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Overview

This tool is used to convert natural language to SQL queries. When passed to the agent it will generate queries and then use them to interact with the database. This enables multiple workflows like having an Agent to access the database fetch information based on the goal and then use the information to generate a response, report or any other output. Along with that provides the ability for the Agent to update the database based on its goal. Attention: Make sure that the Agent has access to a Read-Replica or that is okay for the Agent to run insert/update queries on the database.

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Security Model

NL2SQLTool is an execution-capable tool. It runs model-generated SQL directly against the configured database connection. This means risk depends on your deployment choices:
  • Which credentials you provide in db_uri
  • Whether untrusted input can influence prompts
  • Whether you add tool-call guardrails before execution
If you route untrusted input to agents using this tool, treat it as a high-risk integration.

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Hardening Recommendations

Use all of the following in production:
  • Use a read-only database user whenever possible
  • Prefer a read replica for analytics/retrieval workloads
  • Grant least privilege (no superuser/admin roles, no file/system-level capabilities)
  • Apply database-side resource limits (statement timeout, lock timeout, cost/row limits)
  • Add before_tool_call hooks to enforce allowed query patterns
  • Enable query logging and alerting for destructive statements

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Requirements

  • SqlAlchemy
  • Any DB compatible library (e.g. psycopg2, mysql-connector-python)

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Installation

Install the crewai_tools package

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Usage

In order to use the NL2SQLTool, you need to pass the database URI to the tool. The URI should be in the format dialect+driver://username:password@host:port/database.
Code

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Example

The primary task goal was: “Retrieve the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of user in each city and sort the results by the average monthly revenue in descending order” So the Agent tried to get information from the DB, the first one is wrong so the Agent tries again and gets the correct information and passes to the next agent. The second task goal was: “Review the data and create a detailed report, and then create the table on the database with the fields based on the data provided. Include information on the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of users in each city and sort the results by the average monthly revenue in descending order.” Now things start to get interesting, the Agent generates the SQL query to not only create the table but also insert the data into the table. And in the end the Agent still returns the final report which is exactly what was in the database. This is a simple example of how the NL2SQLTool can be used to interact with the database and generate reports based on the data in the database. The Tool provides endless possibilities on the logic of the Agent and how it can interact with the database.