> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # SQLToolkit integration > Integrate with the SQLToolkit tool using LangChain JavaScript. This will help you getting started with `SqlToolkit` [toolkits](/oss/javascript/langchain/tools#prebuilt-tools). For more information, you can also review the [Python SQL toolkit documentation](https://python.langchain.com/docs/integrations/toolkits/sql_database/). This toolkit contains a the following tools: | Name | Description | | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `query-sql` | Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. | | `info-sql` | Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling list-tables-sql first! Example Input: "table1, table2, table3". | | `list-tables-sql` | Input is an empty string, output is a comma-separated list of tables in the database. | | `query-checker` | Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with query-sql! | This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. ## Setup This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. To set it up, follow [these instructions](https://database.guide/2-sample-databases-sqlite/), placing the `.db` file in the directory where your code lives. If you want to get automated tracing from runs of individual tools, you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} process.env.LANGSMITH_TRACING="true" process.env.LANGSMITH_API_KEY="your-api-key" ``` ### Installation This toolkit lives in the `langchain` package. You'll also need to install the `typeorm` peer dependency. ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install langchain @langchain/core typeorm ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add langchain @langchain/core typeorm ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add langchain @langchain/core typeorm ``` ## Instantiation First, we need to define our LLM to be used in the toolkit. ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} // @lc-docs-hide-cell import { ChatOpenAI } from "@langchain/openai"; const llm = new ChatOpenAI({ model: "gpt-5.4-mini", temperature: 0, }) ``` ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { SqlToolkit } from "@langchain/classic/agents/toolkits/sql" import { DataSource } from "typeorm"; import { SqlDatabase } from "@langchain/classic/sql_db"; const datasource = new DataSource({ type: "sqlite", database: "../../../../../../Chinook.db", // Replace with the link to your database }); const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource, }); const toolkit = new SqlToolkit(db, llm); ``` ## Tools View available tools: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const tools = toolkit.getTools(); console.log(tools.map((tool) => ({ name: tool.name, description: tool.description, }))) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ { name: 'query-sql', description: 'Input to this tool is a detailed and correct SQL query, output is a result from the database.\n' + ' If the query is not correct, an error message will be returned.\n' + ' If an error is returned, rewrite the query, check the query, and try again.' }, { name: 'info-sql', description: 'Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\n' + ' Be sure that the tables actually exist by calling list-tables-sql first!\n' + '\n' + ' Example Input: "table1, table2, table3.' }, { name: 'list-tables-sql', description: 'Input is an empty string, output is a comma-separated list of tables in the database.' }, { name: 'query-checker', description: 'Use this tool to double check if your query is correct before executing it.\n' + ' Always use this tool before executing a query with query-sql!' } ] ``` ## Use within an agent First, ensure you have LangGraph installed: ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/langgraph ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/langgraph ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/langgraph ``` ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { createAgent } from "@langchain/classic" const agentExecutor = createAgent({ llm, tools }); ``` ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const exampleQuery = "Can you list 10 artists from my database?" const stream = await agentExecutor.streamEvents( { messages: [["user", exampleQuery]] }, { version: "v3" }, ); for await (const snapshot of stream.values) { const lastMsg = snapshot.messages[snapshot.messages.length - 1]; if (lastMsg.tool_calls?.length) { console.dir(lastMsg.tool_calls, { depth: null }); } else if (lastMsg.content) { console.log(lastMsg.content); } } ``` ```json theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ { name: 'list-tables-sql', args: {}, type: 'tool_call', id: 'call_LqsRA86SsKmzhRfSRekIQtff' } ] Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track [ { name: 'query-checker', args: { input: 'SELECT * FROM Artist LIMIT 10;' }, type: 'tool_call', id: 'call_MKBCjt4gKhl5UpnjsMHmDrBH' } ] The SQL query you provided is: \`\`\`sql SELECT * FROM Artist LIMIT 10; \`\`\` This query is straightforward and does not contain any of the common mistakes listed. It simply selects all columns from the `Artist` table and limits the result to 10 rows. Therefore, there are no mistakes to correct, and the original query can be reproduced as is: \`\`\`sql SELECT * FROM Artist LIMIT 10; \`\`\` [ { name: 'query-sql', args: { input: 'SELECT * FROM Artist LIMIT 10;' }, type: 'tool_call', id: 'call_a8MPiqXPMaN6yjN9i7rJctJo' } ] [{"ArtistId":1,"Name":"AC/DC"},{"ArtistId":2,"Name":"Accept"},{"ArtistId":3,"Name":"Aerosmith"},{"ArtistId":4,"Name":"Alanis Morissette"},{"ArtistId":5,"Name":"Alice In Chains"},{"ArtistId":6,"Name":"Antônio Carlos Jobim"},{"ArtistId":7,"Name":"Apocalyptica"},{"ArtistId":8,"Name":"Audioslave"},{"ArtistId":9,"Name":"BackBeat"},{"ArtistId":10,"Name":"Billy Cobham"}] Here are 10 artists from your database: 1. AC/DC 2. Accept 3. Aerosmith 4. Alanis Morissette 5. Alice In Chains 6. Antônio Carlos Jobim 7. Apocalyptica 8. Audioslave 9. BackBeat 10. Billy Cobham ``` *** ***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers. [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/tools/sql.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).