SQLBot Demo

"If you give an agent a tool, then nobody has to fish."

SQLBot is a new kind of interface for your database. Instead of writing SQL queries yourself, you delegate high-level analytical tasks to an AI agent. It reasons through your request, executing a chain of queries and analyzing the results until it arrives at a complete answer—all while keeping your data safe with built-in safeguards.

It represents the next logical layer on the modern data stack, building directly on the power of SQL and dbt.

The Problem with Raw SQL

Most people use SQL through apps. Maybe you're comfortable writing raw SQL queries if you're a wizard. Most people aren't.

SQL Layer: The foundation layer showing raw SQL complexity

Sure, SQL powers most relational databases—it's incredibly powerful. But here's the thing: even simple questions can turn into sprawling queries with multiple joins and cryptic table relationships. Want to see what a "basic" customer lookup actually looks like? Here's what you'd need to write just to get someone's rental history from the Sakila database:

-- Raw SQL: Get rental history for customer 526
SELECT
  f.title,
  f.description,
  r.rental_date
FROM customer c
JOIN rental r
  ON c.customer_id = r.customer_id
JOIN inventory i
  ON r.inventory_id = i.inventory_id
JOIN film f
  ON i.film_id = f.film_id
WHERE
  c.customer_id = 526
ORDER BY
  r.rental_date DESC;

This is hard to reuse and requires every user to understand the database's join logic.

Enter dbt: Sharing Database Knowledge

dbt Layer: Knowledge sharing layer making database expertise accessible to business users

Here's where dbt changes the game. Those database wizards who understand all the table relationships? They can package their knowledge into macros and schemas that business users can actually work with. That ugly query above becomes a simple, self-documenting function:

-- In a file like `macros/get_customer_rental_history.sql`
{% macro get_customer_rental_history(customer_id) %}
  SELECT
    f.title,
    f.description,
    r.rental_date
  FROM customer c
  JOIN rental r
    ON c.customer_id = r.customer_id
  JOIN inventory i
    ON r.inventory_id = i.inventory_id
  JOIN film f
    ON i.film_id = f.film_id
  WHERE
    c.customer_id = {{ customer_id }}
  ORDER BY
    r.rental_date DESC;
{% endmacro %}

Suddenly, business users can access complex database operations without needing to understand the underlying join logic:

-- A business user can now write this instead:
{{ get_customer_rental_history(customer_id=526) }}

The real magic happens in dbt's schema.yml files—they're like institutional memory for your database. Wizards document what each table and column actually means in plain English, creating a shared vocabulary that makes databases accessible to entire teams.

SQLBot: Adding Intelligence & Safety

SQLBot Layer: Intelligence and safety layer with AI agent on top of the stack

SQLBot adds the final layer: an AI agent that uses the structure dbt provides while keeping your data protected. The agent is armed with two crucial pieces of information from your dbt profile:

Built-in Safeguards: SQLBot includes read-only protections and query validation to prevent dangerous operations like DROP, DELETE, or UPDATE commands, ensuring your data stays safe while you focus on analysis rather than syntax.

This layered approach gives you the best of all worlds: the raw power of SQL, the structure and reusability of dbt, the conversational intelligence of an AI Agent, and the peace of mind that comes with built-in safety controls.

The Result: A Real-World Example

Because the agent understands this stack, you no longer write SQL or dbt. You state a business goal.

You: "I want to run a 'customer of the month' promotion. First, identify our single best customer based on their total lifetime payment amount. Once you have that customer, find out which actor they have rented the most movies from. I'll need the customer's full name, their email address, the full name of their favorite actor, and the count of films they've rented by that actor."

SQLBot accepts the task and begins its reasoning process, writing and executing the necessary SQL.

Query 1: Find the Top Customer

SELECT p.customer_id, c.first_name, c.last_name, c.email, SUM(p.amount) AS total_payments
FROM payment p
JOIN customer c ON p.customer_id = c.customer_id
GROUP BY p.customer_id
ORDER BY total_payments DESC
LIMIT 1

✔ Query passes safeguard against dangerous operations.
▷ Query Result → Success: Query completed

Query 2: Find Their Favorite Actor

WITH cust_rentals AS (
  SELECT r.rental_id, r.inventory_id
  FROM rental r
  WHERE r.customer_id = 526
), films AS (
  SELECT cr.rental_id, i.film_id
  FROM cust_rentals cr
  JOIN inventory i ON cr.inventory_id = i.inventory_id
), film_actor_map AS (
  SELECT f.rental_id, fa.actor_id
  FROM films f
  JOIN film_actor fa ON f.film_id = fa.film_id
)
SELECT a.first_name || ' ' || a.last_name AS actor_name, COUNT(*) AS rentals_by_actor
FROM film_actor_map fam
JOIN actor a ON fam.actor_id = a.actor_id
GROUP BY fam.actor_id
ORDER BY rentals_by_actor DESC, actor_name ASC
LIMIT 1

✔ Query passes safeguard against dangerous operations.
▷ Query Result → Success: Query completed

It then delivers a synthesized answer, even suggesting the next logical step.

SQLBot:

Got it — we'll find the top customer by total payments, then their most-rented actor.

Approach:

  • Query total lifetime payments by customer to get the top customer.
  • For that customer, join rentals → inventory → film_actor → actor to find the actor with the highest rental count.

Results:

Next step suggestion: Want a quick profile for this customer (total rentals, average spend, preferred categories)?

From SQL Syntax to Business Questions

The traditional approach requires you to:

  1. Write SQL - Master complex syntax and join logic
  2. Debug queries - Fix syntax errors and performance issues
  3. Interpret results - Manually analyze and synthesize findings

SQLBot flips this model. Instead of writing SQL, you delegate analysis:

  1. Ask business questions - "Who are our top customers this quarter?"
  2. Let the agent work - It writes, executes, and chains multiple queries safely
  3. Get insights - Receive synthesized answers with suggested next steps

The result? You spend time on strategy and insights, not syntax and debugging.

Key Features

Data Export Capabilities

SQLBot includes powerful data export functionality that allows you to save query results in multiple formats:

Supported Export Formats

How to Export Data

After running any successful query, you can export the results using natural language:

# After running a query, simply ask to export
> "Show me the top 10 customers by revenue"
[Query executes and shows results]

> "Export this to CSV"
> "Save this as Excel"
> "Export to Parquet format"
> "Save this to /path/to/my/reports as Excel"

Export Features

Install & Setup

1. Installation

pip install sqlbot

# Verify installation
sqlbot --help

2. Environment (.env)

Create a .env file in the root directory with your API key and database credentials.

# OpenAI API Configuration
OPENAI_API_KEY=your_openai_api_key_here

# SQLBot LLM Configuration
SQLBOT_LLM_MODEL=gpt-5
SQLBOT_LLM_MAX_TOKENS=10000
SQLBOT_LLM_TEMPERATURE=0.1
SQLBOT_LLM_VERBOSITY=low
SQLBOT_LLM_EFFORT=minimal
SQLBOT_LLM_PROVIDER=openai

3. Database Connection (dbt profiles)

SQLBot supports both local and global dbt profile configurations:

SQLBot automatically detects and prioritizes local profiles when available.

PostgreSQL:

qbot:
  target: dev
  outputs:
    dev:
      type: postgres
      host: "{{ env_var('DB_SERVER') }}"
      user: "{{ env_var('DB_USER') }}"
      password: "{{ env_var('DB_PASS') }}"
      dbname: "{{ env_var('DB_NAME') }}"

SQL Server:

qbot:
  target: dev
  outputs:
    dev:
      type: sqlserver
      driver: 'ODBC Driver 17 for SQL Server'
      server: "{{ env_var('DB_SERVER') }}"
      database: "{{ env_var('DB_NAME') }}"
      user: "{{ env_var('DB_USER') }}"
      password: "{{ env_var('DB_PASS') }}"

Then, test your connection: dbt debug

4. Teach the Agent Your Schema

This is the most important step. Create a profiles/qbot/models/schema.yml file. The agent's performance depends heavily on clear, detailed descriptions for your tables and columns.

version: 2
sources:
  - name: my_database
    schema: dbo
    tables:
      - name: customers
        description: "Contains one record per customer, including personal details and account creation date."
        columns:
          - name: customer_id
            description: "Unique identifier for each customer (Primary Key)."

Usage

# Start interactive mode
sqlbot

# Delegate a task from the command line
sqlbot "How many new customers did we get last month?"

Quick Start with Sample Data

🚀 Recommended: Use our standalone demo project for the easiest SQLBot experience:

# Clone the demo project (includes everything you need)
git clone https://github.com/AnthusAI/SQLBot-Sakila-SQLite
cd SQLBot-Sakila-SQLite

# Install SQLBot and dependencies
pip install -e .

# Set up the Sakila database (SQLite - no server required!)
sqlbot setup sakila

# Start exploring with natural language queries
sqlbot --profile Sakila

Try these queries:

Why SQLite? No database server installation required! The Sakila database runs entirely from a single .db file with:

Using the Demo as a Template

The SQLBot-Sakila-SQLite demo project demonstrates the recommended project structure for SQLBot:

your-database-project/
├── .sqlbot/                    # All SQLBot configuration
│   ├── config.yml             # SQLBot settings
│   ├── agents/                # Custom database knowledge
│   └── profiles/YourDB/       # Database files and config
├── .dbt/profiles.yml          # Local dbt connection config
├── pyproject.toml             # Python dependencies
└── README.md                  # Project documentation

Key benefits:

Security

Open Source

The complete project is available on GitHub under the MIT license.