diff --git a/sources/platform/integrations/ai/langchain.md b/sources/platform/integrations/ai/langchain.md
index f5f8455918..3a2b5b793b 100644
--- a/sources/platform/integrations/ai/langchain.md
+++ b/sources/platform/integrations/ai/langchain.md
@@ -15,8 +15,8 @@ slug: /integrations/langchain
 In this example, we'll use the [Website Content Crawler](https://apify.com/apify/website-content-crawler) Actor, which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs and extract text content from the web pages.
 Then we feed the documents into a vector index and answer questions from it.
 
-This example focuses on how to integrate Apify with LangChain using the Python language,
-but if you prefer to use JavaScript, you can follow the same steps in the [JavaScript LangChain documentation](https://js.langchain.com/docs/modules/indexes/document_loaders/examples/web_loaders/apify_dataset).
+This example demonstrates how to integrate Apify with LangChain using the Python language.
+If you prefer to use JavaScript, you can follow the  [JavaScript LangChain documentation](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/apify_dataset/).
 
 Before we start with the integration, we need to install all dependencies:
 
@@ -122,11 +122,27 @@ answer: LangChain is a framework for developing applications powered by language
 source: https://python.langchain.com
 ```
 
-LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules you can use to build language model applications. It also provides chains and agents with memory capabilities.
+LangChain is a standard interface through which you can interact with a variety of large language models (LLMs).
+It provides modules you can use to build language model applications as well as chains and agents with memory capabilities.
+
+You can use all of Apify’s Actors as document loaders in LangChain.
+For example, to incorporate web browsing functionality, you can use the [RAG-Web-Browser Actor](https://apify.com/apify/rag-web-browser).
+This allows you to either crawl and scrape top pages from Google Search results or directly scrape text content from a URL and return it as Markdown.
+To set this up, change the `actor_id` to `apify/rag-web-browser` and specify the `run_input`.
+
+```python
+loader = apify.call_actor(
+    actor_id="apify/rag-web-browser",
+    run_input={"query": "apify langchain web browser", "maxResults": 3},
+    dataset_mapping_function=lambda item: Document(page_content=item["text"] or "", metadata={"source": item["metadata"]["url"]}),
+)
+print("Documents:", loader.load())
+```
+
+Similarly, you can use other Apify Actors to load data into LangChain and query the vector index.
 
 ## Resources
 
-- https://python.langchain.com/docs/get_started/introduction
-- https://python.langchain.com/docs/integrations/document_loaders/apify_dataset
-- https://python.langchain.com/docs/integrations/providers/apify
-- https://python.langchain.com/docs/modules/model_io/llms/
+- [LangChain introduction](https://python.langchain.com/docs/get_started/introduction)
+- [Apify Dataset loader](https://python.langchain.com/docs/integrations/document_loaders/apify_dataset)
+- [LangChain Apify Provider](https://python.langchain.com/docs/integrations/providers/apify)