langflow/docs/versioned_docs/version-1.8.0/Components/sql-database.mdx

---
title: SQL Database
slug: /sql-database
---

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The **SQL Database** component executes SQL queries on [SQLAlchemy-compatible databases](https://docs.sqlalchemy.org/en/20/).
It supports any SQLAlchemy-compatible database, such as PostgreSQL, MySQL, and SQLite.

For CQL queries, see the [**DataStax** bundle](/bundles-datastax).

## Query an SQL database with natural language prompts

The following example demonstrates how to use the **SQL Database** component in a flow, and then modify the component to support natural language queries through an **Agent** component.

This allows you to use the same **SQL Database** component for any query, rather than limiting it to a single manually entered query or requiring the user, application, or another component to provide valid SQL syntax as input.
Users don't need to master SQL syntax because the **Agent** component translates the users' natural language prompts into SQL queries, passes the query to the **SQL Database** component, and then returns the results to the user.

Additionally, input from applications and other components doesn't have to be extracted and transformed to exact SQL queries.
Instead, you only need to provide enough context for the agent to understand that it should create and run a SQL query according to the incoming data.

1. Use your own sample database or create a test database.

    <details>
    <summary>Create a test SQL database</summary>

    1. Create a database called `test.db`:

        ```shell
        sqlite3 test.db
        ```

    2. Add some values to the database:

        ```shell
        sqlite3 test.db "
        CREATE TABLE users (
            id INTEGER PRIMARY KEY,
            name TEXT,
            email TEXT,
            age INTEGER
        );

        INSERT INTO users (name, email, age) VALUES
            ('John Doe', 'john@example.com', 30),
            ('Jane Smith', 'jane@example.com', 25),
            ('Bob Johnson', 'bob@example.com', 35);
        "
        ```

    3. Verify that the database has been created and contains your data:

        ```shell
        sqlite3 test.db "SELECT * FROM users;"
        ```

        The result should list the text data you entered in the previous step:

        ```shell
        1|John Doe|john@example.com
        2|Jane Smith|jane@example.com
        3|John Doe|john@example.com
        4|Jane Smith|jane@example.com
        ```

    </details>

2. Add an **SQL Database** component to your flow.

3. In the **Database URL** field, add the connection string for your database, such as `sqlite:///test.db`.

    At this point, you can enter an SQL query in the **SQL Query** field or use the [port](/concepts-components#component-ports) to pass a query from another component, such as a **Chat Input** component.
    If you need more space, click <Icon name="Expand" aria-hidden="true"/> **Expand** to open a full-screen text field.

    However, to make this component more dynamic in an agentic context, use an **Agent** component to transform natural language input to SQL queries, as explained in the following steps.

4. Click the **SQL Database** component to expose the [component's header menu](/concepts-components#component-menus), and then enable **Tool Mode**.

    You can now use this component as a tool for an agent.
    In **Tool Mode**, no query is set in the **SQL Database** component because the agent will generate and send one if it determines that the tool is required to complete the user's request.
    For more information, see [Configure tools for agents](/agents-tools).

5. Add an **Agent** component to your flow, and then enter your OpenAI API key.

    The default model is an OpenAI model.
    If you want to use a different model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.

    If you need to execute highly specialized queries, consider selecting a model that is trained for tasks like advanced SQL queries.
    If your preferred model isn't in the **Agent** component's built-in model list, set **Model Provider** to **Connect other models**, and then connect any [language model component](/components-models).

6. Connect the **SQL Database** component's **Toolset** output to the **Agent** component's **Tools** input.

    ![SQL Database component connected to an Agent component](/img/component-sql-database.png)

7. Click **Playground**, and then ask the agent a question about the data in your database, such as `Which users are in my database?`

    The agent determines that it needs to query the database to answer the question, uses the LLM to generate an SQL query, and then uses the **SQL Database** component's `RUN_SQL_QUERY` action to run the query on your database.
    Finally, it returns the results in a conversational format, unless you provide instructions to return raw results or a different format.

    The following example queried a test database with little data, but with a more robust dataset you could ask more detailed or complex questions.

    ```text
    Here are the users in your database:

    1. **John Doe** - Email: john@example.com
    2. **Jane Smith** - Email: jane@example.com
    3. **John Doe** - Email: john@example.com
    4. **Jane Smith** - Email: jane@example.com

    It seems there are duplicate entries for the users.
    ```

## SQL Database parameters

<PartialParams />

| Name | Display Name | Info |
|------|--------------|------|
| database_url | Database URL | Input parameter. The SQLAlchemy-compatible database connection URL. |
| query | SQL Query | Input parameter. The SQL query to execute, which can be entered directly, passed in from another component, or, in **Tool Mode**, automatically provided by an **Agent** component. |
| include_columns | Include Columns | Input parameter. Whether to include column names in the result. The default is enabled (`true`). |
| add_error | Add Error | Input parameter. If enabled, adds any error messages to the result, if any are returned. The default is disabled (`false`). |
| run_sql_query | Result Table | Output parameter. The query results as a [`DataFrame`](/data-types#dataframe). |