Add support for Docker arg passthrough in langgraph CLI (#2206)

Added a js-example to show it builds
Adapted integration tests after removing the test CLI command

---------

Co-authored-by: Nuno Campos <nuno@langchain.dev>

libs/cli/js-examples/src/agent/graph.ts

@@ -0,0 +1,104 @@
+/**
+ * Starter LangGraph.js Template
+ * Make this code your own!
+ */
+import { StateGraph } from "@langchain/langgraph";
+import { RunnableConfig } from "@langchain/core/runnables";
+import { StateAnnotation } from "./state.js";
+
+/**
+ * Define a node, these do the work of the graph and should have most of the logic.
+ * Must return a subset of the properties set in StateAnnotation.
+ * @param state The current state of the graph.
+ * @param config Extra parameters passed into the state graph.
+ * @returns Some subset of parameters of the graph state, used to update the state
+ * for the edges and nodes executed next.
+ */
+const callModel = async (
+  state: typeof StateAnnotation.State,
+  _config: RunnableConfig,
+): Promise<typeof StateAnnotation.Update> => {
+  /**
+   * Do some work... (e.g. call an LLM)
+   * For example, with LangChain you could do something like:
+   *
+   * ```bash
+   * $ npm i @langchain/anthropic
+   * ```
+   *
+   * ```ts
+   * import { ChatAnthropic } from "@langchain/anthropic";
+   * const model = new ChatAnthropic({
+   *   model: "claude-3-5-sonnet-20240620",
+   *   apiKey: process.env.ANTHROPIC_API_KEY,
+   * });
+   * const res = await model.invoke(state.messages);
+   * ```
+   *
+   * Or, with an SDK directly:
+   *
+   * ```bash
+   * $ npm i openai
+   * ```
+   *
+   * ```ts
+   * import OpenAI from "openai";
+   * const openai = new OpenAI({
+   *   apiKey: process.env.OPENAI_API_KEY,
+   * });
+   *
+   * const chatCompletion = await openai.chat.completions.create({
+   *   messages: [{
+   *     role: state.messages[0]._getType(),
+   *     content: state.messages[0].content,
+   *   }],
+   *   model: "gpt-4o-mini",
+   * });
+   * ```
+   */
+  console.log("Current state:", state);
+  return {
+    messages: [
+      {
+        role: "assistant",
+        content: `Hi there! How are you?`,
+      },
+    ],
+  };
+};
+
+/**
+ * Routing function: Determines whether to continue research or end the builder.
+ * This function decides if the gathered information is satisfactory or if more research is needed.
+ *
+ * @param state - The current state of the research builder
+ * @returns Either "callModel" to continue research or END to finish the builder
+ */
+export const route = (
+  state: typeof StateAnnotation.State,
+): "__end__" | "callModel" => {
+  if (state.messages.length > 0) {
+    return "__end__";
+  }
+  // Loop back
+  return "callModel";
+};
+
+// Finally, create the graph itself.
+const builder = new StateGraph(StateAnnotation)
+  // Add the nodes to do the work.
+  // Chaining the nodes together in this way
+  // updates the types of the StateGraph instance
+  // so you have static type checking when it comes time
+  // to add the edges.
+  .addNode("callModel", callModel)
+  // Regular edges mean "always transition to node B after node A is done"
+  // The "__start__" and "__end__" nodes are "virtual" nodes that are always present
+  // and represent the beginning and end of the builder.
+  .addEdge("__start__", "callModel")
+  // Conditional edges optionally route to different nodes (or end)
+  .addConditionalEdges("callModel", route);
+
+export const graph = builder.compile();
+
+graph.name = "New Agent";