diff --git a/src/oss/langchain/overview.mdx b/src/oss/langchain/overview.mdx
index 03172686aa..e0f9b5f0af 100644
--- a/src/oss/langchain/overview.mdx
+++ b/src/oss/langchain/overview.mdx
@@ -29,47 +29,11 @@ We recommend you use LangChain if you want to quickly build agents and autonomou
 
 LangChain [agents](/oss/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. You do not need to know LangGraph for basic LangChain agent usage.
 
-## <Icon icon="download" size={20} /> Install
-
-:::python
-<CodeGroup>
-```bash pip
-pip install -U langchain
-# Requires Python 3.10+
-```
-
-```bash uv
-uv add langchain
-# Requires Python 3.10+
-```
-</CodeGroup>
-:::
-:::js
-<CodeGroup>
-```bash npm
-npm install langchain @langchain/core
-```
-
-```bash pnpm
-pnpm add langchain @langchain/core
-```
-
-```bash yarn
-yarn add langchain @langchain/core
-```
-
-```bash bun
-bun add langchain @langchain/core
-```
-</CodeGroup>
-:::
-
 ## <Icon icon="wand-magic-sparkles" /> Create an agent
 
 :::python
 ```python
-# pip install -qU "langchain[anthropic]" to call the model
-
+# pip install -qU langchain "langchain[anthropic]"
 from langchain.agents import create_agent
 
 def get_weather(city: str) -> str:
@@ -119,6 +83,7 @@ console.log(
 ```
 :::
 
+See the [Installation instructions](/oss/langchain/install) and [Quickstart guide](/oss/langchain/quickstart) to get started building your own agents and applications with LangChain.
 
 ## <Icon icon="star" size={20} /> Core benefits
 
diff --git a/src/oss/langchain/quickstart.mdx b/src/oss/langchain/quickstart.mdx
index 64fc997aae..4231c85ce2 100644
--- a/src/oss/langchain/quickstart.mdx
+++ b/src/oss/langchain/quickstart.mdx
@@ -4,14 +4,24 @@ title: Quickstart
 
 This quickstart takes you from a simple setup to a fully functional AI agent in just a few minutes.
 
+## Requirements
+
+For these examples, you will need to:
+
+* [Install](/oss/langchain/install) the LangChain package
+* Set up a [Claude (Anthropic)](https://www.anthropic.com/) account and get an API key
+* Set the `ANTHROPIC_API_KEY` environment variable in your terminal
+
+<Tip>
+    **LangChain Docs MCP server**
+
+    If you're using an AI coding assistant, you should install the [LangChain Docs MCP server](/use-these-docs) to get the most out of it. This ensures your agent has access to the latest documentation and examples.
+</Tip>
+
 ## Build a basic agent
 
 Start by creating a simple agent that can answer questions and call tools. The agent will use Claude Sonnet 4.5 as its language model, a basic weather function as a tool, and a simple prompt to guide its behavior.
 
-<Info>
-    For this example, you will need to set up a [Claude (Anthropic)](https://www.anthropic.com/) account and get an API key. Then, set the `ANTHROPIC_API_KEY` environment variable in your terminal.
-</Info>
-
 :::python
 ```python
 from langchain.agents import create_agent
@@ -205,7 +215,7 @@ Let's walk through each step:
         :::
     </Step>
     <Step title="Configure your model">
-        Set up your [language model](/oss/langchain/models) with the right [parameters](/oss/langchain/models#parameters) for your use case:
+        Set up your [language model](/oss/langchain/models) with the right parameters for your use case:
 
         :::python
 
@@ -232,6 +242,8 @@ Let's walk through each step:
         );
         ```
         :::
+
+        Depending on the model and provider chosen, initialization parameters may vary; refer to their reference pages for details.
     </Step>
     <Step title="Define response format">
         :::python