add js to overview (#160)

Author: lnhsingh

src/oss/overview.mdx

@@ -6,12 +6,21 @@ Trusted by companies shaping the future of agents – including Klarna, Replit,
 
 Install LangGraph:
 
-```
+:::python
+```python
 pip install -U langgraph
 ```
+:::
+
+:::js
+```js
+npm install @langchain/langgraph @langchain/core
+```
+:::
 
 Then, create an agent [using prebuilt components](/oss/prebuilts):
 
+:::python
 ```python
 # pip install -qU "langchain[anthropic]" to call the model
 
@@ -32,6 +41,45 @@ agent.invoke(
     {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
 )
 ```
+:::
+
+:::js
+```js
+// npm install @langchain/anthropic to call the model
+
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { initChatModel } from "langchain/chat_models/universal";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const getWeather = tool( 
+  async (input: { city: string }) => {
+    return `It's always sunny in ${input.city}!`;
+  },
+  {
+    name: "getWeather",
+    schema: z.object({
+      city: z.string().describe("The city to get the weather for"),
+    }),
+    description: "Get weather for a given city.",
+  }
+);
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest"); 
+const agent = createReactAgent({
+  llm,
+  tools: [getWeather], 
+  prompt: "You are a helpful assistant", 
+});
+
+// Run the agent
+await agent.invoke({
+  messages: [{ role: "user", content: "what is the weather in sf" }],
+});
+
+```
+:::
+
 
 For more information, see the [Quickstart](/oss/quickstart). Or, to learn how to build an [agent workflow](/oss/graph-api) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](/oss/1-build-basic-chatbot).