add remaining scripts

examples/js/configuration.js

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+// How to create agents with configuration
+import { Client } from "@langchain/langgraph-sdk";
+
+/*
+One of the benefits of LangGraph API is that it lets you create agents with different configurations.
+This is useful when you want to:
+
+- Define a cognitive architecture once as a LangGraph
+- Let that LangGraph be configurable across some attributes (for example, system message or LLM to use)
+- Let users create agents with arbitrary configurations, save them, and then use them in the future
+
+In this guide we will show how to do that for the default agent we have built in.
+
+If you look at the agent we defined, you can see that inside the `call_model` node we have created the model based on some configuration. That node looks like:
+
+```python
+def call_model(state, config):
+    messages = state["messages"]
+    model_name = config.get('configurable', {}).get("model_name", "anthropic")
+    model = _get_model(model_name)
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+```
+
+We are looking inside the config for a `model_name` parameter (which defaults to `anthropic` if none is found).
+That means that by default we are using Anthropic as our model provider.
+In this example we will see an example of how to create an example agent that is configured to use OpenAI.
+*/
+
+async function main() {
+  const client = new Client();
+  const assistant = await client.assistants.create({ graphId: "agent", config: { configurable: { model_name: "openai" } }});
+  // We can see that this assistant has saved the config
+  console.log("Assistant", assistant);
+  const thread = await client.threads.create({ metadata: null });
+  const input = { messages: [{ role: "user", content: "who made you?" }] };
+
+  for await (const event of client.runs.stream(thread.thread_id, assistant.assistant_id, { input })) {
+    console.log(`Receiving new event of type: ${event.event}...`);
+    console.log(JSON.stringify(event.data));
+    console.log("\n\n");
+  }
+}
+
+main()

examples/js/sameThread.js

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+// How to run multiple agents on the same thread
+import { Client } from "@langchain/langgraph-sdk";
+
+/* 
+In LangGraph API, a thread is not explicitly associated with a particular agent.
+This means that you can run multiple agents on the same thread.
+In this example, we will create two agents and then call them both on the same thread.
+*/
+
+async function main() {
+  const client = new Client();
+  // const openaiAssistant = await client.assistants.create({ graphId: "agent", config: { configurable: { model_name: "openai" } }});
+  const defaultAssistant = await client.assistants.create({ graphId: "agent" })
+  const openaiAssistant = await client.assistants.create({ graphId: "agent", config: { configurable: { model_name: "openai" } }});
+
+  console.log("Default assistant", defaultAssistant);
+  // We can see that this assistant has saved the config
+  console.log("OpenAI assistant", openaiAssistant);
+  const thread = await client.threads.create({ metadata: null });
+  let input = { messages: [{ role: "user", content: "who made you?" }] };
+
+  for await (const event of client.runs.stream(thread.thread_id, openaiAssistant.assistant_id, { input, streamMode: "updates" })) {
+    console.log(JSON.stringify(event.data));
+    console.log("\n\n");
+  }
+
+  input = { messages: [{ role: "user", content: "and you?" }] };
+  for await (const event of client.runs.stream(thread.thread_id, defaultAssistant.assistant_id, { input, streamMode: "updates" })) {
+    console.log(JSON.stringify(event.data));
+    console.log("\n\n");
+  }
+}
+
+main()

examples/js/streamMessages.js

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+// How to stream messages from your graph
 import { Client } from "@langchain/langgraph-sdk";
 
 /*

examples/js/streamUpdates.js

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+// How to stream updates from your graph
 import { Client } from "@langchain/langgraph-sdk";
 
 /*

examples/js/streamValues.js

@@ -1,3 +1,4 @@
+// How to stream values from your graph
 import { Client } from "@langchain/langgraph-sdk";
 
 /*