chore(ts): minor adjustments to TypeScript course material

typescript/m1/m1.2_scratch_agent.ts

@@ -4,6 +4,8 @@ import { model } from "../models.js";
 
 const agent = createDeepAgent({ model });
 
-const result = await agent.invoke({"messages": [{"role": "user", "content": "What is an LLM?"}]});
+const result = await agent.invoke({
+    messages: [{ role: "user", content: "What is an LLM?" }]
+});
 
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);

typescript/m1/m1.4_scratch_agent_butler.ts

@@ -1,13 +1,14 @@
+import { context } from "langchain";
 import { createDeepAgent } from "deepagents";
 
 import { model } from "../models.js";
 
-const SYSTEM_PROMPT =
-  "YOU ARE AN EXTREMELY POSH BRITISH BUTLER. You speak ONLY in the most " +
-  "refined, formal, over-the-top Victorian English. You say 'indeed', 'quite', " +
-  "'I dare say', 'one simply must' constantly. You find all things common or " +
-  "nautical to be utterly beneath you. You NEVER break character under ANY " +
-  "circumstances.";
+const SYSTEM_PROMPT = context`
+  YOU ARE AN EXTREMELY POSH BRITISH BUTLER. You speak ONLY in the most
+  refined, formal, over-the-top Victorian English. You say 'indeed', 'quite',
+  'I dare say', 'one simply must' constantly. You find all things common or
+  nautical to be utterly beneath you. You NEVER break character under ANY
+  circumstances.`;
 
 const agent = createDeepAgent({
   model,
@@ -19,4 +20,4 @@ const result = await agent.invoke({
   messages: [{ role: "user", content: "What is an LLM?" }],
 });
 
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);

typescript/m1/m1.5_scratch_agent_tools.ts

@@ -1,8 +1,9 @@
 import { DatabaseSync } from "node:sqlite";
 import { dirname, join } from "node:path";
 import { fileURLToPath } from "node:url";
-import { tool } from "@langchain/core/tools";
+
 import { z } from "zod";
+import { tool } from "langchain";
 import { createDeepAgent } from "deepagents";
 
 import { model } from "../models.js";
@@ -47,4 +48,4 @@ const result = await agent.invoke({
   messages: [{ role: "user", content: "Which five genres have the most tracks?" }],
 });
 
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);

typescript/m1/m1.6_agent_mcp.ts

@@ -12,28 +12,30 @@ const client = new MultiServerMCPClient({
   },
 });
 
-let tools = await client.getTools();
-
-console.log(`\ndocs-langchain: ${tools.length} tool(s)`);
-for (const t of tools) {
-  console.log(`  ${t.name}`);
-  console.log(`  ${t.description?.slice(0, 90)}`);
-}
-
-tools = tools.filter((t) => ALLOWED.has(t.name));
-
-const agent = createDeepAgent({ model, tools });
-
-const result = await agent.invoke({
-  messages: [
-    {
-      role: "user",
-      content:
-        "Use the LangChain docs MCP tool to explain what MCP is and how LangChain uses MCP tools.",
-    },
-  ],
-});
-
-console.log(result.messages[result.messages.length - 1].content);
-
-await client.close();
+try {
+  let tools = await client.getTools();
+
+  console.log(`\ndocs-langchain: ${tools.length} tool(s)`);
+  for (const t of tools) {
+    console.log(`  ${t.name}`);
+    console.log(`  ${t.description?.slice(0, 90)}`);
+  }
+
+  tools = tools.filter((t) => ALLOWED.has(t.name));
+
+  const agent = createDeepAgent({ model, tools });
+
+  const result = await agent.invoke({
+    messages: [
+      {
+        role: "user",
+        content:
+          "Use the LangChain docs MCP tool to explain what MCP is and how LangChain uses MCP tools.",
+      },
+    ],
+  });
+
+  console.log(result.messages.at(-1)?.content);
+} finally {
+  await client.close();
+}

typescript/m1/m1.7_messages_threads_checkpointers.ts

@@ -16,18 +16,18 @@ let result = await agent.invoke(
   threadA
 );
 console.log("Thread A, turn 1:");
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);
 
 result = await agent.invoke(
   { messages: [{ role: "user", content: "What is my favorite color?" }] },
   threadA
 );
 console.log("\nThread A, turn 2:");
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);
 
 result = await agent.invoke(
   { messages: [{ role: "user", content: "What is my favorite color?" }] },
   threadB
 );
 console.log("\nThread B, turn 1:");
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);

typescript/m1/m1.8_hitl.ts

@@ -1,9 +1,8 @@
 import { createInterface } from "node:readline/promises";
-import { tool } from "@langchain/core/tools";
-import { isToolMessage } from "@langchain/core/messages";
 import { z } from "zod";
-import { MemorySaver } from "@langchain/langgraph";
-import { Command, INTERRUPT, isInterrupted } from "@langchain/langgraph";
+
+import { tool, ToolMessage } from "langchain";
+import { Command, INTERRUPT, isInterrupted, MemorySaver } from "@langchain/langgraph";
 import { createDeepAgent } from "deepagents";
 
 import { model } from "../models.js";
@@ -97,7 +96,7 @@ while (isInterrupted<ApprovalRequest>(result) && result[INTERRUPT].length) {
 
 rl.close();
 for (const msg of result.messages) {
-  if (isToolMessage(msg) && msg.name === "send_email") {
+  if (ToolMessage.isInstance(msg) && msg.name === "send_email") {
     console.log(msg.content);
     break;
   }

typescript/m2/m2.2_agent.ts

@@ -3,6 +3,7 @@ import { dirname, join } from "node:path";
 import { mkdirSync, writeFileSync } from "node:fs";
 import { fileURLToPath } from "node:url";
 
+import { context } from "langchain";
 import {
   CompositeBackend,
   FilesystemBackend,
@@ -59,15 +60,15 @@ try {
       messages: [
         {
           role: "user",
-          content:
-            "Read /reference/chinook-sales.md, then add this note to it: " +
-            "'Current promotion: 20% off all Jazz albums through end of month.'",
+          content: context`
+            Read /reference/chinook-sales.md, then add this note to it:
+            'Current promotion: 20% off all Jazz albums through end of month.'`,
         },
       ],
     },
     { configurable: { thread_id: "lab-m2.2" } }
   );
-  console.log(result.messages[result.messages.length - 1].content);
+  console.log(result.messages.at(-1)?.content);
 } catch (e) {
   const err = e instanceof Error ? e : new Error(String(e));
   const cause = err.cause instanceof Error ? err.cause : undefined;

typescript/m2/m2.3_sales_agent.ts

@@ -5,6 +5,7 @@ import { writeFileSync } from "node:fs";
 import { readFile } from "node:fs/promises";
 import { fileURLToPath } from "node:url";
 
+import { context } from "langchain";
 import { LangSmithSandbox, createDeepAgent } from "deepagents";
 import { SandboxClient } from "langsmith/sandbox";
 
@@ -34,33 +35,33 @@ for (const result of uploadResults) {
 const agent = createDeepAgent({
   model,
   backend,
-  systemPrompt:
-    "You are a sales data analyst with access to the Chinook music store database " +
-    "at /chinook.db. Use sqlite3 and matplotlib to answer questions with charts. " +
-    "Install any packages you need with pip before importing them. " +
-    "When asked to produce a chart, write a Python script, execute it, and confirm " +
-    "the output file was created.",
+  systemPrompt: context`
+    You are a sales data analyst with access to the Chinook music store database
+    at /chinook.db. Use sqlite3 and matplotlib to answer questions with charts.
+    Install any packages you need with pip before importing them.
+    When asked to produce a chart, write a Python script, execute it, and confirm
+    the output file was created.`,
 });
 
 try {
   const result = await agent.invoke({
     messages: [
       {
         role: "user",
-        content:
-          "Query the Chinook database at /chinook.db to get total revenue " +
-          "by genre. Create a clean donut chart showing each genre's share " +
-          "of total sales revenue. Group any genres that individually " +
-          "account for less than 3% of total revenue into a single 'Other' " +
-          "slice. Label each slice with the genre name and percentage. " +
-          "Use a visually distinct color palette, leave a white center hole, " +
-          "and make sure no labels overlap with each other or with the title. " +
-          "Add enough top padding so the title is fully visible. " +
-          "Save the chart to /genre_revenue.png.",
+        content: context`
+          Query the Chinook database at /chinook.db to get total revenue
+          by genre. Create a clean donut chart showing each genre's share
+          of total sales revenue. Group any genres that individually
+          account for less than 3% of total revenue into a single 'Other'
+          slice. Label each slice with the genre name and percentage.
+          Use a visually distinct color palette, leave a white center hole,
+          and make sure no labels overlap with each other or with the title.
+          Add enough top padding so the title is fully visible.
+          Save the chart to /genre_revenue.png.`,
       },
     ],
   });
-  console.log(result.messages[result.messages.length - 1].content);
+  console.log(result.messages.at(-1)?.content);
 
   const pngBytes = await ls_sandbox.read("/genre_revenue.png");
   const outPath = join(__dirname, "genre_revenue.png");

typescript/m2/m2.3_sandbox_agent.ts

@@ -1,6 +1,7 @@
 // typescript/m2/m2.3_sandbox_agent.ts
 import { randomUUID } from "node:crypto";
 
+import { context } from "langchain";
 import { LangSmithSandbox, createDeepAgent } from "deepagents";
 import { SandboxClient } from "langsmith/sandbox";
 
@@ -16,23 +17,23 @@ const backend = new LangSmithSandbox({ sandbox: ls_sandbox });
 const agent = createDeepAgent({
   model,
   backend,
-  systemPrompt:
-    "You are a coding assistant. When asked to run code, write the script " +
-    "to a file first, then execute it. Show the output in your final answer.",
+  systemPrompt: context`
+    You are a coding assistant. When asked to run code, write the script
+    to a file first, then execute it. Show the output in your final answer.`,
 });
 
 try {
   const result = await agent.invoke({
     messages: [
       {
         role: "user",
-        content:
-          "Write a Python script that prints the first 15 Fibonacci numbers, " +
-          "save it to fib.py, and run it.",
+        content: context`
+          Write a Python script that prints the first 15 Fibonacci numbers,
+          save it to fib.py, and run it.`,
       },
     ],
   });
-  console.log(result.messages[result.messages.length - 1].content);
+  console.log(result.messages.at(-1)?.content);
 } finally {
   await client.deleteSandbox(ls_sandbox.name);
 }

typescript/m2/m2.4_eval_agent.ts

@@ -18,4 +18,4 @@ const result = await agent.invoke({
   ],
 });
 
-console.log(result.messages[result.messages.length - 1].content);
+console.log(result.messages.at(-1)?.content);

typescript/m2/m2.4_interpreter_agent.ts

@@ -4,29 +4,29 @@ import { DatabaseSync } from "node:sqlite";
 import { dirname, join } from "node:path";
 import { fileURLToPath } from "node:url";
 
-import { createCodeInterpreterMiddleware } from "@langchain/quickjs";
-import { tool } from "@langchain/core/tools";
-import { createDeepAgent } from "deepagents";
 import { z } from "zod";
+import { context, tool } from "langchain";
+import { createDeepAgent } from "deepagents";
+import { createCodeInterpreterMiddleware } from "@langchain/quickjs";
 
 import { model } from "../models.js";
 
 const __dirname = dirname(fileURLToPath(import.meta.url));
 const DB_PATH = join(__dirname, "chinook.db");
 
-const TASK =
-  "Who is our top-selling artist, what is their best-selling album, " +
-  "what is the most-purchased track on that album, " +
-  "and how many distinct customers have bought that track? " +
-  "Each answer depends on the result of the previous query.";
+const TASK = context`
+  Who is our top-selling artist, what is their best-selling album,
+  what is the most-purchased track on that album,
+  and how many distinct customers have bought that track?
+  Each answer depends on the result of the previous query.`;
 
-const SYSTEM =
-  "You are a sales analyst for Chinook Digital Music Store. " +
-  "Use the query_chinook tool to query the database. " +
-  "Key tables: Artist(ArtistId, Name), Album(AlbumId, Title, ArtistId), " +
-  "Track(TrackId, Name, AlbumId), " +
-  "InvoiceLine(InvoiceLineId, InvoiceId, TrackId, UnitPrice, Quantity). " +
-  "Revenue is InvoiceLine.UnitPrice * InvoiceLine.Quantity.";
+const SYSTEM = context`
+  You are a sales analyst for Chinook Digital Music Store.
+  Use the query_chinook tool to query the database.
+  Key tables: Artist(ArtistId, Name), Album(AlbumId, Title, ArtistId),
+  Track(TrackId, Name, AlbumId),
+  InvoiceLine(InvoiceLineId, InvoiceId, TrackId, UnitPrice, Quantity).
+  Revenue is InvoiceLine.UnitPrice * InvoiceLine.Quantity.`;
 
 const queryChinook = tool(
   ({ sql }) => {
@@ -53,13 +53,13 @@ const agentWith = createDeepAgent({
   model,
   tools: [queryChinook],
   middleware: [createCodeInterpreterMiddleware({ ptc: ["query_chinook"] })],
-  systemPrompt:
-    SYSTEM +
-    " The eval tool supports Programmatic Tool Calling (PTC): JavaScript" +
-    " running inside eval() can call query_chinook via tools.queryChinook()." +
-    " For dependent queries where each answer requires a result from the" +
-    " previous, prefer a single eval() call that chains all queries in" +
-    " JavaScript — intermediate values stay in variables and never return to the model.",
+  systemPrompt: context`
+    ${SYSTEM}
+    The eval tool supports Programmatic Tool Calling (PTC): JavaScript
+    running inside eval() can call query_chinook via tools.queryChinook().
+    For dependent queries where each answer requires a result from the
+    previous, prefer a single eval() call that chains all queries in
+    JavaScript — intermediate values stay in variables and never return to the model.`,
 });
 
 const resultWith = await agentWith.invoke(

typescript/m3/m3.1_summarization.ts

@@ -12,8 +12,8 @@
 //     npx tsx m3/m3.1_summarization.ts
 
 import { createDeepAgent } from "deepagents";
+import { HumanMessage } from "langchain";
 import { MemorySaver } from "@langchain/langgraph";
-import { HumanMessage } from "@langchain/core/messages";
 
 import { model } from "../models.js";
 
@@ -47,7 +47,7 @@ async function turn(message: string): Promise<unknown> {
     { messages: [new HumanMessage(message)] },
     THREAD
   );
-  return result.messages[result.messages.length - 1].content;
+  return result.messages.at(-1)?.content;
 }
 
 async function showState(): Promise<void> {