire data-agent workflow and has time to operate it.

Official source: https://github.com/eosphoros-ai/DB-GPT

3. PremSQL: best for local-first experiments

PremSQL is a Python library for building local-first text-to-SQL pipelines with smaller language models. It includes generators, executors, agents, an API, and a self-hosted playground. The appeal is control: sensitive schema context and inference can stay in your environment when you choose local components.

It fits research prototypes, private-data experiments, and teams comparing small models. It is less attractive for a business-facing rollout unless your engineers are ready to build identity, permissions, evaluation, monitoring, and a polished interface around it.

The repository is MIT-licensed, but its latest code push visible during this review was February 2025. That does not make the code unusable. It does mean you should test current dependencies and decide whether you are willing to maintain a fork before making it part of a critical workflow.

Official source: https://github.com/premAI-io/premsql

4. Vanna: capable framework, archived repository

Vanna became popular by combining schema information, documentation, and successful SQL examples through retrieval. Its Python framework can connect to many SQL databases and return SQL, results, tables, and charts. The 2.0 design also added a web component, streaming output, user-aware controls, and lifecycle hooks.

The deciding fact in 2026 is maintenance: the official repository was archived on March 29, 2026 and is read-only. Existing users may reasonably keep a pinned deployment or maintain a fork. New teams should not choose it merely because old comparison posts call it active.

Vanna remains MIT-licensed and its architecture is worth studying. For a new production system, however, an archived upstream shifts security fixes, dependency upgrades, and database compatibility onto you.

Official source: https://github.com/vanna-ai/vanna

5. Dataherald: API-first, but verify maintenance

Dataherald is a natural-language-to-SQL engine designed to expose an API over structured data. Its modular design covers schema context, SQL generation, evaluation, and feedback. It can suit a team that wants a backend service and intends to build its own user interface.

The repository is Apache 2.0 licensed, but the latest code push visible during this review was July 2024. Treat it as code you may need to own rather than a continuously supported dependency. Before adoption, build a clean environment, scan dependencies, run its tests, and confirm support for your database and model provider.

Dataherald can still be useful as an architectural reference or controlled internal service. It is a weak choice for a team expecting frequent upstream releases and quick compatibility fixes.

Official source: https://github.com/Dataherald/dataherald

The hidden cost: accuracy is a system, not a model score

A text-to-SQL system can produce valid SQL and still return the wrong business answer. 'Revenue' might mean invoices paid, bookings created, or subscription value excluding tax. A model cannot infer that definition reliably from column names.

Before exposing any tool to your team, create an evaluation set of at least 30 real questions. Include ambiguous wording, multi-table joins, date boundaries, empty results, cancelled accounts, duplicate records, and permission-sensitive fields. Store the expected SQL shape and expected result, then rerun the suite whenever you change the model, prompt, schema context, or tool version.

Use a read-only database user connected to a replica when possible. Add statement timeouts, row limits, table allowlists, and query logging at the database or proxy layer. The model is an untrusted query author. Your database permissions are the final control.

A practical selection framework

Choose open source when

Choose a managed product when

AI for Database is the managed option for that second case. Connect PostgreSQL, MySQL, Supabase, BigQuery, MongoDB, SQLite, or another supported database; ask a question in plain English; save the result to a self-refreshing dashboard; then trigger an email, Slack message, or webhook when the data changes. Try it free at https://aifordatabase.com.

How to run a two-day proof of concept

Success is not 'the demo worked.' A useful proof of concept answers at least 80% of the evaluation set correctly, refuses unsafe requests, and lets a target user get an answer without an engineer stepping in. Set your own threshold higher for financial, compliance, or customer-facing decisions.

Questions people ask about open-source NLP to SQL

What is the best NLP to SQL open source tool in 2026?

Wren AI is the best starting point when governed business definitions matter. DB-GPT is better when you want a broader data-analysis agent. The right answer depends on whether you need a semantic layer, an agent platform, or only a developer library.

Can open-source text-to-SQL run fully locally?

Yes, if you pair a self-hosted framework with a local model, embeddings, and vector store. PremSQL is designed around local-first pipelines. Local deployment improves control but does not remove the need for permissions, evaluation, monitoring, and secure secret handling.

Is it safe to let an LLM query a production database?

Only with controls outside the LLM. Use a read-only role, preferably on a replica, plus table allowlists, statement timeouts, row limits, audit logs, and per-user authorization. Never give the model owner credentials or depend on a prompt to block writes.

Do open-source NLP-to-SQL tools include dashboards and alerts?

Some can render charts or reports, but most require engineering work for persistent dashboards, scheduled refreshes, user permissions, and alerts. AI for Database combines plain-English queries, self-refreshing dashboards, and email, Slack, or webhook workflows in one managed product.

Bottom line

For a new open-source build, shortlist Wren AI and DB-GPT. Use PremSQL for a local experiment. Adopt Vanna or Dataherald only with an explicit maintenance plan. Then judge all of them with your own questions and database, because generic benchmarks cannot validate your metric definitions.

If your real job is giving a non-technical team reliable database answers this week, building and operating the stack is probably the wrong project. Try AI for Database free, connect a read-only database user, and test the same 30-question evaluation set against a working product.