In this tutorial, you will learn how to build an agent with minimal code that can answer questions about a SQL database. At a high level, the agent will:
1

Fetch the available tables and schemas from the database

2

Decide which tables are relevant to the question

3

Generate a query based on the question and information from the schemas

4

Safety-check the query to limit the impact of LLM-generated queries

5

Execute the query and return the results

6

Correct mistakes surfaced by the database engine until the query is successful

7

Formulate a response based on the results

Building Q&A systems of SQL databases requires executing model-generated SQL queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your agent’s needs. This will mitigate, though not eliminate, the risks of building a model-driven system.

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Before you begin

  1. Install dependencies: 
    npm i langchain @langchain/core typeorm sqlite3 zod
  2. Set up LangSmith to inspect what is happening inside your chain or agent. Then set the following environment variables: 
    export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

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Build an agent with minimal code

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1. Select an LLM

Select a model that supports tool-calling: 
npm i @langchain/openai

OPENAI_API_KEY=your-api-key

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
});
The output shown in the examples below used OpenAI.

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2. Configure the database

You will be creating a SQLite database for this tutorial. SQLite is a lightweight database that is easy to set up and use. We will be loading the chinook database, which is a sample database that represents a digital media store. For convenience, we have hosted the database (Chinook.db) on a public GCS bucket. 
import fs from "node:fs/promises";
import path from "node:path";

const url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db";
const localPath = path.resolve("Chinook.db");

async function resolveDbPath() {
  if (await fs.exists(localPath)) {
    return localPath;
  }
  const resp = await fetch(url);
  if (!resp.ok) throw new Error(`Failed to download DB. Status code: ${resp.status}`);
  const buf = Buffer.from(await resp.arrayBuffer());
  await fs.writeFile(localPath, buf);
  return localPath;
}

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3. Add tools for database interactions

Use the SqlDatabase wrapper available in the langchain/sql_db to interact with the database. The wrapper provides a simple interface to execute SQL queries and fetch results: 
import { SqlDatabase } from "langchain/sql_db";
import { DataSource } from "typeorm";

let db: SqlDatabase | undefined;
async function getDb() {
  if (!db) {
    const dbPath = await resolveDbFile();
    const datasource = new DataSource({ type: "sqlite", database: dbPath });
    db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource });
  }
  return db;
}

async function getSchema() {
  const db = await getDb();
  return await db.getTableInfo();
}

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4. Execute SQL queries

Before running the command, do a check to check the LLM generated command in _safe_sql: 

const DENY_RE = /\b(INSERT|UPDATE|DELETE|ALTER|DROP|CREATE|REPLACE|TRUNCATE)\b/i;
const HAS_LIMIT_TAIL_RE = /\blimit\b\s+\d+(\s*,\s*\d+)?\s*;?\s*$/i;

function sanitizeSqlQuery(q) {
  let query = String(q ?? "").trim();

  // block multiple statements (allow one optional trailing ;)
  const semis = [...query].filter((c) => c === ";").length;
  if (semis > 1 || (query.endsWith(";") && query.slice(0, -1).includes(";"))) {
    throw new Error("multiple statements are not allowed.")
  }
  query = query.replace(/;+\s*$/g, "").trim();

  // read-only gate
  if (!query.toLowerCase().startsWith("select")) {
    throw new Error("Only SELECT statements are allowed")
  }
  if (DENY_RE.test(query)) {
    throw new Error("DML/DDL detected. Only read-only queries are permitted.")
  }

  // append LIMIT only if not already present
  if (!HAS_LIMIT_TAIL_RE.test(query)) {
    query += " LIMIT 5";
  }
  return query;
}
Then, use run from SQLDatabase to execute commands with an execute_sql tool: 
import { tool } from "langchain"
import { z } from "zod";

const executeSql = tool(
  async ({ query }) => {
    const q = sanitizeSqlQuery(query);
    try {
      const result = await db.run(q);
      return typeof result === "string" ? result : JSON.stringify(result, null, 2);
    } catch (e) {
      throw new Error(e?.message ?? String(e))
    }
  },
  {
    name: "execute_sql",
    description: "Execute a READ-ONLY SQLite SELECT query and return results.",
    schema: z.object({
      query: z.string().describe("SQLite SELECT query to execute (read-only)."),
    }),
  }
);

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5. Use createAgent

Use createAgent to build a ReAct agent with minimal code. The agent will interpret the request and generate a SQL command. The tools will check the command for safety and then try to execute the command. If the command has an error, the error message is returned to the model. The model can then examine the original request and the new error message and generate a new command. This can continue until the LLM generates the command successfully or reaches an end count. This pattern of providing a model with feedback - error messages in this case - is very powerful. Initialize the agent with a descriptive system prompt to customize its behavior: 
import { SystemMessage } from "langchain";

const getSystemPrompt = async () => new SystemMessage(`You are a careful SQLite analyst.

Authoritative schema (do not invent columns/tables):
${await getSchema()}

Rules:
- Think step-by-step.
- When you need data, call the tool \`execute_sql\` with ONE SELECT query.
- Read-only only; no INSERT/UPDATE/DELETE/ALTER/DROP/CREATE/REPLACE/TRUNCATE.
- Limit to 5 rows unless user explicitly asks otherwise.
- If the tool returns 'Error:', revise the SQL and try again.
- Limit the number of attempts to 5.
- If you are not successful after 5 attempts, return a note to the user.
- Prefer explicit column lists; avoid SELECT *.
`);
Now, create an agent with the model, tools, and prompt: 
import { createAgent } from "langchain";

const agent = createAgent({
  model: "openai:gpt-5",
  tools: [executeSql],
  prompt: getSystemPrompt,
});

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5. Run the agent

Run the agent on a sample query and observe its behavior: 
const question = "Which genre, on average, has the longest tracks?";
const stream = await agent.stream(
  { messages: [{ role: "user", content: question }] },
  { streamMode: "values" }
);
for await (const step of stream) {
  const message = step.messages.at(-1);
  console.log(`${message.role}: ${JSON.stringify(message.content, null, 2)}`);
}

human: Which genre, on average, has the longest tracks?
ai:
tool: [{"Genre":"Sci Fi & Fantasy","AvgMilliseconds":2911783.0384615385}]
ai: Sci Fi & Fantasy — average track length ≈ 48.5 minutes (about 2,911,783 ms).
The agent correctly wrote a query, checked the query, and ran it to inform its final response.
You can inspect all aspects of the above run, including steps taken, tools invoked, what prompts were seen by the LLM, and more in the LangSmith trace.

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(Optional) Use Studio

Studio provides a “client side” loop as well as memory so you can run this as a chat interface and query the database. You can ask questions like “Tell me the scheme of the database” or “Show me the invoices for the 5 top customers”. You will see the SQL command that is generated and the resulting output. The details of how to get that started are below.

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Next steps

Check out the Evaluate a graph guide for evaluating LangGraph applications, including SQL agents like this one, using LangSmith.