feat(deepagents): PTC via Worker repl

examples/sandbox/sandbox-ptc-node.ts

@@ -66,7 +66,7 @@ console.log(`  100 employee records loaded\n`);
 try {
   const agent = createDeepAgent({
     model: new ChatAnthropic({
-      model: "claude-sonnet-4-20250514",
+      model: "claude-haiku-4-5",
       temperature: 0,
     }),
     systemPrompt,

examples/sandbox/sandbox-ptc-python.ts

@@ -66,7 +66,7 @@ console.log(`  100 employee records loaded\n`);
 try {
   const agent = createDeepAgent({
     model: new ChatAnthropic({
-      model: "claude-sonnet-4-20250514",
+      model: "claude-haiku-4-5",
       temperature: 0,
     }),
     systemPrompt,

examples/sandbox/sandbox-ptc-repl.ts

@@ -0,0 +1,182 @@
+/* eslint-disable no-console */
+/**
+ * Sandbox PTC (Programmatic Tool Calling) Example — Worker REPL + StateBackend
+ *
+ * Same scenario as sandbox-ptc.ts but without any sandbox infrastructure.
+ * A StateBackend stores the CSV file in agent state, giving the agent
+ * filesystem tools (read_file, write_file, etc.) for data access.
+ * The PTC middleware adds a `js_eval` tool backed by a Worker REPL
+ * with `toolCall()` and `spawnAgent()` as async globals.
+ *
+ *   Phase 1 — 100 parallel `toolCall("classify_record", ...)` via Promise.all
+ *   Phase 2 — 100 parallel `spawnAgent(...)` "analyst" (real LLM subagents)
+ *
+ * No VfsSandbox, no Deno, no Modal, no Docker — just a StateBackend + Worker thread.
+ *
+ * ## Running
+ *
+ * ```bash
+ * ANTHROPIC_API_KEY=sk-... npx tsx examples/sandbox/sandbox-ptc-repl.ts
+ * ```
+ */
+
+import "dotenv/config";
+
+import { z } from "zod/v4";
+
+import { ChatAnthropic } from "@langchain/anthropic";
+import { createMiddleware, tool, HumanMessage, AIMessage } from "langchain";
+import {
+  createDeepAgent,
+  createSandboxPtcMiddleware,
+  StateBackend,
+} from "deepagents";
+
+import { generateCsv } from "./utils/sandbox-ptc.js";
+
+/**
+ * ===============================
+ * Define the classification tool.
+ * ===============================
+ */
+
+const classifyTool = tool(
+  async (input: {
+    name: string;
+    age: number;
+    department: string;
+    years_at_company: number;
+  }) => {
+    const seniority =
+      input.years_at_company >= 15
+        ? "senior"
+        : input.years_at_company >= 5
+          ? "mid-level"
+          : "junior";
+    const ageGroup =
+      input.age >= 55
+        ? "55+"
+        : input.age >= 40
+          ? "40-54"
+          : input.age >= 30
+            ? "30-39"
+            : "under-30";
+    const eligible = input.years_at_company >= 3 && input.age >= 25;
+    return JSON.stringify({
+      name: input.name,
+      seniority,
+      age_group: ageGroup,
+      department: input.department,
+      promotion_eligible: eligible,
+    });
+  },
+  {
+    name: "classify_record",
+    description:
+      "Classify an employee record — returns seniority, age group, promotion eligibility",
+    schema: z.object({
+      name: z.string(),
+      age: z.number(),
+      department: z.string(),
+      years_at_company: z.number(),
+    }),
+  },
+);
+
+const ptcOnlyToolsMiddleware = createMiddleware({
+  name: "PtcOnlyTools",
+  tools: [classifyTool],
+  wrapModelCall: async (request, handler) => {
+    const visibleTools = (request.tools as { name: string }[]).filter(
+      (t) => t.name !== "classify_record",
+    );
+    return handler({ ...request, tools: visibleTools });
+  },
+});
+
+/**
+ * =============================
+ * Define the system prompt.
+ * =============================
+ */
+const systemPrompt = `You are a data-processing agent.
+
+The file \`data/employees.csv\` contains 100 employee records with columns: id, name, age, department, years_at_company.
+Use read_file to load it, then use js_eval with toolCall/spawnAgent for processing.`;
+
+const csv = generateCsv(100);
+const now = new Date().toISOString();
+
+console.log("Starting Worker REPL with StateBackend...\n");
+console.log(`  100 employee records loaded into state\n`);
+
+/**
+ * =============================
+ * Create the agent.
+ * =============================
+ */
+const agent = createDeepAgent({
+  model: new ChatAnthropic({
+    model: "claude-haiku-4-5",
+    temperature: 0,
+  }),
+  systemPrompt,
+  backend: (config) => new StateBackend(config),
+  subagents: [
+    {
+      name: "analyst",
+      description: "Provides career recommendations for individual employees",
+      systemPrompt: `You are an HR analyst. Given an employee's classification data,
+provide exactly ONE sentence of career recommendation. Be specific and actionable.
+Do not use markdown. Keep it under 30 words.`,
+      model: new ChatAnthropic({
+        model: "claude-haiku-4-5",
+        temperature: 0,
+      }),
+    },
+  ],
+  middleware: [
+    // No backend passed to PTC → Worker REPL with js_eval tool.
+    // The agent's StateBackend still provides filesystem tools (read_file, etc.)
+    createSandboxPtcMiddleware({ ptc: true }),
+    // @ts-expect-error type issue with branded AgentMiddleware type
+    ptcOnlyToolsMiddleware,
+  ],
+});
+
+console.log(
+  "Running: 100 parallel classify + 100 parallel analyst subagents (Worker REPL)...\n",
+);
+const t0 = performance.now();
+
+/**
+ * =============================
+ * Invoke the agent.
+ * =============================
+ */
+const result = await agent.invoke(
+  {
+    messages: [
+      new HumanMessage(
+        `Classify all 100 employees and spawn 100 analyst subagents in parallel.`,
+      ),
+    ],
+    files: {
+      "/data/employees.csv": {
+        content: csv.split("\n"),
+        created_at: now,
+        modified_at: now,
+      },
+    },
+  },
+  { recursionLimit: 100 },
+);
+
+const elapsed = ((performance.now() - t0) / 1000).toFixed(1);
+const last = result.messages.findLast(AIMessage.isInstance);
+if (last) {
+  console.log(`\nAgent Response (${elapsed}s total):\n`);
+  console.log(last.content);
+}
+
+console.log("\nDone.");

examples/sandbox/sandbox-ptc.ts

@@ -116,7 +116,7 @@ try {
    */
   const agent = createDeepAgent({
     model: new ChatAnthropic({
-      model: "claude-sonnet-4-20250514",
+      model: "claude-haiku-4-5",
       temperature: 0,
     }),
     systemPrompt,
@@ -136,6 +136,7 @@ Do not use markdown. Keep it under 30 words.`,
     ],
     middleware: [
       createSandboxPtcMiddleware({ backend: sandbox, ptc: true }),
+      // @ts-expect-error type issue with branded AgentMiddleware type
       ptcOnlyToolsMiddleware,
     ],
   });

libs/deepagents/src/index.ts

@@ -125,7 +125,9 @@ export {
   createSandboxPtcMiddleware,
   PtcExecutionEngine,
   StdoutScanner,
+  WorkerRepl,
   generateSandboxPtcPrompt,
+  generateWorkerReplPrompt,
   type SandboxPtcMiddlewareOptions,
   type PtcEngineOptions,
 } from "./sandbox-ptc/index.js";

libs/deepagents/src/sandbox-ptc/index.ts

@@ -18,7 +18,12 @@ export {
   type PtcEngineOptions,
 } from "./engine.js";
 
-export { generateSandboxPtcPrompt } from "./prompt.js";
+export {
+  generateSandboxPtcPrompt,
+  generateWorkerReplPrompt,
+} from "./prompt.js";
+
+export { WorkerRepl } from "./worker-repl.js";
 
 export {
   BASH_RUNTIME,