diff --git a/libs/langchain-community/src/chat_models/ibm.ts b/libs/langchain-community/src/chat_models/ibm.ts
index d0fae3ac15ce..dd468909a886 100644
--- a/libs/langchain-community/src/chat_models/ibm.ts
+++ b/libs/langchain-community/src/chat_models/ibm.ts
@@ -1,3 +1,5 @@
+/* eslint-disable @typescript-eslint/no-explicit-any */
+
 import {
   AIMessage,
   AIMessageChunk,
diff --git a/libs/langchain-community/src/chat_models/tests/ibm.test.ts b/libs/langchain-community/src/chat_models/tests/ibm.test.ts
index 3fea7de8504b..8e04c1c26c6b 100644
--- a/libs/langchain-community/src/chat_models/tests/ibm.test.ts
+++ b/libs/langchain-community/src/chat_models/tests/ibm.test.ts
@@ -1,4 +1,5 @@
 /* eslint-disable no-process-env */
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import WatsonxAiMlVml_v1 from "@ibm-cloud/watsonx-ai/dist/watsonx-ai-ml/vml_v1.js";
 import { ChatWatsonx, ChatWatsonxInput, WatsonxCallParams } from "../ibm.js";
 import { authenticateAndSetInstance } from "../../utils/ibm.js";
diff --git a/libs/langchain-community/src/embeddings/tests/ibm.test.ts b/libs/langchain-community/src/embeddings/tests/ibm.test.ts
index affa8491807f..05f033f6f1af 100644
--- a/libs/langchain-community/src/embeddings/tests/ibm.test.ts
+++ b/libs/langchain-community/src/embeddings/tests/ibm.test.ts
@@ -1,4 +1,5 @@
 /* eslint-disable no-process-env */
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import { testProperties } from "../../llms/tests/ibm.test.js";
 import { WatsonxEmbeddings } from "../ibm.js";
 
diff --git a/libs/langchain-community/src/llms/tests/ibm.test.ts b/libs/langchain-community/src/llms/tests/ibm.test.ts
index e0d6f3e4b521..7dfaecd6361c 100644
--- a/libs/langchain-community/src/llms/tests/ibm.test.ts
+++ b/libs/langchain-community/src/llms/tests/ibm.test.ts
@@ -1,4 +1,5 @@
 /* eslint-disable no-process-env */
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import WatsonxAiMlVml_v1 from "@ibm-cloud/watsonx-ai/dist/watsonx-ai-ml/vml_v1.js";
 import { WatsonxLLM, WatsonxInputLLM } from "../ibm.js";
 import { authenticateAndSetInstance } from "../../utils/ibm.js";
diff --git a/libs/langchain-community/src/utils/ibm.ts b/libs/langchain-community/src/utils/ibm.ts
index acbb86f1a304..ccbe1204ef60 100644
--- a/libs/langchain-community/src/utils/ibm.ts
+++ b/libs/langchain-community/src/utils/ibm.ts
@@ -1,3 +1,4 @@
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import { WatsonXAI } from "@ibm-cloud/watsonx-ai";
 import {
   IamAuthenticator,
diff --git a/libs/langchain-community/src/vectorstores/libsql.ts b/libs/langchain-community/src/vectorstores/libsql.ts
index 05c77da7489c..dfdaeaca167b 100644
--- a/libs/langchain-community/src/vectorstores/libsql.ts
+++ b/libs/langchain-community/src/vectorstores/libsql.ts
@@ -1,7 +1,7 @@
-import type { Client } from "@libsql/client";
-import { VectorStore } from "@langchain/core/vectorstores";
-import type { EmbeddingsInterface } from "@langchain/core/embeddings";
 import { Document } from "@langchain/core/documents";
+import type { EmbeddingsInterface } from "@langchain/core/embeddings";
+import { VectorStore } from "@langchain/core/vectorstores";
+import type { Client, InStatement } from "@libsql/client";
 
 /**
  * Interface for LibSQLVectorStore configuration options.
@@ -82,23 +82,17 @@ export class LibSQLVectorStore extends VectorStore {
 
     for (let i = 0; i < rows.length; i += batchSize) {
       const chunk = rows.slice(i, i + batchSize);
-      const insertQueries = chunk.map((row) => ({
-        sql: `INSERT INTO ${this.table} (content, metadata, ${this.column}) VALUES (?, ?, ?) RETURNING id`,
-        args: [row.content, row.metadata, row.embedding],
+
+      const insertQueries: InStatement[] = chunk.map((row) => ({
+        sql: `INSERT INTO ${this.table} (content, metadata, ${this.column}) VALUES (:content, :metadata, vector(:embedding)) RETURNING ${this.table}.rowid AS id`,
+        args: row,
       }));
 
       const results = await this.db.batch(insertQueries);
 
-      for (const result of results) {
-        if (
-          result &&
-          result.rows &&
-          result.rows.length > 0 &&
-          result.rows[0].id != null
-        ) {
-          ids.push(result.rows[0].id.toString());
-        }
-      }
+      ids.push(
+        ...results.flatMap((result) => result.rows.map((row) => String(row.id)))
+      );
     }
 
     return ids;
@@ -123,11 +117,12 @@ export class LibSQLVectorStore extends VectorStore {
 
     const queryVector = `[${query.join(",")}]`;
 
-    const sql = `
-      SELECT ${this.table}.id, ${this.table}.content, ${this.table}.metadata, vector_distance_cos(${this.table}.${this.column}, vector('${queryVector}')) AS distance
-      FROM vector_top_k('idx_${this.table}_${this.column}', vector('${queryVector}'), ${k}) AS top_k
-      JOIN ${this.table} ON top_k.rowid = ${this.table}.id
-    `;
+    const sql: InStatement = {
+      sql: `SELECT ${this.table}.rowid as id, ${this.table}.content, ${this.table}.metadata, vector_distance_cos(${this.table}.${this.column}, vector(:queryVector)) AS distance
+      FROM vector_top_k('idx_${this.table}_${this.column}', vector(:queryVector), CAST(:k AS INTEGER)) as top_k
+      JOIN ${this.table} ON top_k.rowid = ${this.table}.rowid`,
+      args: { queryVector, k },
+    };
 
     const results = await this.db.execute(sql);
 
@@ -136,7 +131,7 @@ export class LibSQLVectorStore extends VectorStore {
       const metadata = JSON.parse(row.metadata);
 
       const doc = new Document({
-        id: row.id,
+        id: String(row.id),
         metadata,
         pageContent: row.content,
       });
@@ -145,6 +140,32 @@ export class LibSQLVectorStore extends VectorStore {
     });
   }
 
+  /**
+   * Deletes vectors from the store.
+   * @param {Object} params - Delete parameters.
+   * @param {string[] | number[]} [params.ids] - The ids of the vectors to delete.
+   * @returns {Promise<void>}
+   */
+  async delete(params: {
+    ids?: string[] | number[];
+    deleteAll?: boolean;
+  }): Promise<void> {
+    if (params.deleteAll) {
+      await this.db.execute(`DELETE FROM ${this.table}`);
+    } else if (params.ids !== undefined) {
+      await this.db.batch(
+        params.ids.map((id) => ({
+          sql: `DELETE FROM ${this.table} WHERE rowid = :id`,
+          args: { id },
+        }))
+      );
+    } else {
+      throw new Error(
+        `You must provide an "ids" parameter or a "deleteAll" parameter.`
+      );
+    }
+  }
+
   /**
    * Creates a new LibSQLVectorStore instance from texts.
    * @param {string[]} texts - The texts to add to the store.
diff --git a/libs/langchain-community/src/vectorstores/tests/libsql.int.test.ts b/libs/langchain-community/src/vectorstores/tests/libsql.int.test.ts
index 63fe6cbe2df7..5dbec055afff 100644
--- a/libs/langchain-community/src/vectorstores/tests/libsql.int.test.ts
+++ b/libs/langchain-community/src/vectorstores/tests/libsql.int.test.ts
@@ -1,13 +1,14 @@
 /* eslint-disable no-process-env */
 /* eslint-disable @typescript-eslint/no-non-null-assertion */
 import { expect, test } from "@jest/globals";
-import { OpenAIEmbeddings } from "@langchain/openai";
 import { Document } from "@langchain/core/documents";
+import { OpenAIEmbeddings } from "@langchain/openai";
 import { createClient } from "@libsql/client";
+import { SyntheticEmbeddings } from "@langchain/core/utils/testing";
+import fs from "node:fs";
+import { LibSQLVectorStore, LibSQLVectorStoreArgs } from "../libsql.js";
 
-import { LibSQLVectorStore } from "../libsql.js";
-
-test("can create and query", async () => {
+test("can create and query (cloud)", async () => {
   const client = createClient({
     url: process.env.LIBSQL_URL!,
     authToken: process.env.LIBSQL_AUTH_TOKEN,
@@ -43,3 +44,290 @@ test("can create and query", async () => {
   const results = await vectorStore.similaritySearchWithScore("added first", 4);
   expect(results.length).toBe(4);
 });
+
+describe("LibSQLVectorStore (local)", () => {
+  const client = createClient({
+    url: "file:store.db",
+  });
+
+  const config: LibSQLVectorStoreArgs = {
+    db: client,
+  };
+
+  const embeddings = new SyntheticEmbeddings({
+    vectorSize: 1024,
+  });
+
+  afterAll(async () => {
+    await client.close();
+    if (fs.existsSync("store.db")) {
+      fs.unlinkSync("store.db");
+    }
+  });
+
+  test("a document with content can be added", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "hello",
+        metadata: { a: 1 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(1);
+
+    const [id] = ids;
+
+    expect(typeof id).toBe("string");
+
+    const resultSet = await client.execute(`SELECT * FROM vectors`);
+
+    expect(resultSet.rows).toHaveLength(1);
+
+    const [row] = resultSet.rows;
+
+    expect(row.content).toBe("hello");
+    expect(JSON.parse(row.metadata as string)).toEqual({ a: 1 });
+  });
+
+  test("a document with spaces in the content can be added", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "hello world",
+        metadata: { a: 1 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(1);
+
+    const [id] = ids;
+
+    expect(typeof id).toBe("string");
+
+    const resultSet = await client.execute(`SELECT * FROM vectors`);
+
+    expect(resultSet.rows).toHaveLength(1);
+
+    const [row] = resultSet.rows;
+
+    expect(row.content).toBe("hello world");
+    expect(JSON.parse(row.metadata as string)).toEqual({ a: 1 });
+  });
+
+  test("a similarity search can be performed", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "the quick brown fox",
+        metadata: { a: 1 },
+      },
+      {
+        pageContent: "jumped over the lazy dog",
+        metadata: { a: 2 },
+      },
+      {
+        pageContent: "hello world",
+        metadata: { a: 3 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(3);
+    expect(ids.every((id) => typeof id === "string")).toBe(true);
+
+    const results1 = await store.similaritySearch("the quick brown dog", 2);
+
+    expect(results1).toHaveLength(2);
+    expect(
+      results1.map((result) => result.id).every((id) => typeof id === "string")
+    ).toBe(true);
+
+    const results2 = await store.similaritySearch("hello");
+
+    expect(results2).toHaveLength(3);
+    expect(
+      results2.map((result) => result.id).every((id) => typeof id === "string")
+    ).toBe(true);
+  });
+
+  test("a document can be deleted by id", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "the quick brown fox",
+        metadata: { a: 1 },
+      },
+      {
+        pageContent: "jumped over the lazy dog",
+        metadata: { a: 2 },
+      },
+      {
+        pageContent: "hello world",
+        metadata: { a: 3 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(3);
+    expect(ids.every((id) => typeof id === "string")).toBe(true);
+
+    const [id1, id2] = ids;
+
+    await store.delete({ ids: [id1, id2] });
+
+    const resultSet = await client.execute(`SELECT * FROM vectors`);
+
+    expect(resultSet.rows).toHaveLength(1);
+
+    const [row] = resultSet.rows;
+
+    expect(row.content).toBe("hello world");
+    expect(JSON.parse(row.metadata as string)).toEqual({ a: 3 });
+  });
+
+  test("all documents can be deleted", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "the quick brown fox",
+        metadata: { a: 1 },
+      },
+      {
+        pageContent: "jumped over the lazy dog",
+        metadata: { a: 2 },
+      },
+      {
+        pageContent: "hello world",
+        metadata: { a: 3 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(3);
+    expect(ids.every((id) => typeof id === "string")).toBe(true);
+
+    await store.delete({
+      deleteAll: true,
+    });
+
+    const resultSet = await client.execute(`SELECT * FROM vectors`);
+
+    expect(resultSet.rows).toHaveLength(0);
+  });
+
+  test("the table can have a custom id column name", async () => {
+    await client.batch([
+      `DROP TABLE IF EXISTS vectors;`,
+      `CREATE TABLE IF NOT EXISTS vectors (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        content TEXT,
+        metadata JSON,
+        embedding F32_BLOB(1024)
+      );`,
+      `CREATE INDEX IF NOT EXISTS idx_vectors_embedding
+        ON vectors (libsql_vector_idx(embedding));`,
+    ]);
+
+    const store = new LibSQLVectorStore(embeddings, config);
+
+    const ids = await store.addDocuments([
+      {
+        pageContent: "the quick brown fox",
+        metadata: { a: 1 },
+      },
+      {
+        pageContent: "jumped over the lazy dog",
+        metadata: { a: 2 },
+      },
+      {
+        pageContent: "hello world",
+        metadata: { a: 3 },
+      },
+    ]);
+
+    expect(ids).toHaveLength(3);
+    expect(ids).toEqual(["1", "2", "3"]);
+
+    const results = await store.similaritySearch("the quick brown dog", 2);
+
+    expect(results).toHaveLength(2);
+    expect(results.map((result) => result.pageContent)).toEqual([
+      "the quick brown fox",
+      "jumped over the lazy dog",
+    ]);
+    expect(
+      results.map((result) => result.id).every((id) => typeof id === "string")
+    ).toBe(true);
+
+    const [id1, id2] = ids;
+
+    await store.delete({ ids: [id1, id2] });
+
+    const resultSet = await client.execute(`SELECT * FROM vectors`);
+
+    expect(resultSet.rows).toHaveLength(1);
+
+    const [row] = resultSet.rows;
+
+    expect(row.content).toBe("hello world");
+    expect(JSON.parse(row.metadata as string)).toEqual({ a: 3 });
+  });
+});