feat(javascript)!: move to AI SDK 7

README.md

@@ -103,6 +103,8 @@ uv add langwatch-scenario pytest
 pnpm install @langwatch/scenario vitest
 ```
 
+The TypeScript package targets [AI SDK](https://ai-sdk.dev) 7. If your project is still on AI SDK 6, stay on `@langwatch/scenario` 1.x until you upgrade.
+
 Now create your first scenario, copy the full working example below.
 
 <details>
@@ -198,13 +200,8 @@ describe("Vegetarian Recipe Agent", () => {
     call: async (input) => {
       const response = await generateText({
         model: openai("gpt-4.1-mini"),
-        messages: [
-          {
-            role: "system",
-            content: `You are a vegetarian recipe agent.\nGiven the user request, ask AT MOST ONE follow-up question, then provide a complete recipe. Keep your responses concise and focused.`,
-          },
-          ...input.messages,
-        ],
+        instructions: `You are a vegetarian recipe agent.\nGiven the user request, ask AT MOST ONE follow-up question, then provide a complete recipe. Keep your responses concise and focused.`,
+        messages: input.messages,
       });
       return response.text;
     },

docs/docs/pages/advanced/remote-traces.mdx

@@ -238,13 +238,23 @@ async def test_remote_trace_propagation():
 ```typescript [typescript]
 import { createServer, Server } from "http";
 import { openai } from "@ai-sdk/openai";
+import { OpenTelemetry } from "@ai-sdk/otel";
 import scenario, { AgentRole, type AgentAdapter } from "@langwatch/scenario";
 import { context, propagation } from "@opentelemetry/api";
 import { W3CTraceContextPropagator } from "@opentelemetry/core";
-import { generateText, ModelMessage, stepCountIs, tool } from "ai";
+import {
+  generateText,
+  ModelMessage,
+  isStepCount,
+  registerTelemetry,
+  tool,
+} from "ai";
 import { describe, it, expect, beforeAll, afterAll } from "vitest";
 import { z } from "zod/v4";
 
+// AI SDK 7 emits no spans until a telemetry integration is registered.
+registerTelemetry(new OpenTelemetry());
+
 // Standard OTel HTTP instrumentation registers a propagator on its own;
 // this hand-rolled demo server registers the W3C propagator explicitly.
 propagation.setGlobalPropagator(new W3CTraceContextPropagator());
@@ -277,17 +287,12 @@ beforeAll(async () => {
       const result = await context.with(extracted, () =>
         generateText({
           model: openai("gpt-5-mini"),
-          messages: [
-            {
-              role: "system",
-              content:
-                "You are an order support assistant. Use the lookup_order_status tool to check orders before answering.",
-            },
-            ...messages,
-          ],
+          instructions:
+            "You are an order support assistant. Use the lookup_order_status tool to check orders before answering.",
+          messages,
           tools: { lookup_order_status: lookupOrderStatus },
-          stopWhen: stepCountIs(3),
-          experimental_telemetry: { isEnabled: true },
+          stopWhen: isStepCount(3),
+          telemetry: { isEnabled: true },
         })
       );
 

docs/docs/pages/agent-integration.mdx

@@ -379,7 +379,7 @@ import { z } from "zod";
 
 const getCurrentWeather = tool({
   description: "Get the current weather in a given city.",
-  parameters: z.object({
+  inputSchema: z.object({
     city: z.string().describe("The city to get the weather for."),
   }),
   execute: async ({ city }) => `The weather in ${city} is sunny!`,

docs/docs/pages/agent-integration/vercel-ai.mdx

@@ -20,11 +20,8 @@ import { generateText } from 'ai';
 async function myAgent(prompt: string) {
   const result = await generateText({
     model: openai('gpt-4-turbo'),
+    instructions: 'You are a helpful AI assistant.',
     messages: [
-      {
-        role: 'system',
-        content: 'You are a helpful AI assistant.',
-      },
       {
         role: 'user',
         content: prompt,
@@ -46,7 +43,7 @@ const createMyAgentAdapter = (): AgentAdapter => {
     name: "VercelAIAgent",
     call: async (input) => {
       const result = await myAgent(input.messages.at(-1)?.content);
-      return result.response.messages;
+      return result.responseMessages;
     },
   };
 };

docs/docs/pages/basics/scripted-simulations.mdx

@@ -257,7 +257,8 @@ const script = [
     ],
   }),
 
-  // System message
+  // System message (with AI SDK 7, an agent that forwards these messages to
+  // generateText needs `allowSystemInMessages: true`)
   message({
     role: "system",
     content: "The user is now in a hurry and needs quick responses",

docs/docs/pages/examples/multimodal/multimodal-images.mdx

@@ -36,13 +36,8 @@ const imageAgent: AgentAdapter = {
   async call(input) {
     const response = await generateText({
       model: openai("gpt-4o"),
-      messages: [
-        {
-          role: "system",
-          content: `You are a helpful assistant that can describe images.`,
-        },
-        ...input.messages,
-      ],
+      instructions: `You are a helpful assistant that can describe images.`,
+      messages: input.messages,
     });
     return response.text;
   },

docs/docs/pages/introduction/getting-started.mdx

@@ -26,6 +26,40 @@ pnpm install @langwatch/scenario vitest
 
 :::
 
+<Callout type="info">
+The TypeScript SDK targets [AI SDK](https://ai-sdk.dev) 7 and Node.js 22.12 or later. If your project is still on AI SDK 6, stay on `@langwatch/scenario` 1.x until you upgrade.
+</Callout>
+
+### AI SDK telemetry (TypeScript)
+
+AI SDK 7 emits no spans until a telemetry integration is registered, so the judge and LangWatch cannot see your agent's LLM calls by default. Register the OpenTelemetry integration once at start-up, for example in a Vitest setup file:
+
+```bash
+pnpm install @ai-sdk/otel
+```
+
+```typescript
+// vitest.setup.ts
+import { OpenTelemetry } from "@ai-sdk/otel";
+import { registerTelemetry } from "ai";
+
+registerTelemetry(new OpenTelemetry());
+```
+
+To attach per-call metadata to the spans (a user id, a tenant, a feature flag), pass it as `runtimeContext` and opt each key in with `telemetry.includeRuntimeContext`. Turn on `runtimeContext` on the integration too; the values are emitted as `ai.settings.context.<key>`:
+
+```typescript
+registerTelemetry(new OpenTelemetry({ runtimeContext: true }));
+
+await generateText({
+  model: openai("gpt-5-mini"),
+  instructions: "You are a vegetarian recipe agent.",
+  messages: input.messages,
+  runtimeContext: { userId: "user-123" },
+  telemetry: { functionId: "recipe-agent", includeRuntimeContext: { userId: true } },
+});
+```
+
 ## Quick Start
 
 Create your first Scenario test. This example demonstrates testing a vegetarian recipe agent:
@@ -138,10 +172,8 @@ const vegetarianRecipeAgent: AgentAdapter = {
   call: async (input) => {
     const response = await generateText({
       model: openai("gpt-4.1-mini"),
-      messages: [
-        { role: "system", content: "You are a vegetarian recipe agent." },
-        ...input.messages,
-      ],
+      instructions: "You are a vegetarian recipe agent.",
+      messages: input.messages,
     });
     return response.text;
   },

docs/docs/pages/testing-guides/mocks.mdx

@@ -161,7 +161,7 @@ const mockWeatherTool = vi.fn().mockResolvedValue("Sunny, 75°F");
 
 const weatherTool = tool({
   description: "Get weather data",
-  parameters: z.object({ city: z.string() }),
+  inputSchema: z.object({ city: z.string() }),
   execute: mockWeatherTool,
 });
 

docs/docs/pages/testing-guides/tool-calling.mdx

@@ -309,16 +309,11 @@ const agent = (): AgentAdapter => ({
   call: async (input) => {
     const response = await generateText({
       model: openai("gpt-4.1-nano"),
-      messages: [
-        {
-          role: "system",
-          content: `
+      instructions: `
             You are a helpful assistant that may help the user with weather information.
             Do not guess the city if they don't provide it.
           `,
-        },
-        ...input.messages,
-      ],
+      messages: input.messages,
       tools: { get_current_weather: getCurrentWeather },
       toolChoice: "auto",
     });

javascript/README.md

@@ -27,6 +27,17 @@ npm install @langwatch/scenario
 yarn add @langwatch/scenario
 ```
 
+Scenario targets [AI SDK](https://ai-sdk.dev) 7 (`ai@^7`) and Node.js 22.12 or later. If your project is still on AI SDK 6, stay on `@langwatch/scenario` 1.x until you upgrade.
+
+AI SDK 7 emits no spans until a telemetry integration is registered. To let the judge and LangWatch see your agent's LLM calls, register one once at start-up (for example in a Vitest setup file):
+
+```typescript
+import { OpenTelemetry } from "@ai-sdk/otel";
+import { registerTelemetry } from "ai";
+
+registerTelemetry(new OpenTelemetry());
+```
+
 ## Quick Start
 
 Create your first scenario test in under a minute.
@@ -86,7 +97,7 @@ describe("Weather Agent", () => {
     // 1. Define the tools your agent can use
     const getCurrentWeather = tool({
       description: "Get the current weather in a given city.",
-      parameters: z.object({
+      inputSchema: z.object({
         city: z.string().describe("The city to get the weather for."),
       }),
       execute: async ({ city }) =>
@@ -100,7 +111,7 @@ describe("Weather Agent", () => {
       call: async (input) => {
         const response = await generateText({
           model: openai("gpt-4.1-mini"),
-          system: `You are a helpful assistant that may help the user with weather information.`,
+          instructions: `You are a helpful assistant that may help the user with weather information.`,
           messages: input.messages,
           tools: { get_current_weather: getCurrentWeather },
         });
@@ -113,6 +124,7 @@ describe("Weather Agent", () => {
             {
               toolCallId: toolCall.toolCallId,
               messages: input.messages,
+              context: {},
             }
           );
           return [

javascript/examples/vitest/package.json

@@ -21,11 +21,12 @@
     "vitest-mock-extended": "5.1.1"
   },
   "dependencies": {
-    "@ai-sdk/anthropic": "3.0.41",
-    "@ai-sdk/xai": "3.0.50",
+    "@ai-sdk/anthropic": "4.0.79",
+    "@ai-sdk/otel": "^1.0.130",
+    "@ai-sdk/xai": "5.0.21",
     "@langwatch/scenario": "workspace:*",
     "@openai/agents": "^0.16.0",
-    "ai": "^6.0.0",
+    "ai": "^7.0.130",
     "express": "^5.1.0",
     "openai": "^6.9.1",
     "vite": "^8.2.1",

javascript/examples/vitest/tests/00-demo-light.test.ts

@@ -25,15 +25,10 @@ describe("Demo: Lightweight Scenarios", () => {
     call: async (input) => {
       const response = await generateText({
         model: openai("gpt-5-mini"),
-        messages: [
-          {
-            role: "system",
-            content:
-              "You can analyze images and respond briefly and helpfully.",
-          },
-          ...input.messages,
-        ],
-        experimental_telemetry: { isEnabled: true },
+        instructions:
+          "You can analyze images and respond briefly and helpfully.",
+        messages: input.messages,
+        telemetry: { isEnabled: true },
       });
       return response.text;
     },

javascript/examples/vitest/tests/api-service-mocking.test.ts

@@ -48,7 +48,7 @@ const userDataAgent: AgentAdapter = {
       messages: input.messages,
       tools: { fetch_user_data: fetchUserDataTool },
       toolChoice: "auto",
-      experimental_telemetry: { isEnabled: true },
+      telemetry: { isEnabled: true },
     });
     return response.text;
   },

javascript/examples/vitest/tests/database-tool-mocking.test.ts

@@ -52,7 +52,7 @@ const databaseAgent: AgentAdapter = {
         find_user: findUserTool,
       },
       toolChoice: "auto",
-      experimental_telemetry: { isEnabled: true },
+      telemetry: { isEnabled: true },
     });
     return response.text;
   },

javascript/examples/vitest/tests/false-assumptions.test.ts

@@ -16,11 +16,9 @@ describe("False Assumptions", () => {
       call: async (input) => {
         const response = await generateText({
           model: openai("gpt-5-mini"),
-          messages: [
-            { role: "system", content: "You are a helpful assistant" },
-            ...input.messages,
-          ],
-          experimental_telemetry: { isEnabled: true },
+          instructions: "You are a helpful assistant",
+          messages: input.messages,
+          telemetry: { isEnabled: true },
         });
 
         return response.text;

javascript/examples/vitest/tests/helpers/register-ai-telemetry.ts

@@ -0,0 +1,8 @@
+/**
+ * AI SDK 7 emits no spans until a telemetry integration is registered.
+ * Loaded once per worker as a vitest setup file.
+ */
+import { OpenTelemetry } from "@ai-sdk/otel";
+import { registerTelemetry } from "ai";
+
+registerTelemetry(new OpenTelemetry());

javascript/examples/vitest/tests/judge-tool-calls-without-telemetry.test.ts

@@ -42,7 +42,7 @@ const weatherAgent: AgentAdapter = {
       messages: input.messages,
       tools: { get_weather: getWeatherTool },
       toolChoice: "auto",
-      experimental_telemetry: { isEnabled: true },
+      telemetry: { isEnabled: true },
     });
 
     if (response.toolCalls && response.toolCalls.length > 0) {

javascript/examples/vitest/tests/llm-provider-mocking.test.ts

@@ -1,6 +1,6 @@
 import { openai } from "@ai-sdk/openai";
 import scenario, { type AgentAdapter, AgentRole } from "@langwatch/scenario";
-import { generateText, ToolSet, GenerateTextResult } from "ai";
+import { generateText } from "ai";
 import { describe, it, expect, vi } from "vitest";
 
 /**
@@ -39,7 +39,7 @@ const chatAgent: AgentAdapter = {
     const response = await generateText({
       model: openai("gpt-5-mini"),
       messages: input.messages,
-      experimental_telemetry: { isEnabled: true },
+      telemetry: { isEnabled: true },
     });
     return response.text;
   },
@@ -51,7 +51,7 @@ describe("LLM Provider Mocking", () => {
     // No actual LLM call will be made
     mockGenerateText.mockResolvedValue({
       text: "I can help you with that request.",
-    } as GenerateTextResult<ToolSet, never>);
+    } as Awaited<ReturnType<typeof generateText>>);
 
     const result = await scenario.run({
       name: "llm mock test",

javascript/examples/vitest/tests/mocked-weather-agent-tool.test.ts

@@ -26,19 +26,14 @@ describe("Mocked Weather Agent Tool", () => {
       call: async (input) => {
         const response = await generateText({
           model: openai("gpt-5-mini"),
-          messages: [
-            {
-              role: "system",
-              content: `
+          instructions: `
                 You are a helpful assistant that may help the user with weather information.
                 Do not guess the city if they don't provide it.
               `,
-            },
-            ...input.messages,
-          ],
+          messages: input.messages,
           tools: { get_current_weather: getCurrentWeather },
           toolChoice: "auto",
-          experimental_telemetry: { isEnabled: true },
+          telemetry: { isEnabled: true },
         });
 
         return response.text;