diff --git a/README.md b/README.md
index 852cf464..e4731a7d 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 # Apify Actor templates
 
 > This repository stores boilerplate templates and code examples for [Apify Actor](https://apify.com/actors).
-> The purpose of these templates is to help devlopers get started with actor development on the Apify platform.
+> The purpose of these templates is to help developers get started with actor development on the Apify platform.
 
 ## How to use the templates
 
diff --git a/templates/js-langchain/.eslintrc b/templates/js-langchain/.eslintrc
new file mode 100644
index 00000000..6c22ab8b
--- /dev/null
+++ b/templates/js-langchain/.eslintrc
@@ -0,0 +1,4 @@
+{
+    "extends": "@apify",
+    "root": true
+}
diff --git a/templates/js-langchain/README.md b/templates/js-langchain/README.md
index 030a3248..6a5c2bdc 100644
--- a/templates/js-langchain/README.md
+++ b/templates/js-langchain/README.md
@@ -1,7 +1,6 @@
 ## LangChain.js template
 
 > LangChain is a framework for developing applications powered by language models.
-> 
 
 This example template illustrates how to use LangChain.js with Apify to crawl the web data, vectorize them, and prompt the OpenAI model. All of this is within a single Apify Actor and slightly over a hundred lines of code.
 
@@ -33,7 +32,6 @@ To be able to run this template both locally and on the Apify platform, you need
 ## Production use
 
 > This serves purely as an example of the whole pipeline.
-> 
 
 For production use, we recommend you to:
 
@@ -49,6 +47,5 @@ For production use, we recommend you to:
 - [Video guide on getting data using Apify API](https://www.youtube.com/watch?v=ViYYDHSBAKM)
 - A short guide on [how to create web scrapers using code templates](https://www.youtube.com/watch?v=u-i-Korzf8w)
 
-<br>
-[langchain content crawler](https://www.youtube.com/watch?v=8uvHH-ocSes)
+[Web Scraping Data for Generative AI](https://www.youtube.com/watch?v=8uvHH-ocSes)
 
diff --git a/templates/js-langchain/package.json b/templates/js-langchain/package.json
index ec2abbe5..ee75a053 100644
--- a/templates/js-langchain/package.json
+++ b/templates/js-langchain/package.json
@@ -1,21 +1,30 @@
 {
     "name": "project-langchain",
-    "version": "0.0.1",
+    "version": "0.0.2",
     "type": "module",
     "description": "This is a boilerplate of an Apify actor.",
     "engines": {
         "node": ">=18.0.0"
     },
     "dependencies": {
+        "@langchain/community": "^0.2.32",
+        "@langchain/core": "^0.2.31",
+        "@langchain/openai": "^0.2.10",
         "apify": "^3.1.10",
-        "hnswlib-node": "^1.4.2",
-        "langchain": "^0.1.11",
+        "hnswlib-node": "^3.0.0",
+        "langchain": "^0.2.18",
         "tar": "^6.1.14"
     },
     "scripts": {
         "start": "node src/main.js",
+        "lint": "eslint ./src --ext .js,.jsx",
+        "lint:fix": "eslint ./src --ext .js,.jsx --fix",
         "test": "echo \"Error: oops, the actor has no tests yet, sad!\" && exit 1"
     },
     "author": "It's not you it's me",
-    "license": "ISC"
+    "license": "ISC",
+    "devDependencies": {
+        "@apify/eslint-config": "^0.4.0",
+        "eslint": "^8.57.0"
+    }
 }
diff --git a/templates/js-langchain/src/main.js b/templates/js-langchain/src/main.js
index 91e4ec72..797cc6bb 100644
--- a/templates/js-langchain/src/main.js
+++ b/templates/js-langchain/src/main.js
@@ -1,24 +1,31 @@
-import { Actor } from 'apify';
-import { ApifyDatasetLoader } from 'langchain/document_loaders/web/apify_dataset';
-import { Document } from 'langchain/document';
-import { HNSWLib } from 'langchain/vectorstores/hnswlib';
-import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
-import { RetrievalQAChain } from 'langchain/chains';
-import { OpenAI } from 'langchain/llms/openai';
 import { rm } from 'node:fs/promises';
 
-// this is ESM project, and as such, it requires you to specify extensions in your relative imports
-// read more about this here: https://nodejs.org/docs/latest-v18.x/api/esm.html#mandatory-file-extensions
+import { ApifyDatasetLoader } from '@langchain/community/document_loaders/web/apify_dataset';
+import { HNSWLib } from '@langchain/community/vectorstores/hnswlib';
+import { ChatPromptTemplate } from '@langchain/core/prompts';
+import { OpenAI, OpenAIEmbeddings } from '@langchain/openai';
+import { Actor, log } from 'apify';
+import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';
+import { createRetrievalChain } from 'langchain/chains/retrieval';
+import { Document } from 'langchain/document';
+
+// This is ESM project, and as such, it requires you to specify extensions in your relative imports.
+// Read more about this here: https://nodejs.org/docs/latest-v18.x/api/esm.html#mandatory-file-extensions
 import { retrieveVectorIndex, cacheVectorIndex } from './vector_index_cache.js';
 
 await Actor.init();
 
-// There are 2 steps you need to proceed first in order to be able to run this template:
-// 1. If you are running template locally then you need to authenticate to Apify platform by calling `apify login` in your terminal. Without this, you won't be able to run the required Website Content Crawler Actor to gather the data.
-// 2. Configure the OPENAI_API_KEY environment variable (https://docs.apify.com/cli/docs/vars#set-up-environment-variables-in-apify-console) with your OpenAI API key you obtain at https://platform.openai.com/account/api-keys.
+// Follow these steps to run this template:
+// 1. If running locally, authenticate to the Apify platform by executing `apify login` in your terminal.
+//    This is necessary to run the Website Content Crawler Actor for data gathering.
+// 2. Set the `OPENAI_API_KEY` environment variable with your OpenAI API key, which can be obtained from
+//    https://platform.openai.com/account/api-keys. Refer to
+//    https://docs.apify.com/cli/docs/vars#set-up-environment-variables-in-apify-console for guidance
+//    on setting environment variables.
 const { OPENAI_API_KEY, APIFY_TOKEN } = process.env;
 
-// You can configure the input for the Actor in the Apify UI when running on the Apify platform or editing storage/key_value_stores/default/INPUT.json when running locally.
+// You can configure the input for the Actor in the Apify UI when running on the Apify platform or editing
+// storage/key_value_stores/default/INPUT.json when running locally.
 const {
     startUrls = [{ url: 'https://wikipedia.com' }],
     maxCrawlPages = 3,
@@ -30,10 +37,14 @@ const {
 // Local directory where the vector index will be stored.
 const VECTOR_INDEX_PATH = './vector_index';
 
+const prompt = ChatPromptTemplate.fromTemplate(
+    `Answer the user's question: {input} based on the following context {context}`,
+);
+
 if (!openAIApiKey) throw new Error('Please configure the OPENAI_API_KEY as environment variable or enter it into the input!');
 if (!APIFY_TOKEN) throw new Error('Please configure the APIFY_TOKEN environment variable! Call `apify login` in your terminal to authenticate.');
 
-// Now we want to creare a vector index from the crawled documents.
+// Now we want to create a vector index from the crawled documents.
 // Following object represents an input for the https://apify.com/apify/website-content-crawler actor that crawls the website to gather the data.
 const websiteContentCrawlerInput = { startUrls, maxCrawlPages };
 
@@ -42,12 +53,12 @@ let vectorStore;
 
 // First, we check if the vector index is already cached. If not, we run the website content crawler to get the documents.
 // By setting up forceRecrawl=true you can enforce a re-scrape of the website content and re-creation of the vector index.
-console.log('Fetching cached vector index from key-value store...');
+log.info('Fetching cached vector index from key-value store...');
 const reinitializeIndex = forceRecrawl || !(await retrieveVectorIndex(websiteContentCrawlerInput));
 if (reinitializeIndex) {
     // Run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.
-    console.log('Vector index was not found.')
-    console.log('Running apify/website-content-crawler to gather the data...');
+    log.info('Vector index was not found.');
+    log.info('Running apify/website-content-crawler to gather the data...');
     const loader = await ApifyDatasetLoader.fromActorCall(
         'apify/website-content-crawler',
         websiteContentCrawlerInput,
@@ -57,41 +68,47 @@ if (reinitializeIndex) {
                 metadata: { source: item.url },
             }),
             clientOptions: { token: APIFY_TOKEN },
-        }
+        },
     );
 
     // Initialize the vector index from the crawled documents.
-    console.log('Feeding vector index with crawling results...');
+    log.info('Feeding vector index with crawling results...');
     const docs = await loader.load();
     vectorStore = await HNSWLib.fromDocuments(
         docs,
-        new OpenAIEmbeddings({ openAIApiKey })
+        new OpenAIEmbeddings({ openAIApiKey }),
     );
 
     // Save the vector index to the key-value store so that we can skip this phase in the next run.
-    console.log('Saving vector index to the disk...')
+    log.info('Saving vector index to the disk...');
     await vectorStore.save(VECTOR_INDEX_PATH);
     await cacheVectorIndex(websiteContentCrawlerInput, VECTOR_INDEX_PATH);
 }
 
 // Load the vector index from the disk if not already initialized above.
 if (!vectorStore) {
-    console.log('Initializing the vector store...');
-    vectorStore = await HNSWLib.load(
-        VECTOR_INDEX_PATH,
-        new OpenAIEmbeddings({ openAIApiKey })
-    );
+    log.info('Initializing the vector store...');
+    vectorStore = await HNSWLib.load(VECTOR_INDEX_PATH, new OpenAIEmbeddings({ openAIApiKey }));
 }
 
 // Next, create the retrieval chain and enter a query:
-console.log('Asking model a question...');
-const model = new OpenAI({ openAIApiKey });
-const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever(), {
+const llm = new OpenAI({ openAIApiKey });
+const combineDocsChain = await createStuffDocumentsChain({
+    llm,
+    prompt,
+});
+
+const chain = await createRetrievalChain({
+    combineDocsChain,
+    retriever: vectorStore.asRetriever(),
     returnSourceDocuments: true,
 });
-const res = await chain.call({ query });
 
-console.log(`\n${res.text}\n`);
+log.info('Asking model a question...');
+const res = await chain.invoke({ input: query });
+
+log.info(`Question: ${query}`);
+log.info(`Model response: ${res.answer}`);
 
 // Remove the vector index directory as we have it cached in the key-value store for the next time.
 await rm(VECTOR_INDEX_PATH, { recursive: true });
diff --git a/templates/js-langchain/src/vector_index_cache.js b/templates/js-langchain/src/vector_index_cache.js
index 865e6096..6ef72eda 100644
--- a/templates/js-langchain/src/vector_index_cache.js
+++ b/templates/js-langchain/src/vector_index_cache.js
@@ -1,7 +1,8 @@
-import { Actor } from 'apify';
 import { createHash } from 'crypto';
-import { finished } from 'node:stream/promises';
 import { Readable } from 'node:stream';
+import { finished } from 'node:stream/promises';
+
+import { Actor } from 'apify';
 import tar from 'tar';
 
 const VECTOR_INDEX_CACHE_STORE_NAME = 'vector-index-cache';