diff --git a/src/oss/javascript/integrations/chat/ibm.mdx b/src/oss/javascript/integrations/chat/ibm.mdx
index 2ef4949ba8..faaf13c740 100644
--- a/src/oss/javascript/integrations/chat/ibm.mdx
+++ b/src/oss/javascript/integrations/chat/ibm.mdx
@@ -150,6 +150,30 @@ Note:
 - You must provide `spaceId`, `projectId` or `idOrName`(deployment id) unless you use lighweight engine which works without specifying either (refer to [watsonx.ai docs](https://www.ibm.com/docs/en/cloud-paks/cp-data/5.0.x?topic=install-choosing-installation-mode))
 - Depending on the region of your provisioned service instance, use correct serviceUrl.
 
+### Using Model Gateway
+
+```typescript
+import { ChatWatsonx } from "@langchain/community/chat_models/ibm";
+const props = {
+  maxTokens: 200,
+  temperature: 0.5
+};
+
+const instance = new ChatWatsonx({
+  version: "YYYY-MM-DD",
+  serviceUrl: process.env.API_URL,
+  model: "<ALIAS_MODEL_ID>",
+  modelGateway: true,
+  ...props
+});
+```
+
+To use model gateway with Langchain, you need to previously create a provider and add model via `@ibm-cloud/watsonx-ai` SDK or `watsonx.ai` API. Follow this documentation:
+- [API](https://cloud.ibm.com/apidocs/watsonx-ai#create-watsonxai-provider).
+- [SDK](https://ibm.github.io/watsonx-ai-node-sdk/modules/1_7_x.gateway.html).
+
+
+
 ## Invocation
 
 ```javascript
diff --git a/src/oss/javascript/integrations/llms/ibm.mdx b/src/oss/javascript/integrations/llms/ibm.mdx
index d29b93929a..7eb0657fa7 100644
--- a/src/oss/javascript/integrations/llms/ibm.mdx
+++ b/src/oss/javascript/integrations/llms/ibm.mdx
@@ -149,6 +149,32 @@ Note:
 - Depending on the region of your provisioned service instance, use correct serviceUrl.
 - You need to specify the model you want to use for inferencing through model_id.
 
+### Using Model Gateway
+```typescript
+import { WatsonxLLM } from "@langchain/community/llms/ibm";
+
+const props = {
+  decoding_method: "sample",
+  maxNewTokens: 100,
+  minNewTokens: 1,
+  temperature: 0.5,
+  topK: 50,
+  topP: 1,
+};
+
+const instance = new WatsonxLLM({
+  version: "YYYY-MM-DD",
+  serviceUrl: process.env.API_URL,
+  model: "<MODEL_ID>",
+  modelGateway: true,
+  ...props,
+});
+```
+
+To use model gateway with Langchain, you need to previously create a provider and add model via `@ibm-cloud/watsonx-ai` SDK or `watsonx.ai` API. Follow this documentation:
+- [API](https://cloud.ibm.com/apidocs/watsonx-ai#create-watsonxai-provider).
+- [SDK](https://ibm.github.io/watsonx-ai-node-sdk/modules/1_7_x.gateway.html).
+
 ## Invocation and generation
 
 ```javascript
diff --git a/src/oss/javascript/integrations/text_embedding/ibm.mdx b/src/oss/javascript/integrations/text_embedding/ibm.mdx
index e7762e362a..a36918b99d 100644
--- a/src/oss/javascript/integrations/text_embedding/ibm.mdx
+++ b/src/oss/javascript/integrations/text_embedding/ibm.mdx
@@ -138,6 +138,18 @@ Note:
 - You must provide `spaceId` or `projectId` in order to proceed.
 - Depending on the region of your provisioned service instance, use correct serviceUrl.
 
+### Model Gateway
+```typescript
+import { WatsonxEmbeddings } from "@langchain/community/embeddings/ibm";
+
+const instance = new WatsonxEmbeddings({
+  version: "YYYY-MM-DD",
+  serviceUrl: process.env.API_URL,
+  model: "<ALIAS_MODEL_ID>",
+  modelGateway: true,
+});
+```
+
 ## Indexing and Retrieval
 
 Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [**Learn** tab](/oss/learn/).