diff --git a/app/_hub/kong-inc/ai-proxy/how-to/llm-library-integration-guides/_langchain.md b/app/_hub/kong-inc/ai-proxy/how-to/llm-library-integration-guides/_langchain.md
new file mode 100644
index 000000000000..e44e48a899d7
--- /dev/null
+++ b/app/_hub/kong-inc/ai-proxy/how-to/llm-library-integration-guides/_langchain.md
@@ -0,0 +1,272 @@
+---
+nav_title: Langchain
+title: Set up AI Proxy with Langchain
+minimum_version: 3.9.x
+---
+
+This guide walks you through setting up the AI Proxy plugin with [Langchain](https://www.langchain.com/).
+
+{% include_cached /md/plugins-hub/ai-providers-prereqs.md snippet='intro' %}
+
+## Kong AI Gateway
+
+[Kong AI Gateway](https://konghq.com/products/kong-ai-gateway) delivers a suite of AI-specific plugins
+on top of the API Gateway platform, enabling you to:
+
+* Route a single consumer interface to multiple models, across many providers
+* Load balance similar models based on cost, latency, and other metrics/algorithms
+* Deliver a rich analytics and auditing suite for your deployments
+* Enable semantic features to protect your users, your models, and your costs
+* Provide no-code AI enhancements to your existing REST APIs
+* Leverage Kong's existing ecosystem of authentication, monitoring, and traffic-control plugins
+
+## Get Started
+
+Kong AI Gateway exchanges inference requests in the OpenAI formats - thus you can easily and quickly
+connect your existing LangChain OpenAI adaptor-based integrations directly through Kong with no code changes.
+
+You can target hundreds of models across the [supported providers](https://docs.konghq.com/hub/kong-inc/ai-proxy/),
+all from the same client-side codebase.
+
+### Create LLM Configuration
+
+Kong AI Gateway uses the same familiar service/route/plugin system as the API Gateway product,
+with a declarative setup that launches a complete gateway system configured from a single
+YAML file.
+
+Create your gateway YAML file, using the [Kong AI-Proxy Plugin](https://docs.konghq.com/hub/kong-inc/ai-proxy/),
+in this example for:
+
+* the **OpenAI** backend and **GPT-4o** model
+* the **Gemini** backend and **Google One-hosted Gemini** model
+
+```yaml
+_format_version: "3.0"
+
+# A service can hold plugins and features for "all models" you configure
+services:
+  - name: ai
+    url: https://localhost:32000  # this can be any hostname
+
+    # A route can denote a single model, or can support multiple based on the request parameters
+    routes:
+      - name: openai-gpt4o
+        paths:
+          - "/gpt4o"
+        plugins:
+          - name: ai-proxy  # ai-proxy is the core AI Gateway enabling feature
+            config:
+              route_type: llm/v1/chat
+              model:
+                provider: openai
+                name: gpt-4o
+              auth:
+                header_name: Authorization
+                header_value: "Bearer <OPENAI_KEY_HERE>"  # replace with your OpenAI key
+```
+
+Output this file to `kong.yaml`
+
+### Launch the Gateway
+
+Launch the Kong open-source gateway, loading this configuration YAML, with one command:
+
+```sh
+$ docker run -it --rm --name kong-ai -p 8000:8000 \
+    -v "$(pwd)/kong.yaml:/etc/kong/kong.yaml" \
+    -e "KONG_DECLARATIVE_CONFIG=/etc/kong/kong.yaml" \
+    -e "KONG_DATABASE=off" \
+    kong:3.8
+```
+
+#### Test it
+
+Check you are reaching GPT-4o on OpenAI correctly:
+
+```sh
+$ curl -H 'Content-Type: application/json' -d '{"messages":[{"role":"user","content":"What are you?"}]}' http://127.0.0.1:8000/gpt4o
+
+{
+  ...
+  ...
+        "content": "I am an AI language model developed by OpenAI, designed to assist with generating text-based responses and providing information on a wide range of topics. How can I assist you today?",
+  ...
+  ...
+}
+```
+
+### Execute Your LangChain Code
+
+Now you can (re-)configure your LangChain client code to point to Kong, and we should see
+identical results!
+
+First, load the LangChain SDK into your Python dependencies:
+
+```sh
+# WSL2, Linux, macOS-native:
+pip3 install -U langchain-openai
+```
+
+```sh
+# or macOS if installed via Homebrew:
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -U langchain-openai
+```
+
+Then create an `app.py` script:
+
+```python
+from langchain_openai import ChatOpenAI
+
+kong_url = "http://127.0.0.1:8000"
+kong_route = "gpt4o"
+
+llm = ChatOpenAI(
+    base_url=f'{kong_url}/{kong_route}',  # simply override the base URL from OpenAI, to Kong
+    model="gpt-4o",
+    api_key="NONE"  # set to NONE as we have not added any gateway-layer security yet
+)
+
+response = llm.invoke("What are you?")
+print(f"$ ChainAnswer:> {response.content}")
+```
+
+and run it!
+
+```sh
+$ python3 ./app.py
+```
+
+#### Custom Tool Usage
+
+Kong also supports custom tools, defined via any supported OpenAI-compatible SDK, including LangChain.
+
+With the same `kong.yaml` configuration, you can execute a simple custom tool definition:
+
+```python
+from langchain_openai import ChatOpenAI
+from langchain_core.tools import tool
+
+kong_url = "http://127.0.0.1:8000"
+kong_route = "gpt4o"
+
+@tool
+def multiply(first_int: int, second_int: int) -> int:
+    """Multiply two integers together."""
+    return first_int * second_int
+
+llm = ChatOpenAI(
+    base_url=f'{kong_url}/{kong_route}',
+    api_key="department-1-api-key"
+)
+
+llm_with_tools = llm.bind_tools([multiply])
+
+chain = llm_with_tools | (lambda x: x.tool_calls[0]["args"]) | multiply
+response = chain.invoke("What's four times 23?")
+print(f"$ ToolsAnswer:> {response}")
+```
+
+## Productionize the Gateway
+
+### Secure your AI Model
+
+We've just opened up our GPT-4o subscription to the localhost.
+
+Now add a Kong-level API key to the `kong.yaml` configuration file, which secures your published AI route, and allows your to track usage across multiple
+users, departments, paying-subscribers, or any other entity:
+
+```yaml
+_format_version: "3.0"
+
+services:
+  - name: ai
+    url: https://localhost:32000
+
+    routes:
+      - name: openai-gpt4o
+        paths:
+          - "/gpt4o"
+        plugins:
+          - name: ai-proxy
+            config:
+              route_type: llm/v1/chat
+              model:
+                provider: openai
+                name: gpt-4o
+              auth:
+                header_name: Authorization
+                header_value: "Bearer <OPENAI_KEY_HERE>"  # replace with your OpenAI key again
+
+          # Now we add a security plugin at the "individual model" scope
+          - name: key-auth
+            config:
+              key_names:
+                - Authorization
+
+# and finally a consumer with **its own API key**
+consumers:
+  - username: department-1
+    keyauth_credentials:
+      - key: "Bearer department-1-api-key"
+```
+
+and adjust your Python code accordingly:
+
+```python
+...
+...
+llm = ChatOpenAI(
+    base_url=f'{kong_url}/{kong_route}',
+    model="gpt-4o",
+    api_key="department-1-api-key"  # THIS TIME WE SET THE API KEY FOR THE CONSUMER, AS CREATED ABOVE
+)
+...
+...
+```
+
+## Observability
+
+There are two mechanisms for observability in Kong AI Gateway, depending on your deployment architecture:
+
+* Self-hosted and Kong Community users can bring their favourite JSON-log dashboard software.
+* Konnect Cloud users can use [Konnect Advanced Analytics](https://docs.konghq.com/konnect/analytics/) to automatically visualize every aspect of the AI Gateway operation.
+
+### Self-Hosting AI Gateway Observability
+
+You can use one (or more) of Kong's many [logging protocol plugins](https://docs.konghq.com/hub/?category=logging),
+sending your AI Gateway metrics and logs (in JSON format) to your chosen dashboarding software.
+
+You can choose to log metrics, input/output payloads, or both.
+
+#### Sample ELK Stack
+
+Use the [sample Elasticsearch/Logstash/Kibana stack](https://github.com/KongHQ-CX/kong-ai-gateway-observability) on GitHub 
+to see the full range of observability tools available when running LangChain applications via Kong AI Gateway.
+
+Boot it up in three steps:
+
+1. Clone the repository: `git clone https://github.com/KongHQ-CX/kong-ai-gateway-observability && cd kong-ai-gateway-observability/`
+2. Export your OpenAI API auth header (with API key) into the current shell environment: `export OPENAI_AUTH_HEADER="Bearer sk-proj-......"`
+3. Start the stack: `docker compose up`
+
+Now you can run the same LangChain code as in the previous step(s), visualizing exactly what's happening in Kibana, at:
+http://localhost:5601/app/dashboards#/view/aa8e4cb0-9566-11ef-beb2-c361d8db17a8
+
+**You can generate analytics over every AI request executed by LangChain/Kong:**
+
+![Kong API Stats Example](/assets/images/guides/llm-libraries/kong-analytics.png)
+
+**and even, if enabled, every request and response, as granular as 'who-is-executing-what-when':**
+
+![Kong API Logs Example](/assets/images/guides/llm-libraries/kong-logs.png)
+
+*This uses the [Kong HTTP-Log plugin](https://docs.konghq.com/hub/kong-inc/http-log/) 
+to send all AI statistics and payloads to Logstash.*
+
+## Prompt Tuning, Audit, and Cost Control Features
+
+Now that you have your LangChain codebase calling one or many LLMs via Kong AI Gateway, you can 
+snap-in as many features as required, by harnessing 
+[Kong's growing array of AI plugins](https://docs.konghq.com/hub/?category=ai).
diff --git a/app/assets/images/guides/llm-libraries/kong-analytics.png b/app/assets/images/guides/llm-libraries/kong-analytics.png
new file mode 100644
index 000000000000..708dbb81155b
Binary files /dev/null and b/app/assets/images/guides/llm-libraries/kong-analytics.png differ
diff --git a/app/assets/images/guides/llm-libraries/kong-logs.png b/app/assets/images/guides/llm-libraries/kong-logs.png
new file mode 100644
index 000000000000..23dd43ee9c15
Binary files /dev/null and b/app/assets/images/guides/llm-libraries/kong-logs.png differ