Merge branch 'vercel:main' into vivek/providers

content/docs/03-ai-sdk-core/05-generating-text.mdx

@@ -122,17 +122,21 @@ const result = streamText({
 
 ### `onFinish` callback
 
-When using `streamText`, you can provide an `onFinish` callback that is triggered when the stream is finished.
-It contains the text, usage information, finish reason, and more:
+When using `streamText`, you can provide an `onFinish` callback that is triggered when the stream is finished (
+[API Reference](/docs/reference/ai-sdk-core/stream-text#on-finish)
+).
+It contains the text, usage information, finish reason, messages, and more:
 
 ```tsx highlight="6-8"
 import { streamText } from 'ai';
 
 const result = streamText({
   model: yourModel,
   prompt: 'Invent a new holiday and describe its traditions.',
-  onFinish({ text, finishReason, usage }) {
+  onFinish({ text, finishReason, usage, response }) {
     // your own logic, e.g. for saving the chat history or recording usage
+
+    const messages = response.messages; // messages that were generated
   },
 });
 ```

content/docs/04-ai-sdk-ui/01-overview.mdx

@@ -26,8 +26,8 @@ Here is a comparison of the supported functions across these frameworks:
 | [useChat](/docs/reference/ai-sdk-ui/use-chat)             | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
 | [useChat](/docs/reference/ai-sdk-ui/use-chat) attachments | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | [useCompletion](/docs/reference/ai-sdk-ui/use-completion) | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
-| [useObject](/docs/reference/ai-sdk-ui/use-object)         | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
-| [useAssistant](/docs/reference/ai-sdk-ui/use-assistant)   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Cross size={18} /> |
+| [useObject](/docs/reference/ai-sdk-ui/use-object)         | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Check size={18} /> |
+| [useAssistant](/docs/reference/ai-sdk-ui/use-assistant)   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
 
 <Note>
   [Contributions](https://github.com/vercel/ai/blob/main/CONTRIBUTING.md) are

content/docs/07-reference/02-ai-sdk-ui/03-use-object.mdx

@@ -5,7 +5,10 @@ description: API reference for the useObject hook.
 
 # `experimental_useObject()`
 
-<Note>`useObject` is an experimental feature and only available in React.</Note>
+<Note>
+  `useObject` is an experimental feature and only available in React and
+  SolidJS.
+</Note>
 
 Allows you to consume text streams that represent a JSON object and parse them into a complete object based on a schema.
 You can use it together with [`streamObject`](/docs/reference/ai-sdk-core/stream-object) in the backend.

content/docs/07-reference/02-ai-sdk-ui/20-use-assistant.mdx

@@ -11,10 +11,6 @@ with the UI updated automatically as the assistant is streaming its execution.
 
 This works in conjunction with [`AssistantResponse`](./assistant-response) in the backend.
 
-<Note>
-  `useAssistant` is supported in `ai/react`, `ai/svelte`, and `ai/vue`.
-</Note>
-
 ## Import
 
 <Tabs items={['React', 'Svelte']}>

content/docs/07-reference/02-ai-sdk-ui/index.mdx

@@ -66,8 +66,8 @@ Here is a comparison of the supported functions across these frameworks:
 | [useChat](/docs/reference/ai-sdk-ui/use-chat)             | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
 | [useChat](/docs/reference/ai-sdk-ui/use-chat) attachments | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | [useCompletion](/docs/reference/ai-sdk-ui/use-completion) | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
-| [useObject](/docs/reference/ai-sdk-ui/use-object)         | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
-| [useAssistant](/docs/reference/ai-sdk-ui/use-assistant)   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Cross size={18} /> |
+| [useObject](/docs/reference/ai-sdk-ui/use-object)         | <Check size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Check size={18} /> |
+| [useAssistant](/docs/reference/ai-sdk-ui/use-assistant)   | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> | <Check size={18} /> |
 
 <Note>
   [Contributions](https://github.com/vercel/ai/blob/main/CONTRIBUTING.md) are

content/providers/01-ai-sdk-providers/08-amazon-bedrock.mdx

@@ -264,6 +264,13 @@ See the [Amazon Bedrock Guardrails documentation](https://docs.aws.amazon.com/be
 | `meta.llama2-70b-chat-v1`                   | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | `meta.llama3-8b-instruct-v1:0`              | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | `meta.llama3-70b-instruct-v1:0`             | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-1-8b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-1-70b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-1-405b-instruct-v1:0`          | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-2-1b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-2-3b-instruct-v1:0`            | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-2-11b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
+| `meta.llama3-2-90b-instruct-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | `mistral.mistral-7b-instruct-v0:2`          | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | `mistral.mixtral-8x7b-instruct-v0:1`        | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |
 | `mistral.mistral-large-2402-v1:0`           | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> | <Cross size={18} /> |

content/providers/05-observability/braintrust.mdx

@@ -0,0 +1,97 @@
+---
+title: Braintrust
+description: Monitoring and tracing LLM applications with Braintrust
+---
+
+# Braintrust Observability
+
+Braintrust is an end-to-end platform for building AI applications. When building with the AI SDK, you can integrate Braintrust to [log](https://www.braintrust.dev/docs/guides/logging), monitor, and take action on real-world interactions.
+
+## Setup
+
+### OpenTelemetry
+
+Braintrust supports [AI SDK telemetry data](/docs/ai-sdk-core/telemetry).
+To set up Braintrust as an [OpenTelemetry](https://opentelemetry.io/docs/) backend, you'll need to route the traces to Braintrust's OpenTelemetry endpoint, set your API key, and specify a parent project or experiment.
+
+Once you set up an [OpenTelemetry Protocol Exporter](https://opentelemetry.io/docs/languages/js/exporters/) (OTLP) to send traces to Braintrust, we automatically convert LLM calls into Braintrust `LLM` spans, which can be saved as [prompts](https://www.braintrust.dev/docs/guides/functions/prompts) and evaluated in the [playground](https://www.braintrust.dev/docs/guides/playground).
+
+To use the AI SDK to send telemetry data to Braintrust, set these environment variables in your Next.js app's `.env` file:
+
+```bash
+OTEL_EXPORTER_OTLP_ENDPOINT=https://api.braintrust.dev/otel
+OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <Your API Key>, x-bt-parent=project_id:<Your Project ID>"
+```
+
+You can then use the `experimental_telemetry` option to enable telemetry on supported AI SDK function calls:
+
+```typescript
+import { createOpenAI } from '@ai-sdk/openai';
+import { generateText } from 'ai';
+
+const openai = createOpenAI();
+
+async function main() {
+  const result = await generateText({
+    model: openai('gpt-4o-mini'),
+    prompt: 'What is 2 + 2?',
+    experimental_telemetry: {
+      isEnabled: true,
+      metadata: {
+        query: 'weather',
+        location: 'San Francisco',
+      },
+    },
+  });
+  console.log(result);
+}
+
+main();
+```
+
+Traced LLM calls will appear under the Braintrust project or experiment provided in the `x-bt-parent` header.
+
+### Model Wrapping
+
+You can wrap AI SDK models in Braintrust to automatically log your requests.
+
+```typescript
+import { initLogger, wrapAISDKModel } from 'braintrust';
+import { openai } from '@ai-sdk/openai';
+
+const logger = initLogger({
+  projectName: 'My Project',
+  apiKey: process.env.BRAINTRUST_API_KEY,
+});
+
+const model = wrapAISDKModel(openai.chat('gpt-3.5-turbo'));
+
+async function main() {
+  // This will automatically log the request, response, and metrics to Braintrust
+  const response = await model.doGenerate({
+    inputFormat: 'messages',
+    mode: {
+      type: 'regular',
+    },
+    prompt: [
+      {
+        role: 'user',
+        content: [{ type: 'text', text: 'What is the capital of France?' }],
+      },
+    ],
+  });
+  console.log(response);
+}
+
+main();
+```
+
+## Resources
+
+To see a step-by-step example, check out the Braintrust [cookbook](https://www.braintrust.dev/docs/cookbook/recipes/OTEL-logging).
+
+After you log your application in Braintrust, explore other workflows like:
+
+- Adding [tools](https://www.braintrust.dev/docs/guides/functions/tools) to your library and using them in [experiments](https://www.braintrust.dev/docs/guides/evals) and the [playground](https://www.braintrust.dev/docs/guides/playground)
+- Creating [custom scorers](https://www.braintrust.dev/docs/guides/functions/scorers) to assess the quality of your LLM calls
+- Adding your logs to a [dataset](https://www.braintrust.dev/docs/guides/datasets) and running evaluations comparing models and prompts

content/providers/05-observability/index.mdx

@@ -9,8 +9,9 @@ Several LLM observability providers offer integrations with the AI SDK telemetry
 
 - [Langfuse](https://langfuse.com/docs/integrations/vercel-ai-sdk)
 - [LangSmith](https://docs.smith.langchain.com/observability/how_to_guides/tracing/trace_with_vercel_ai_sdk)
-- [Braintrust](https://www.braintrust.dev/docs/cookbook/recipes/OTEL-logging)
+- [Braintrust](/providers/observability/braintrust)
 - [Laminar](https://docs.lmnr.ai/tracing/vercel-ai-sdk)
+- [HoneyHive](https://docs.honeyhive.ai/integrations/vercel)
 
 There are also providers that provide monitoring and tracing for the AI SDK through model wrappers:
 

examples/ai-core/package.json

@@ -3,20 +3,20 @@
   "version": "0.0.0",
   "private": true,
   "dependencies": {
-    "@ai-sdk/amazon-bedrock": "1.0.1",
-    "@ai-sdk/anthropic": "1.0.1",
-    "@ai-sdk/azure": "1.0.3",
-    "@ai-sdk/cohere": "1.0.1",
-    "@ai-sdk/google": "1.0.1",
-    "@ai-sdk/google-vertex": "1.0.1",
-    "@ai-sdk/groq": "1.0.1",
-    "@ai-sdk/mistral": "1.0.2",
-    "@ai-sdk/openai": "1.0.2",
-    "@ai-sdk/xai": "1.0.2",
+    "@ai-sdk/amazon-bedrock": "1.0.3",
+    "@ai-sdk/anthropic": "1.0.2",
+    "@ai-sdk/azure": "1.0.5",
+    "@ai-sdk/cohere": "1.0.3",
+    "@ai-sdk/google": "1.0.3",
+    "@ai-sdk/google-vertex": "1.0.3",
+    "@ai-sdk/groq": "1.0.3",
+    "@ai-sdk/mistral": "1.0.3",
+    "@ai-sdk/openai": "1.0.4",
+    "@ai-sdk/xai": "1.0.3",
     "@opentelemetry/sdk-node": "0.54.2",
     "@opentelemetry/auto-instrumentations-node": "0.47.0",
     "@opentelemetry/sdk-trace-node": "1.27.0",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "dotenv": "16.4.5",
     "mathjs": "12.4.2",
     "zod": "3.23.8",

examples/express/package.json

@@ -7,8 +7,8 @@
     "type-check": "tsc --noEmit"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
-    "ai": "4.0.2",
+    "@ai-sdk/openai": "1.0.4",
+    "ai": "4.0.3",
     "dotenv": "16.4.5",
     "express": "5.0.1"
   },

examples/fastify/package.json

@@ -3,8 +3,8 @@
   "version": "0.0.0",
   "private": true,
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
-    "ai": "4.0.2",
+    "@ai-sdk/openai": "1.0.4",
+    "ai": "4.0.3",
     "dotenv": "16.4.5",
     "fastify": "5.1.0"
   },

examples/hono/package.json

@@ -3,9 +3,9 @@
   "version": "0.0.0",
   "private": true,
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
+    "@ai-sdk/openai": "1.0.4",
     "@hono/node-server": "1.12.2",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "dotenv": "16.4.5",
     "hono": "4.6.9"
   },

examples/nest/package.json

@@ -15,11 +15,11 @@
     "lint": "eslint \"{src,apps,libs,test}/**/*.ts\" --fix"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
+    "@ai-sdk/openai": "1.0.4",
     "@nestjs/common": "^10.0.0",
     "@nestjs/core": "^10.0.0",
     "@nestjs/platform-express": "^10.4.8",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "reflect-metadata": "^0.2.0",
     "rxjs": "^7.8.1"
   },

examples/next-fastapi/package.json

@@ -11,8 +11,8 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/ui-utils": "1.0.1",
-    "ai": "4.0.2",
+    "@ai-sdk/ui-utils": "1.0.2",
+    "ai": "4.0.3",
     "geist": "^1.3.1",
     "next": "latest",
     "react": "^18",

examples/next-langchain/package.json

@@ -9,10 +9,10 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/react": "1.0.1",
+    "@ai-sdk/react": "1.0.2",
     "@langchain/openai": "0.0.28",
     "@langchain/core": "0.1.63",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "langchain": "0.1.36",
     "next": "latest",
     "react": "^18",

examples/next-openai-kasada-bot-protection/package.json

@@ -9,9 +9,9 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
+    "@ai-sdk/openai": "1.0.4",
     "@vercel/functions": "latest",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "next": "latest",
     "react": "^18",
     "react-dom": "^18",

examples/next-openai-pages/package.json

@@ -9,9 +9,9 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
-    "@ai-sdk/react": "1.0.1",
-    "ai": "4.0.2",
+    "@ai-sdk/openai": "1.0.4",
+    "@ai-sdk/react": "1.0.2",
+    "ai": "4.0.3",
     "next": "latest",
     "openai": "4.52.6",
     "react": "^18",

examples/next-openai-telemetry-sentry/package.json

@@ -9,15 +9,15 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
-    "@ai-sdk/react": "1.0.1",
+    "@ai-sdk/openai": "1.0.4",
+    "@ai-sdk/react": "1.0.2",
     "@opentelemetry/api-logs": "0.54.2",
     "@opentelemetry/instrumentation": "0.52.1",
     "@opentelemetry/sdk-logs": "0.54.2",
     "@sentry/nextjs": "^8.22.0",
     "@sentry/opentelemetry": "8.22.0",
     "@vercel/otel": "1.9.1",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "next": "latest",
     "openai": "4.52.6",
     "react": "^18",

examples/next-openai-telemetry/package.json

@@ -9,13 +9,13 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
-    "@ai-sdk/react": "1.0.1",
+    "@ai-sdk/openai": "1.0.4",
+    "@ai-sdk/react": "1.0.2",
     "@opentelemetry/api-logs": "0.54.2",
     "@opentelemetry/sdk-logs": "0.54.2",
     "@opentelemetry/instrumentation": "0.52.1",
     "@vercel/otel": "1.9.1",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "next": "latest",
     "openai": "4.52.6",
     "react": "^18",

examples/next-openai-upstash-rate-limits/package.json

@@ -9,10 +9,10 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "@ai-sdk/openai": "1.0.2",
+    "@ai-sdk/openai": "1.0.4",
     "@upstash/ratelimit": "^0.4.3",
     "@vercel/kv": "^0.2.2",
-    "ai": "4.0.2",
+    "ai": "4.0.3",
     "next": "latest",
     "react": "^18",
     "react-dom": "^18",

examples/next-openai/app/api/use-chat-tools/route.ts

@@ -19,6 +19,9 @@ export async function POST(req: Request) {
         description: 'show the weather in a given city to the user',
         parameters: z.object({ city: z.string() }),
         execute: async ({}: { city: string }) => {
+          // Add artificial delay of 2 seconds
+          await new Promise(resolve => setTimeout(resolve, 2000));
+
           const weatherOptions = ['sunny', 'cloudy', 'rainy', 'snowy', 'windy'];
           return weatherOptions[
             Math.floor(Math.random() * weatherOptions.length)

examples/next-openai/app/api/use-object/route.ts

@@ -9,7 +9,7 @@ export async function POST(req: Request) {
   const context = await req.json();
 
   const result = streamObject({
-    model: openai('gpt-4-turbo'),
+    model: openai('gpt-4o'),
     prompt: `Generate 3 notifications for a messages app in this context: ${context}`,
     schema: notificationSchema,
   });