Zodv4

.changeset/dark-pans-carry.md

@@ -0,0 +1,5 @@
+---
+"@browserbasehq/stagehand": patch
+---
+
+Migrate from zodv3 to zodv4

packages/core/examples/2048.ts

@@ -1,5 +1,5 @@
 import { Stagehand } from "../lib/v3";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 async function example() {
   console.log("🎮 Starting 2048 bot...");

packages/core/examples/agent-custom-tools.ts

@@ -1,7 +1,7 @@
 /**
  * This example shows how to pass custom tools to stagehand agent (both CUA and non-CUA)
  */
-import { z } from "zod/v3";
+import { z } from "zod";
 import { tool } from "ai";
 import { Stagehand } from "../lib/v3";
 import chalk from "chalk";

packages/core/examples/custom_client_aisdk.ts

@@ -7,7 +7,7 @@
  */
 import { Stagehand } from "../lib/v3";
 import { AISdkClient } from "./external_clients/aisdk";
-import { z } from "zod/v3";
+import { z } from "zod";
 import { openai } from "@ai-sdk/openai";
 
 async function example() {

packages/core/examples/custom_client_langchain.ts

@@ -3,15 +3,17 @@
  *
  * You will need to reference the Langchain Client in /external_clients/langchain.ts
  */
-import { z } from "zod/v3";
+import { z } from "zod";
 import { Stagehand } from "../lib/v3";
 import { LangchainClient } from "./external_clients/langchain";
 import { ChatOpenAI } from "@langchain/openai";
 
 async function example() {
+  // @ts-expect-error Type instantiation is excessively deep and possibly infinite
   const stagehand = new Stagehand({
     env: "BROWSERBASE",
     verbose: 1,
+    // @ts-expect-error Type instantiation is excessively deep and possibly infinite
     llmClient: new LangchainClient(
       new ChatOpenAI({
         model: "gpt-4o",

packages/core/examples/custom_client_openai.ts

@@ -6,7 +6,7 @@
  * You will need to reference the Custom OpenAI Client in /external_clients/customOpenAI.ts
  */
 import { Stagehand } from "../lib/v3";
-import { z } from "zod/v3";
+import { z } from "zod";
 import { CustomOpenAIClient } from "./external_clients/customOpenAI";
 import OpenAI from "openai";
 

packages/core/examples/external_clients/customOpenAI.ts

@@ -22,7 +22,7 @@ import type {
   ChatCompletionSystemMessageParam,
   ChatCompletionUserMessageParam,
 } from "openai/resources/chat/completions";
-import { z } from "zod/v3";
+import { z } from "zod";
 import { CreateChatCompletionResponseError } from "../../lib/v3";
 
 function validateZodSchema(schema: z.ZodTypeAny, data: unknown) {

packages/core/examples/external_clients/langchain.ts

@@ -4,7 +4,7 @@ import {
   LLMClient,
   AvailableModel,
 } from "../../lib/v3";
-import { zodToJsonSchema } from "zod-to-json-schema";
+import { z } from "zod";
 import {
   AIMessage,
   BaseMessageLike,
@@ -60,9 +60,8 @@ export class LangchainClient extends LLMClient {
     );
 
     if (options.response_model) {
-      const responseSchema = zodToJsonSchema(options.response_model.schema, {
-        $refStrategy: "none",
-      });
+      //ref string no longer needed, this is now default behavior
+      const responseSchema = z.toJSONSchema(options.response_model.schema);
       const structuredModel = this.model.withStructuredOutput(responseSchema);
       const response = await structuredModel.invoke(formattedMessages);
 

packages/core/examples/parameterizeApiKey.ts

@@ -1,5 +1,5 @@
 import { Stagehand } from "../lib/v3";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 /**
  * This example shows how to parameterize the API key for the LLM provider.

packages/core/examples/v3/patchright.ts

@@ -1,6 +1,6 @@
 import { Stagehand } from "../../lib/v3";
 import { chromium } from "patchright-core";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 async function example(stagehand: Stagehand) {
   const browser = await chromium.connectOverCDP({

packages/core/examples/v3/playwright.ts

@@ -1,6 +1,6 @@
 import { Stagehand } from "../../lib/v3";
 import { chromium } from "playwright-core";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 async function example(stagehand: Stagehand) {
   const browser = await chromium.connectOverCDP({

packages/core/examples/v3/targetedExtract.ts

@@ -1,5 +1,5 @@
 import { Stagehand } from "../../lib/v3";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 async function example(stagehand: Stagehand) {
   const page = stagehand.context.pages()[0];

packages/core/examples/v3_example.ts

@@ -1,5 +1,5 @@
 import { V3 } from "../lib/v3";
-import { z } from "zod/v3";
+import { z } from "zod";
 
 async function example(v3: V3) {
   const page = v3.context.pages()[0];

packages/core/lib/inference.ts

@@ -1,4 +1,4 @@
-import { z } from "zod/v3";
+import { z } from "zod";
 import { LogLine } from "./v3/types/public/logs";
 import { ChatMessage, LLMClient } from "./v3/llm/LLMClient";
 import {
@@ -12,20 +12,8 @@ import {
 } from "./prompt";
 import { appendSummary, writeTimestampedTxtFile } from "./inferenceLogUtils";
 
-/** Simple usage shape if your LLM returns usage tokens. */
-interface LLMUsage {
-  prompt_tokens: number;
-  completion_tokens: number;
-  total_tokens: number;
-}
-
-/**
- * For calls that use a schema: the LLMClient may return { data: T; usage?: LLMUsage }
- */
-export interface LLMParsedResponse<T> {
-  data: T;
-  usage?: LLMUsage;
-}
+// Re-export for backward compatibility
+export type { LLMParsedResponse } from "./v3/llm/LLMClient";
 
 export async function extract({
   instruction,
@@ -101,8 +89,7 @@ export async function extract({
     });
   const extractEndTime = Date.now();
 
-  const { data: extractedData, usage: extractUsage } =
-    extractionResponse as LLMParsedResponse<ExtractionResponse>;
+  const { data: extractedData, usage: extractUsage } = extractionResponse;
 
   let extractResponseFile = "";
   if (logInferenceToFile) {
@@ -171,7 +158,7 @@ export async function extract({
       progress: metadataResponseProgress,
     },
     usage: metadataResponseUsage,
-  } = metadataResponse as LLMParsedResponse<MetadataResponse>;
+  } = metadataResponse;
 
   let metadataResponseFile = "";
   if (logInferenceToFile) {
@@ -308,8 +295,7 @@ export async function observe({
   const end = Date.now();
   const usageTimeMs = end - start;
 
-  const { data: observeData, usage: observeUsage } =
-    rawResponse as LLMParsedResponse<ObserveResponse>;
+  const { data: observeData, usage: observeUsage } = rawResponse;
   const promptTokens = observeUsage?.prompt_tokens ?? 0;
   const completionTokens = observeUsage?.completion_tokens ?? 0;
 
@@ -436,8 +422,7 @@ export async function act({
   const end = Date.now();
   const usageTimeMs = end - start;
 
-  const { data: actData, usage: actUsage } =
-    rawResponse as LLMParsedResponse<ActResponse>;
+  const { data: actData, usage: actUsage } = rawResponse;
   const promptTokens = actUsage?.prompt_tokens ?? 0;
   const completionTokens = actUsage?.completion_tokens ?? 0;