diff --git a/docs/extras/ecosystem/integrations/jina.mdx b/docs/extras/ecosystem/integrations/jina.mdx
index be278bf7d4..7cbe5a41d4 100644
--- a/docs/extras/ecosystem/integrations/jina.mdx
+++ b/docs/extras/ecosystem/integrations/jina.mdx
@@ -16,3 +16,59 @@ There exists a Jina Embeddings wrapper, which you can access with
 from langchain.embeddings import JinaEmbeddings
 ```
 For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/text_embedding/integrations/jina.html)
+
+## Deployment
+
+[Langchain-serve](https://github.com/jina-ai/langchain-serve), powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots. 
+
+### Usage
+
+Install the package from PyPI. 
+
+```bash
+pip install langchain-serve
+```
+
+Wrap your LangChain app with the `@serving` decorator. 
+
+```python
+# app.py
+from lcserve import serving
+
+@serving
+def ask(input: str) -> str:
+    from langchain import LLMChain, OpenAI
+    from langchain.agents import AgentExecutor, ZeroShotAgent
+    
+    tools = [...] # list of tools
+    prompt = ZeroShotAgent.create_prompt(
+        tools, input_variables=["input", "agent_scratchpad"],
+    )
+    llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
+    agent = ZeroShotAgent(
+        llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools]
+    )
+    agent_executor = AgentExecutor.from_agent_and_tools(
+        agent=agent, 
+        tools=tools, 
+        verbose=True,
+    )
+    return agent_executor.run(input)
+```
+
+Deploy on Jina AI Cloud with `lc-serve deploy jcloud app`. Once deployed, we can send a POST request to the API endpoint to get a response.
+
+```bash
+curl -X 'POST' 'https://<your-app>.wolf.jina.ai/ask' \
+ -d '{
+  "input": "Your Quesion here?",
+  "envs": {
+     "OPENAI_API_KEY": "sk-***"
+  }
+}'
+```
+
+You can also self-host the app on your infrastructure with Docker-compose or Kubernetes. See [here](https://github.com/jina-ai/langchain-serve#-self-host-llm-apps-with-docker-compose-or-kubernetes) for more details.
+
+
+Langchain-serve also allows to deploy the apps with WebSocket APIs and Slack Bots both on [Jina AI Cloud](https://cloud.jina.ai/) or self-hosted infrastructure. 
diff --git a/docs/extras/guides/deployments/index.mdx b/docs/extras/guides/deployments/index.mdx
index d56f3365ba..09841cff14 100644
--- a/docs/extras/guides/deployments/index.mdx
+++ b/docs/extras/guides/deployments/index.mdx
@@ -24,6 +24,7 @@ Understanding these components is crucial when assessing serving systems. LangCh
 - [BentoML](https://github.com/bentoml/BentoML)
 - [OpenLLM](/docs/ecosystem/integrations/openllm.html)
 - [Modal](/docs/ecosystem/integrations/modal.html)
+- [Jina](/docs/ecosystem/integrations/jina.html#deployment)
 
 These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
 
diff --git a/docs/extras/guides/deployments/template_repos.mdx b/docs/extras/guides/deployments/template_repos.mdx
index aee56a203b..f1f025fff9 100644
--- a/docs/extras/guides/deployments/template_repos.mdx
+++ b/docs/extras/guides/deployments/template_repos.mdx
@@ -61,7 +61,7 @@ This repository contains LangChain adapters for Steamship, enabling LangChain de
 
 ## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
 
-This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
+This repository allows users to deploy any LangChain app as REST/WebSocket APIs or, as Slack Bots with ease. Benefit from the scalability and serverless architecture of Jina AI Cloud, or deploy on-premise with Kubernetes.
 
 ## [BentoML](https://github.com/ssheng/BentoChain)