diff --git a/js/examples/ai-sdk/next-openai-app/.env.local.example b/js/examples/ai-sdk/next-openai-app/.env.local.example deleted file mode 100644 index 75ec40d4a..000000000 --- a/js/examples/ai-sdk/next-openai-app/.env.local.example +++ /dev/null @@ -1,17 +0,0 @@ -# You must first activate a Billing Account here: https://platform.openai.com/account/billing/overview -# Then get your OpenAI API Key here: https://platform.openai.com/account/api-keys -OPENAI_API_KEY=xxxxxxx - -# You must first create an OpenAI Assistant here: https://platform.openai.com/assistants -# Then get your Assistant ID here: https://platform.openai.com/assistants -ASSISTANT_ID=xxxxxxx - -# If you choose to use external files for attachments, you will need to configure a Vercel Blob Store. -# Instructions to create a Vercel Blob Store here: https://vercel.com/docs/storage/vercel-blob -BLOB_READ_WRITE_TOKEN=xxxxxxx - -# Required for reasoning example -FIREWORKS_API_KEY=xxxxxxx - -# Required for resumable streams. You can create a Redis store here: https://vercel.com/marketplace/redis -REDIS_URL=xxxxxx diff --git a/js/examples/ai-sdk/next-openai-app/.gitignore b/js/examples/ai-sdk/next-openai-app/.gitignore deleted file mode 100644 index 4aa0ffe31..000000000 --- a/js/examples/ai-sdk/next-openai-app/.gitignore +++ /dev/null @@ -1,60 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* - -# local env files -.env*.local - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts - -# persistence -.chats -.streams - -# trim the create-next-app next-openai-app. if an example exists, we've modified it from the original to have braintrust tracing enabled -app/* -app/api/* -agent/* -tool/* -components/* -components/tool/* -util/* -lib/utils.ts - -!app/api -!app/stream-object -!app/test-openai-code-interpreter -!app/use-chat-streaming-tool-calls -!app/api/chat -!app/api/chat-openai-code-interpreter -!app/api/use-chat-streaming-tool-calls -!components/chat-input.tsx -!components/tool -!components/tool/openai-code-interpreter-view.tsx diff --git a/js/examples/ai-sdk/next-openai-app/README.md b/js/examples/ai-sdk/next-openai-app/README.md deleted file mode 100644 index 6354c7c8b..000000000 --- a/js/examples/ai-sdk/next-openai-app/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# AI SDK, Next.js, and OpenAI Chat Example + Braintrust - -> Slightly modified from the original example. We exclude many examples/files that we didn't modify to have Braintrust tracing. - -This example shows how to use the [AI SDK](https://ai-sdk.dev/docs) with [Next.js](https://nextjs.org/) and [OpenAI](https://openai.com) to create a ChatGPT-like AI-powered streaming chat bot. - -## Deploy your own - -Deploy the example using [Vercel](https://vercel.com?utm_source=github&utm_medium=readme&utm_campaign=ai-sdk-example): - -[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fvercel%2Fai%2Ftree%2Fmain%2Fexamples%2Fnext-openai&env=OPENAI_API_KEY&project-name=ai-sdk-next-openai&repository-name=ai-sdk-next-openai) - -## How to use - -Execute [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app) with [npm](https://docs.npmjs.com/cli/init), [Yarn](https://yarnpkg.com/lang/en/docs/cli/create/), or [pnpm](https://pnpm.io) to bootstrap the example: - -```bash -npx create-next-app --example https://github.com/vercel/ai/tree/main/examples/next-openai next-openai-app -``` - -```bash -yarn create next-app --example https://github.com/vercel/ai/tree/main/examples/next-openai next-openai-app -``` - -```bash -pnpm create next-app --example https://github.com/vercel/ai/tree/main/examples/next-openai next-openai-app -``` - -To run the example locally you need to: - -1. Sign up at [OpenAI's Developer Platform](https://platform.openai.com/signup). -2. Go to [OpenAI's dashboard](https://platform.openai.com/account/api-keys) and create an API KEY. -3. If you choose to use external files for attachments, then create a [Vercel Blob Store](https://vercel.com/docs/storage/vercel-blob). -4. Set the required environment variable as the token value as shown [the example env file](./.env.local.example) but in a new file called `.env.local` -5. `pnpm install` to install the required dependencies. -6. `pnpm dev` to launch the development server. - -## Learn More - -To learn more about OpenAI, Next.js, and the AI SDK take a look at the following resources: - -- [AI SDK docs](https://ai-sdk.dev/docs) -- [Vercel AI Playground](https://ai-sdk.dev/playground) -- [OpenAI Documentation](https://platform.openai.com/docs) - learn about OpenAI features and API. -- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. diff --git a/js/examples/ai-sdk/next-openai-app/app/api/chat-openai-code-interpreter/route.ts b/js/examples/ai-sdk/next-openai-app/app/api/chat-openai-code-interpreter/route.ts deleted file mode 100644 index b50ebe2f3..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/api/chat-openai-code-interpreter/route.ts +++ /dev/null @@ -1,42 +0,0 @@ -/* eslint-disable @typescript-eslint/no-explicit-any */ -import { openai, OpenAIResponsesProviderOptions } from "@ai-sdk/openai"; -import * as ai from "ai"; - -import type { InferUITools, ToolSet, UIDataTypes, UIMessage } from "ai"; -import { wrapAISDK, initLogger } from "braintrust"; - -initLogger({ projectName: "example-ai-sdk-next-openai-app" }); - -const { convertToModelMessages, streamText, validateUIMessages } = - (process.env.WRAPPED || "true") === "true" ? wrapAISDK(ai) : ai; - -const tools = { - code_interpreter: openai.tools.codeInterpreter(), -} satisfies ToolSet; - -export type OpenAICodeInterpreterMessage = UIMessage< - never, - UIDataTypes, - InferUITools ->; - -export async function POST(req: Request) { - const { messages } = await req.json(); - const uiMessages = await validateUIMessages({ messages }); - - const result = streamText({ - model: openai("gpt-5-nano"), - tools, - messages: convertToModelMessages(uiMessages), - onStepFinish: ({ request }: any) => { - console.log(JSON.stringify(request.body, null, 2)); - }, - providerOptions: { - openai: { - store: false, - } satisfies OpenAIResponsesProviderOptions, - }, - }); - - return result.toUIMessageStreamResponse(); -} diff --git a/js/examples/ai-sdk/next-openai-app/app/api/chat/route.ts b/js/examples/ai-sdk/next-openai-app/app/api/chat/route.ts deleted file mode 100644 index fe22c1ec3..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/api/chat/route.ts +++ /dev/null @@ -1,40 +0,0 @@ -/* eslint-disable @typescript-eslint/no-explicit-any */ -import { openai } from "@ai-sdk/openai"; - -// remember to build & then do a reinstall to see the latest -import { wrapAISDK, initLogger } from "braintrust"; - -import * as ai from "ai"; -import { type UIMessage } from "ai"; - -initLogger({ projectName: "example-ai-sdk-next-openai-app" }); - -const { streamText } = - (process.env.WRAPPED || "true") === "true" ? wrapAISDK(ai) : ai; - -const { consumeStream, convertToModelMessages } = ai; - -export const maxDuration = 30; - -export async function POST(req: Request) { - const { messages }: { messages: UIMessage[] } = await req.json(); - - const prompt = convertToModelMessages(messages); - - const result = streamText({ - model: openai("gpt-4o"), - prompt, - abortSignal: req.signal, - }); - - debugger; - - return result.toUIMessageStreamResponse({ - onFinish: async ({ isAborted }: any) => { - if (isAborted) { - console.log("Aborted"); - } - }, - consumeSseStream: consumeStream, // needed for correct abort handling - }); -} diff --git a/js/examples/ai-sdk/next-openai-app/app/api/use-chat-streaming-tool-calls/route.ts b/js/examples/ai-sdk/next-openai-app/app/api/use-chat-streaming-tool-calls/route.ts deleted file mode 100644 index a3fd9b5d8..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/api/use-chat-streaming-tool-calls/route.ts +++ /dev/null @@ -1,73 +0,0 @@ -import { openai } from "@ai-sdk/openai"; -import * as ai from "ai"; -import type { UIDataTypes, UIMessage } from "ai"; -import { z } from "zod"; -import { wrapAISDK, initLogger } from "braintrust"; - -initLogger({ projectName: "example-ai-sdk-next-openai-app" }); - -const { convertToModelMessages, streamText } = - (process.env.WRAPPED || "true") === "true" ? wrapAISDK(ai) : ai; - -// Allow streaming responses up to 30 seconds -export const maxDuration = 30; - -export type StreamingToolCallsMessage = UIMessage< - never, - UIDataTypes, - { - showWeatherInformation: { - input: { - city: string; - weather: string; - temperature: number; - typicalWeather: string; - }; - output: string; - }; - } ->; - -export async function POST(req: Request) { - const { messages } = await req.json(); - - const result = streamText({ - model: openai("gpt-4o"), - messages: convertToModelMessages(messages), - system: - "You are a helpful assistant that answers questions about the weather in a given city." + - "You use the showWeatherInformation tool to show the weather information to the user instead of talking about it.", - tools: { - // server-side tool with execute function: - getWeatherInformation: { - description: "show the weather in a given city to the user", - inputSchema: z.object({ city: z.string() }), - execute: async ({}: { city: string }) => { - const weatherOptions = ["sunny", "cloudy", "rainy", "snowy", "windy"]; - return { - weather: - weatherOptions[Math.floor(Math.random() * weatherOptions.length)], - temperature: Math.floor(Math.random() * 50 - 10), - }; - }, - }, - // client-side tool that displays weather information to the user: - showWeatherInformation: { - description: - "Show the weather information to the user. Always use this tool to tell weather information to the user.", - inputSchema: z.object({ - city: z.string(), - weather: z.string(), - temperature: z.number(), - typicalWeather: z - .string() - .describe( - "2-3 sentences about the typical weather in the city during spring.", - ), - }), - }, - }, - }); - - return result.toUIMessageStreamResponse(); -} diff --git a/js/examples/ai-sdk/next-openai-app/app/stream-object/actions.ts b/js/examples/ai-sdk/next-openai-app/app/stream-object/actions.ts deleted file mode 100644 index 3faf5469e..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/stream-object/actions.ts +++ /dev/null @@ -1,32 +0,0 @@ -"use server"; - -import { openai } from "@ai-sdk/openai"; -import * as ai from "ai"; -import { wrapAISDK, initLogger } from "braintrust"; -import { createStreamableValue } from "@ai-sdk/rsc"; -import { PartialNotification, notificationSchema } from "./schema"; - -initLogger({ projectName: "example-ai-sdk-next-openai-app" }); - -const { streamObject } = - (process.env.WRAPPED || "true") === "true" ? wrapAISDK(ai) : ai; - -export async function generateNotifications(context: string) { - const notificationsStream = createStreamableValue(); - - const result = streamObject({ - model: openai("gpt-5-mini"), - prompt: `Generate 3 notifications for a messages app in this context: ${context}`, - schema: notificationSchema, - }); - - try { - for await (const partialObject of result.partialObjectStream) { - notificationsStream.update(partialObject); - } - } finally { - notificationsStream.done(); - } - - return notificationsStream.value; -} diff --git a/js/examples/ai-sdk/next-openai-app/app/stream-object/page.tsx b/js/examples/ai-sdk/next-openai-app/app/stream-object/page.tsx deleted file mode 100644 index 75d9dd2e4..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/stream-object/page.tsx +++ /dev/null @@ -1,67 +0,0 @@ -"use client"; - -import { StreamableValue, useStreamableValue } from "@ai-sdk/rsc"; -import { useState } from "react"; -import { generateNotifications } from "./actions"; -import { PartialNotification } from "./schema"; - -// Allow streaming responses up to 30 seconds -export const maxDuration = 30; - -// page component with a button to generate notifications -export default function Page() { - const [notificationStream, setNotificationStream] = - useState | null>(null); - - return ( -
- - - {notificationStream && ( - - )} -
- ); -} - -// separate component to display notifications that received the streamable value: -function NotificationsView({ - notificationStream, -}: { - notificationStream: StreamableValue; -}) { - const [data, pending, error] = useStreamableValue(notificationStream); - - return ( -
- {data?.notifications?.map((notification, index) => ( -
-
-
-

{notification?.name}

-

- {notification?.minutesAgo} - {notification?.minutesAgo != null ? " minutes ago" : ""} -

-
-

- {notification?.message} -

-
-
- ))} -
- ); -} diff --git a/js/examples/ai-sdk/next-openai-app/app/stream-object/schema.ts b/js/examples/ai-sdk/next-openai-app/app/stream-object/schema.ts deleted file mode 100644 index ce8e131f6..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/stream-object/schema.ts +++ /dev/null @@ -1,16 +0,0 @@ -import { DeepPartial } from "ai"; -import { z } from "zod"; - -// define a schema for the notifications -export const notificationSchema = z.object({ - notifications: z.array( - z.object({ - name: z.string().describe("Name of a fictional person."), - message: z.string().describe("Message. Do not use emojis or links."), - minutesAgo: z.number(), - }), - ), -}); - -// define a type for the partial notifications during generation -export type PartialNotification = DeepPartial; diff --git a/js/examples/ai-sdk/next-openai-app/app/test-openai-code-interpreter/page.tsx b/js/examples/ai-sdk/next-openai-app/app/test-openai-code-interpreter/page.tsx deleted file mode 100644 index bf239391d..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/test-openai-code-interpreter/page.tsx +++ /dev/null @@ -1,38 +0,0 @@ -"use client"; - -import { useChat } from "@ai-sdk/react"; -import { DefaultChatTransport } from "ai"; -import ChatInput from "@/components/chat-input"; -import { OpenAICodeInterpreterMessage } from "@/app/api/chat-openai-code-interpreter/route"; -import CodeInterpreterView from "@/components/tool/openai-code-interpreter-view"; - -export default function TestOpenAIWebSearch() { - const { status, sendMessage, messages } = - useChat({ - transport: new DefaultChatTransport({ - api: "/api/chat-openai-code-interpreter", - }), - }); - - return ( -
-

OpenAI Code Interpreter Test

- - {messages.map((message) => ( -
- {message.role === "user" ? "User: " : "AI: "} - {message.parts.map((part, index) => { - switch (part.type) { - case "text": - return
{part.text}
; - case "tool-code_interpreter": - return ; - } - })} -
- ))} - - sendMessage({ text })} /> -
- ); -} diff --git a/js/examples/ai-sdk/next-openai-app/app/use-chat-streaming-tool-calls/page.tsx b/js/examples/ai-sdk/next-openai-app/app/use-chat-streaming-tool-calls/page.tsx deleted file mode 100644 index dd3e48206..000000000 --- a/js/examples/ai-sdk/next-openai-app/app/use-chat-streaming-tool-calls/page.tsx +++ /dev/null @@ -1,79 +0,0 @@ -"use client"; - -import { useChat } from "@ai-sdk/react"; -import ChatInput from "@/components/chat-input"; -import { - DefaultChatTransport, - lastAssistantMessageIsCompleteWithToolCalls, -} from "ai"; -import { StreamingToolCallsMessage } from "../api/use-chat-streaming-tool-calls/route"; - -export default function Chat() { - const { messages, status, sendMessage, addToolOutput } = - useChat({ - transport: new DefaultChatTransport({ - api: "/api/use-chat-streaming-tool-calls", - }), - - sendAutomaticallyWhen: lastAssistantMessageIsCompleteWithToolCalls, - - // run client-side tools that are automatically executed: - async onToolCall({ toolCall }) { - if (toolCall.toolName === "showWeatherInformation") { - // display tool. add tool result that informs the llm that the tool was executed. - addToolOutput({ - tool: "showWeatherInformation", - toolCallId: toolCall.toolCallId, - output: "Weather information was shown to the user.", - }); - } - }, - }); - - // used to only render the role when it changes: - let lastRole: string | undefined = undefined; - - return ( -
- {messages?.map((m) => { - const isNewRole = m.role !== lastRole; - lastRole = m.role; - - return ( -
- {isNewRole && {`${m.role}: `}} - {m.parts.map((part) => { - if (part.type === "text") { - return part.text; - } - - if (part.type === "tool-showWeatherInformation") { - return ( -
-

{part.input?.city ?? ""}

-
-
- {part.input?.weather && {part.input.weather}} - {part.input?.temperature && ( - {part.input.temperature} °C - )} -
- {part.input?.typicalWeather && ( -
{part.input.typicalWeather}
- )} -
-
- ); - } - })} -
- ); - })} - - sendMessage({ text })} /> -
- ); -} diff --git a/js/examples/ai-sdk/next-openai-app/components.json b/js/examples/ai-sdk/next-openai-app/components.json deleted file mode 100644 index 36ef86ed5..000000000 --- a/js/examples/ai-sdk/next-openai-app/components.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "$schema": "https://ui.shadcn.com/schema.json", - "style": "new-york", - "rsc": true, - "tsx": true, - "tailwind": { - "config": "tailwind.config.js", - "css": "app/globals.css", - "baseColor": "neutral", - "cssVariables": true, - "prefix": "" - }, - "iconLibrary": "lucide", - "aliases": { - "components": "@/components", - "utils": "@/lib/utils", - "ui": "@/components/ui", - "lib": "@/lib", - "hooks": "@/hooks" - }, - "registries": {} -} diff --git a/js/examples/ai-sdk/next-openai-app/components/chat-input.tsx b/js/examples/ai-sdk/next-openai-app/components/chat-input.tsx deleted file mode 100644 index c2e6a263f..000000000 --- a/js/examples/ai-sdk/next-openai-app/components/chat-input.tsx +++ /dev/null @@ -1,41 +0,0 @@ -import { useState } from "react"; - -export default function ChatInput({ - status, - onSubmit, - stop, -}: { - status: string; - onSubmit: (text: string) => void; - stop?: () => void; -}) { - const [text, setText] = useState(""); - - return ( -
{ - e.preventDefault(); - if (text.trim() === "") return; - onSubmit(text); - setText(""); - }} - > - setText(e.target.value)} - /> - {stop && (status === "streaming" || status === "submitted") && ( - - )} -
- ); -} diff --git a/js/examples/ai-sdk/next-openai-app/components/tool/openai-code-interpreter-view.tsx b/js/examples/ai-sdk/next-openai-app/components/tool/openai-code-interpreter-view.tsx deleted file mode 100644 index 84dae0789..000000000 --- a/js/examples/ai-sdk/next-openai-app/components/tool/openai-code-interpreter-view.tsx +++ /dev/null @@ -1,47 +0,0 @@ -import { openai } from "@ai-sdk/openai"; -import { UIToolInvocation } from "ai"; - -export default function CodeInterpreterView({ - invocation, -}: { - invocation: UIToolInvocation>; -}) { - return ( -
-
-
- {invocation.input?.containerId} -
-
- -
-
-
Code:
- 
-            {invocation.input?.code}
-
-
- - {invocation.state === "output-available" && ( -
-
- Output: -
-
- {invocation.output.outputs?.map((output, index) => ( -
- {output.type === "logs" && ( -
- {output.logs} -
- )} - {output.type === "image" && } -
- ))} -
-
- )} -
-
- ); -} diff --git a/js/examples/ai-sdk/next-openai-app/next.config.js b/js/examples/ai-sdk/next-openai-app/next.config.js deleted file mode 100644 index 658404ac6..000000000 --- a/js/examples/ai-sdk/next-openai-app/next.config.js +++ /dev/null @@ -1,4 +0,0 @@ -/** @type {import('next').NextConfig} */ -const nextConfig = {}; - -module.exports = nextConfig; diff --git a/js/examples/ai-sdk/next-openai-app/package.json b/js/examples/ai-sdk/next-openai-app/package.json deleted file mode 100644 index f90c51428..000000000 --- a/js/examples/ai-sdk/next-openai-app/package.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "name": "@example/next-openai", - "version": "0.0.0", - "private": true, - "scripts": { - "dev": "next dev", - "build": "next build", - "start": "next start", - "lint": "next lint" - }, - "dependencies": { - "@ai-sdk/amazon-bedrock": "4.0.0-beta.55", - "@ai-sdk/anthropic": "3.0.0-beta.50", - "@ai-sdk/cohere": "3.0.0-beta.32", - "@ai-sdk/deepseek": "2.0.0-beta.32", - "@ai-sdk/fireworks": "2.0.0-beta.32", - "@ai-sdk/google": "3.0.0-beta.39", - "@ai-sdk/google-vertex": "4.0.0-beta.59", - "@ai-sdk/groq": "3.0.0-beta.31", - "@ai-sdk/mcp": "1.0.0-beta.13", - "@ai-sdk/mistral": "3.0.0-beta.32", - "@ai-sdk/openai": "3.0.0-beta.54", - "@ai-sdk/perplexity": "3.0.0-beta.31", - "@ai-sdk/provider-utils": "4.0.0-beta.31", - "@ai-sdk/react": "3.0.0-beta.94", - "@ai-sdk/rsc": "2.0.0-beta.94", - "@ai-sdk/xai": "3.0.0-beta.35", - "@modelcontextprotocol/sdk": "^1.10.2", - "@radix-ui/react-collapsible": "^1.1.12", - "@radix-ui/react-use-controllable-state": "^1.2.2", - "@valibot/to-json-schema": "^1.3.0", - "@vercel/blob": "^0.26.0", - "@vercel/sandbox": "^0.0.21", - "ai": "6.0.0-beta.94", - "braintrust": "file:../../..", - "class-variance-authority": "^0.7.1", - "clsx": "^2.1.1", - "lucide-react": "^0.545.0", - "next": "^15.5.9", - "react": "^18", - "react-dom": "^18", - "react-markdown": "9.0.1", - "redis": "^4.7.0", - "resumable-stream": "^2.2.0", - "streamdown": "^1.4.0", - "tailwind-merge": "^3.0.2", - "tailwindcss-animate": "^1.0.7", - "valibot": "1.4.0", - "zod": "3.25.76" - }, - "devDependencies": { - "@types/node": "20.17.24", - "@types/react": "^18", - "@types/react-dom": "^18", - "autoprefixer": "^10.4.14", - "eslint": "8.57.1", - "eslint-config-next": "14.2.3", - "postcss": "^8.4.49", - "tailwindcss": "^3.4.15", - "typescript": "5.8.3" - } -} diff --git a/js/examples/ai-sdk/next-openai-app/pnpm-lock.yaml b/js/examples/ai-sdk/next-openai-app/pnpm-lock.yaml deleted file mode 100644 index 5130b6a5f..000000000 --- a/js/examples/ai-sdk/next-openai-app/pnpm-lock.yaml +++ /dev/null @@ -1,8965 +0,0 @@ -lockfileVersion: '9.0' - -settings: - autoInstallPeers: true - excludeLinksFromLockfile: false - -importers: - - .: - dependencies: - '@ai-sdk/amazon-bedrock': - specifier: 4.0.0-beta.55 - version: 4.0.0-beta.55(@valibot/to-json-schema@1.6.0(valibot@1.4.0(typescript@5.8.3)))(zod@3.25.76) - '@ai-sdk/anthropic': - specifier: 3.0.0-beta.50 - version: 3.0.0-beta.50(@valibot/to-json-schema@1.6.0(valibot@1.4.0(typescript@5.8.3)))(zod@3.25.76) - 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plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -}; diff --git a/js/examples/ai-sdk/next-openai-app/tailwind.config.js b/js/examples/ai-sdk/next-openai-app/tailwind.config.js deleted file mode 100644 index 33fba7b24..000000000 --- a/js/examples/ai-sdk/next-openai-app/tailwind.config.js +++ /dev/null @@ -1,66 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -module.exports = { - darkMode: ["class"], - content: [ - "./pages/**/*.{js,ts,jsx,tsx,mdx}", - "./components/**/*.{js,ts,jsx,tsx,mdx}", - "./app/**/*.{js,ts,jsx,tsx,mdx}", - ], - theme: { - extend: { - backgroundImage: { - "gradient-radial": "radial-gradient(var(--tw-gradient-stops))", - "gradient-conic": - "conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))", - }, - borderRadius: { - lg: "var(--radius)", - md: "calc(var(--radius) - 2px)", - sm: "calc(var(--radius) - 4px)", - }, - colors: { - background: "hsl(var(--background))", - foreground: "hsl(var(--foreground))", - card: { - DEFAULT: "hsl(var(--card))", - foreground: "hsl(var(--card-foreground))", - }, - popover: { - DEFAULT: "hsl(var(--popover))", - foreground: "hsl(var(--popover-foreground))", - }, - primary: { - DEFAULT: "hsl(var(--primary))", - foreground: "hsl(var(--primary-foreground))", - }, - secondary: { - DEFAULT: "hsl(var(--secondary))", - foreground: "hsl(var(--secondary-foreground))", - }, - muted: { - DEFAULT: "hsl(var(--muted))", - foreground: "hsl(var(--muted-foreground))", - }, - accent: { - DEFAULT: "hsl(var(--accent))", - foreground: "hsl(var(--accent-foreground))", - }, - destructive: { - DEFAULT: "hsl(var(--destructive))", - foreground: "hsl(var(--destructive-foreground))", - }, - border: "hsl(var(--border))", - input: "hsl(var(--input))", - ring: "hsl(var(--ring))", - chart: { - 1: "hsl(var(--chart-1))", - 2: "hsl(var(--chart-2))", - 3: "hsl(var(--chart-3))", - 4: "hsl(var(--chart-4))", - 5: "hsl(var(--chart-5))", - }, - }, - }, - }, - plugins: [require("tailwindcss-animate")], -}; diff --git a/js/examples/ai-sdk/next-openai-app/tsconfig.json b/js/examples/ai-sdk/next-openai-app/tsconfig.json deleted file mode 100644 index ad0898613..000000000 --- a/js/examples/ai-sdk/next-openai-app/tsconfig.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "lib": ["dom", "dom.iterable", "esnext"], - "allowJs": true, - "skipLibCheck": true, - "strict": true, - "forceConsistentCasingInFileNames": true, - "esModuleInterop": true, - "module": "esnext", - "moduleResolution": "Bundler", - "resolveJsonModule": true, - "isolatedModules": true, - "jsx": "preserve", - "incremental": true, - "plugins": [ - { - "name": "next" - } - ], - "paths": { - "@/*": ["./*"], - "@component/*": ["./component/*"], - "@util/*": ["./util/*"] - }, - "composite": true, - "noEmit": true - }, - "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], - "exclude": ["node_modules"], - "references": [ - { - "path": "../../packages/ai" - }, - { - "path": "../../packages/rsc" - }, - { - "path": "../../packages/anthropic" - }, - { - "path": "../../packages/deepseek" - }, - { - "path": "../../packages/fireworks" - }, - { - "path": "../../packages/google" - }, - { - "path": "../../packages/google-vertex" - }, - { - "path": "../../packages/openai" - }, - { - "path": "../../packages/perplexity" - }, - { - "path": "../../packages/react" - } - ] -} diff --git a/js/examples/ai-sdk/stopWhen.ts b/js/examples/ai-sdk/stopWhen.ts deleted file mode 100644 index 3ac971f10..000000000 --- a/js/examples/ai-sdk/stopWhen.ts +++ /dev/null @@ -1,57 +0,0 @@ -/* eslint-disable @typescript-eslint/no-explicit-any */ -import { z } from "zod"; -import * as ai from "ai"; -import { wrapAISDK } from "ai-sdk"; -import { initLogger } from "../../../logger"; -import { configureNode } from "../../../node/config"; -import { openai } from "@ai-sdk/openai"; - -configureNode(); - -const SHOULD_WRAP = (process.env.WRAPPED || "true") === "true"; - -const { generateText, tool, hasToolCall } = SHOULD_WRAP ? wrapAISDK(ai) : ai; - -initLogger({ projectName: "examples-ai-sdk-stopWhen" }); - -const main = async () => { - const example = await generateText({ - model: openai("gpt-4o"), - tools: { - weather: tool({ - description: "Get the weather in a location", - inputSchema: z.object({ - location: z.string().describe("The location to get the weather for"), - }), - execute: async ({ location }: any) => ({ - location, - temperature: 72 + Math.floor(Math.random() * 21) - 10, - }), - }), - submitAnswer: tool({ - description: "Submit the final answer to the user", - inputSchema: z.object({ - answer: z.string().describe("The final answer to submit"), - }), - execute: async ({ answer }: any) => ({ - submitted: true, - answer, - }), - }), - }, - stopWhen: hasToolCall("weather"), // Stop when submitAnswer is called - prompt: - "What is the weather in San Francisco? After you get the weather, use the submitAnswer tool to provide your final response.", - }); - - console.log("Steps taken:", example.steps.length); - console.log("Response:", example.text); - console.log( - "submitAnswer called:", - example.steps.some((step: any) => - step.toolCalls?.some((tc: any) => tc.toolName === "submitAnswer"), - ), - ); -}; - -main().catch(console.error); diff --git a/js/examples/ai-sdk/stream.ts b/js/examples/ai-sdk/stream.ts deleted file mode 100644 index e64a69924..000000000 --- a/js/examples/ai-sdk/stream.ts +++ /dev/null @@ -1,44 +0,0 @@ -import { openai } from "@ai-sdk/openai"; -import * as ai from "ai"; -import { z } from "zod"; -import { Readable } from "node:stream"; -import { initLogger, wrapAISDK } from "../../src"; - -initLogger({ - projectName: "My AI Project", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -const { streamObject } = wrapAISDK(ai); - -export const notificationSchema = z.object({ - notifications: z.array( - z.object({ - name: z.string().describe("Name of a fictional person."), - message: z.string().describe("Message. Do not use emojis or links."), - }), - ), -}); - -async function main() { - const result = streamObject({ - model: openai("gpt-4.1"), - schema: notificationSchema, - prompt: `Generate 3 notifications for a messages app in this context: Messages during finals week.`, - }); - - const response = result.toTextStreamResponse(); - - const chunks = []; - if (response.body) { - const stream = Readable.fromWeb(response.body as any); - - for await (const chunk of stream) { - chunks.push(chunk.toString()); - } - } - - console.log(JSON.parse(chunks.join(""))); -} - -main().catch(console.error); diff --git a/js/examples/anthropic_beta_example.ts b/js/examples/anthropic_beta_example.ts deleted file mode 100755 index b449ed5da..000000000 --- a/js/examples/anthropic_beta_example.ts +++ /dev/null @@ -1,59 +0,0 @@ -#!/usr/bin/env tsx - -import Anthropic from "@anthropic-ai/sdk"; - -import { wrapAnthropic, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const client = wrapAnthropic(new Anthropic()); - -async function main() { - console.log("\nPrompt: What is 6 squared?"); - const result = await client.beta.messages.create({ - messages: [{ role: "user", content: "What is 6 squared?" }], - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - system: [{ text: "Just return the number please.", type: "text" }], - }); - console.dir(result); - - // Using create with stream=true - console.log("\nPrompt: What is 7 squared?"); - const stream1 = await client.beta.messages.create({ - messages: [{ role: "user", content: "What is 7 squared?" }], - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - system: [{ text: "Just return the number please.", type: "text" }], - stream: true, - }); - for await (const chunk of stream1) { - if ( - chunk.type === "content_block_delta" && - chunk.delta.type === "text_delta" - ) { - console.log(chunk.delta.text); - } - } - console.log(); - - // Using messages.stream - console.log("\nPrompt: What is 8 squared?"); - const stream2 = client.beta.messages.stream({ - messages: [{ role: "user", content: "What is 8 squared?" }], - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - system: [{ text: "Just return the number please.", type: "text" }], - }); - for await (const chunk of stream2) { - if ( - chunk.type === "content_block_delta" && - chunk.delta.type === "text_delta" - ) { - console.log(chunk.delta.text); - } - } - console.log(); -} - -main(); diff --git a/js/examples/anthropic_example.ts b/js/examples/anthropic_example.ts deleted file mode 100755 index 4e2272cc2..000000000 --- a/js/examples/anthropic_example.ts +++ /dev/null @@ -1,35 +0,0 @@ -#!/usr/bin/env tsx - -import Anthropic from "@anthropic-ai/sdk"; - -import { wrapAnthropic, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const client = wrapAnthropic(new Anthropic()); - -async function main() { - const result = await client.messages.create({ - messages: [ - { - role: "user", - content: "Hey Claude!?", - }, - ], - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - system: [ - { - text: "do this!", - type: "text", - }, - { - text: "do that!", - type: "text", - }, - ], - }); - console.dir(result); -} - -main(); diff --git a/js/examples/anthropic_streaming_example.ts b/js/examples/anthropic_streaming_example.ts deleted file mode 100755 index 20919b81c..000000000 --- a/js/examples/anthropic_streaming_example.ts +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env tsx - -import Anthropic from "@anthropic-ai/sdk"; -import { initLogger, wrapAnthropic } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const client = wrapAnthropic(new Anthropic()); - -async function main() { - const stream = client.messages - .stream({ - messages: [ - { - role: "user", - content: "What is Shakespeare's Sonnet 18?", - }, - ], - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - }) - // Once a content block is fully streamed, this event will fire - .on("contentBlock", (content) => console.log("contentBlock", content)) - // Once a message is fully streamed, this event will fire - .on("message", (message) => console.log("message", message)); - - for await (const event of stream) { - console.log("event", event); - } - - const message = await stream.finalMessage(); - console.log("finalMessage", message); -} - -main().catch((err) => { - console.error(err); -}); diff --git a/js/examples/anthropic_tools_example.ts b/js/examples/anthropic_tools_example.ts deleted file mode 100755 index f64bc0213..000000000 --- a/js/examples/anthropic_tools_example.ts +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env tsx - -import Anthropic from "@anthropic-ai/sdk"; -import { wrapAnthropic, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const client = wrapAnthropic(new Anthropic()); - -async function main() { - const userMessage: Anthropic.MessageParam = { - role: "user", - content: "What is the weather in Toronto?", - }; - const tools: Anthropic.Tool[] = [ - { - name: "get_weather", - description: "Get the weather for a specific location", - input_schema: { - type: "object", - properties: { location: { type: "string" } }, - }, - }, - ]; - - const message = await client.messages.create({ - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - messages: [userMessage], - tools, - }); - console.log("Initial response:"); - console.dir(message, { depth: 4 }); - - const tool = message.content.find( - (content): content is Anthropic.ToolUseBlock => content.type === "tool_use", - ); - if (!tool) { - throw new Error("No tool used"); - } - - const result = await client.messages.create({ - model: "claude-3-5-sonnet-latest", - max_tokens: 1024, - messages: [ - userMessage, - { role: message.role, content: message.content }, - { - role: "user", - content: [ - { - type: "tool_result", - tool_use_id: tool.id, - content: [{ type: "text", text: "The weather is 73f" }], - }, - ], - }, - ], - tools, - }); - console.log("\nFinal response"); - console.dir(result, { depth: 4 }); -} - -main(); diff --git a/js/examples/auto-instrumentation/.env.example b/js/examples/auto-instrumentation/.env.example deleted file mode 100644 index a895eee57..000000000 --- a/js/examples/auto-instrumentation/.env.example +++ /dev/null @@ -1,16 +0,0 @@ -# Copy this file to .env and fill in your API keys - -# Braintrust API key (get from https://www.braintrust.dev) -BRAINTRUST_API_KEY= - -# OpenAI API key (get from https://platform.openai.com) -# Required for: openai-example.js, openai-streaming-example.js, vercel-ai-sdk-example.js -OPENAI_API_KEY= - -# Anthropic API key (get from https://console.anthropic.com) -# Required for: anthropic-example.js, claude-agent-sdk-example.js -ANTHROPIC_API_KEY= - -# Google GenAI API key (get from https://aistudio.google.com/apikey) -# Required for: google-genai-example.js -GOOGLE_GENAI_API_KEY= diff --git a/js/examples/auto-instrumentation/.envrc b/js/examples/auto-instrumentation/.envrc deleted file mode 100644 index 2c57cdac5..000000000 --- a/js/examples/auto-instrumentation/.envrc +++ /dev/null @@ -1 +0,0 @@ -dotenv_if_exists diff --git a/js/examples/auto-instrumentation/.gitignore b/js/examples/auto-instrumentation/.gitignore deleted file mode 100644 index 6bfae0721..000000000 --- a/js/examples/auto-instrumentation/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -# Environment variables -.env - -# Dependencies -node_modules/ -package-lock.json - -# Logs -*.log -npm-debug.log* - -# OS files -.DS_Store diff --git a/js/examples/auto-instrumentation/README.md b/js/examples/auto-instrumentation/README.md deleted file mode 100644 index 92f5f2396..000000000 --- a/js/examples/auto-instrumentation/README.md +++ /dev/null @@ -1,128 +0,0 @@ -# Auto-Instrumentation Examples - -This directory demonstrates how to use Braintrust's auto-instrumentation feature with various AI SDKs. Auto-instrumentation automatically traces your AI SDK calls without needing to manually wrap clients. - -## Prerequisites - -### 1. Install Dependencies - -```bash -npm install -``` - -This will install all required dependencies including `@braintrust/auto-instrumentations` and the various AI SDKs. - -### 2. Set Up Environment Variables - -**Important:** Before running any examples, you must create a `.env` file with your API keys: - -```bash -cp .env.example .env -``` - -Then edit `.env` and add your API keys: - -```bash -BRAINTRUST_API_KEY=your_braintrust_key_here -OPENAI_API_KEY=your_openai_key_here -ANTHROPIC_API_KEY=your_anthropic_key_here -GOOGLE_GENAI_API_KEY=your_google_key_here -``` - -The examples use [mise](https://mise.jdx.dev/) (or [direnv](https://direnv.net/)) to automatically load environment variables from the `.env` file when you `cd` into this directory. The `.envrc` file is configured to do this automatically. - -Get your API keys from: - -- **Braintrust**: https://www.braintrust.dev -- **OpenAI**: https://platform.openai.com -- **Anthropic**: https://console.anthropic.com -- **Google GenAI**: https://aistudio.google.com/apikey - -## Supported SDKs - -The examples demonstrate auto-instrumentation with: - -- **OpenAI SDK** - Chat completions, embeddings, streaming -- **Anthropic SDK** - Messages API, streaming -- **Vercel AI SDK** - Generate text and objects -- **Google GenAI SDK** - Gemini models -- **Claude Agent SDK** - Agent queries with tools (uses Anthropic API) - -## Running Examples - -Each example can be run using the provided npm scripts, which automatically use the `--import` flag to enable auto-instrumentation: - -```bash -npm run openai # OpenAI chat completion -npm run openai-streaming # OpenAI streaming -npm run anthropic # Anthropic messages -npm run vercel # Vercel AI SDK -npm run google # Google GenAI -npm run claude-agent # Claude Agent SDK -``` - -Or run them directly: - -```bash -node --import @braintrust/auto-instrumentations/hook.mjs openai-example.js -``` - -## How It Works - -1. **No manual wrapping needed** - Unlike the traditional approach where you call `wrapOpenAI()` or `wrapAnthropic()`, auto-instrumentation handles this automatically -2. **Just call `initLogger()`** - Initialize Braintrust logging at the start of your application -3. **Use SDKs normally** - Create and use AI SDK clients as you normally would -4. **Automatic tracing** - All API calls are automatically traced to Braintrust - -## Configuration - -### Disable Specific Integrations - -You can disable auto-instrumentation for specific SDKs: - -**Via Environment Variable:** - -```bash -BRAINTRUST_DISABLE_INSTRUMENTATION=openai npm run anthropic -``` - -**Programmatically:** - -```typescript -import { configureInstrumentation } from "braintrust"; - -configureInstrumentation({ - integrations: { - openai: false, - }, -}); -``` - -## Comparing with Manual Wrapping - -**Traditional approach (manual wrapping):** - -```typescript -import { wrapOpenAI, initLogger } from "braintrust"; -import OpenAI from "openai"; - -initLogger({ projectName: "my-project" }); -const client = wrapOpenAI(new OpenAI()); // Manual wrapping required -``` - -**Auto-instrumentation approach:** - -```typescript -import { initLogger } from "braintrust"; -import OpenAI from "openai"; - -initLogger({ projectName: "my-project" }); -const client = new OpenAI(); // No wrapping needed! -``` - -## More Information - -See the main documentation at: - -- [Auto-Instrumentation Package](../../integrations/auto-instrumentations/README.md) -- [Braintrust Docs](https://www.braintrust.dev/docs) diff --git a/js/examples/auto-instrumentation/anthropic-example.js b/js/examples/auto-instrumentation/anthropic-example.js deleted file mode 100644 index 841ccb2af..000000000 --- a/js/examples/auto-instrumentation/anthropic-example.js +++ /dev/null @@ -1,55 +0,0 @@ -/** - * Anthropic Auto-Instrumentation Example - * - * This example demonstrates using Anthropic SDK with Braintrust auto-instrumentation. - * No manual wrapping is needed - just initialize the logger and use Anthropic normally. - * - * Run with: npm run anthropic - * Or: node --import @braintrust/auto-instrumentations/hook.mjs anthropic-example.js - */ - -import { initLogger } from "braintrust"; -import Anthropic from "@anthropic-ai/sdk"; - -// Check for required API keys -if (!process.env.ANTHROPIC_API_KEY) { - console.error("Error: ANTHROPIC_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Create Anthropic client normally - no wrapping needed! -const anthropic = new Anthropic({ - apiKey: process.env.ANTHROPIC_API_KEY, -}); - -async function main() { - console.log("Making Anthropic messages request..."); - - // This call will be automatically traced to Braintrust - const message = await anthropic.messages.create({ - model: "claude-sonnet-4-5", - max_tokens: 1024, - messages: [ - { - role: "user", - content: "Explain what auto-instrumentation is in one sentence.", - }, - ], - }); - - const response = message.content[0]; - console.log("\nResponse:", response.type === "text" ? response.text : ""); - console.log("\nTokens used:"); - console.log(" Input:", message.usage.input_tokens); - console.log(" Output:", message.usage.output_tokens); - console.log("\n✅ Request automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/auto-instrumentation/claude-agent-sdk-example.js b/js/examples/auto-instrumentation/claude-agent-sdk-example.js deleted file mode 100644 index b60f1abf5..000000000 --- a/js/examples/auto-instrumentation/claude-agent-sdk-example.js +++ /dev/null @@ -1,102 +0,0 @@ -/** - * Claude Agent SDK Auto-Instrumentation Example - * - * This example demonstrates using Claude Agent SDK with Braintrust auto-instrumentation. - * Agent queries will be automatically traced. - * - * Run with: npm run claude-agent - * Or: node --import @braintrust/auto-instrumentations/hook.mjs claude-agent-sdk-example.js - */ - -import { initLogger } from "braintrust"; -import { query, tool } from "@anthropic-ai/claude-agent-sdk"; -import { z } from "zod"; - -// Check for required API keys -if (!process.env.ANTHROPIC_API_KEY) { - console.error("Error: ANTHROPIC_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Create a simple calculator tool -const calculator = tool( - "calculator", - "Performs basic arithmetic operations (add, subtract, multiply, divide)", - { - operation: z.enum(["add", "subtract", "multiply", "divide"]), - a: z.number(), - b: z.number(), - }, - async (args) => { - let result; - switch (args.operation) { - case "add": - result = args.a + args.b; - break; - case "subtract": - result = args.a - args.b; - break; - case "multiply": - result = args.a * args.b; - break; - case "divide": - result = args.a / args.b; - break; - } - return { - content: [ - { - type: "text", - text: `${args.operation}(${args.a}, ${args.b}) = ${result}`, - }, - ], - }; - }, -); - -async function main() { - console.log("Running Claude Agent SDK example..."); - - const prompt = "What is 15 multiplied by 7? Use the calculator tool."; - console.log(`\nPrompt: ${prompt}\n`); - - // This query will be automatically traced to Braintrust - for await (const message of query({ - prompt, - options: { - model: "claude-sonnet-4-5", - tools: [calculator], - permissionMode: "bypassPermissions", - }, - })) { - if (message.type === "assistant") { - const content = message.message?.content; - if (Array.isArray(content)) { - for (const block of content) { - if (block.type === "text") { - console.log(`Claude: ${block.text}`); - } else if (block.type === "tool_use") { - console.log(`\n[Tool Call: ${block.name}]`); - console.log(`Input: ${JSON.stringify(block.input, null, 2)}`); - } - } - } - } else if (message.type === "result") { - console.log("\n--- Result ---"); - console.log(`Turns: ${message.num_turns}`); - console.log(`Input tokens: ${message.usage?.input_tokens}`); - console.log(`Output tokens: ${message.usage?.output_tokens}`); - } - } - - console.log("\n✅ Agent query automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/auto-instrumentation/google-genai-example.js b/js/examples/auto-instrumentation/google-genai-example.js deleted file mode 100644 index 7aa7aaa63..000000000 --- a/js/examples/auto-instrumentation/google-genai-example.js +++ /dev/null @@ -1,44 +0,0 @@ -/** - * Google GenAI Auto-Instrumentation Example - * - * This example demonstrates using Google GenAI SDK with Braintrust auto-instrumentation. - * Gemini API calls will be automatically traced. - * - * Run with: npm run google - * Or: node --import @braintrust/auto-instrumentations/hook.mjs google-genai-example.js - */ - -import { initLogger } from "braintrust"; -import { GoogleGenAI } from "@google/genai"; - -// Check for required API keys -if (!process.env.GOOGLE_GENAI_API_KEY) { - console.error("Error: GOOGLE_GENAI_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - console.error("Get your API key from: https://aistudio.google.com/apikey"); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Create Google GenAI client -const ai = new GoogleGenAI({ apiKey: process.env.GOOGLE_GENAI_API_KEY }); - -async function main() { - console.log("Making Google Gemini request..."); - - // This call will be automatically traced to Braintrust - const response = await ai.models.generateContent({ - model: "gemini-2.5-flash-lite", - contents: "Explain what Gemini models are in one sentence.", - }); - - console.log("\nResponse:", response.text); - console.log("\n✅ Request automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/auto-instrumentation/openai-example.js b/js/examples/auto-instrumentation/openai-example.js deleted file mode 100644 index feeac71ff..000000000 --- a/js/examples/auto-instrumentation/openai-example.js +++ /dev/null @@ -1,56 +0,0 @@ -/** - * OpenAI Auto-Instrumentation Example - * - * This example demonstrates using OpenAI SDK with Braintrust auto-instrumentation. - * No manual wrapping is needed - just initialize the logger and use OpenAI normally. - * - * Run with: npm run openai - * Or: node --import @braintrust/auto-instrumentations/hook.mjs openai-example.js - */ - -import { initLogger } from "braintrust"; -import OpenAI from "openai"; - -// Check for required API keys -if (!process.env.OPENAI_API_KEY) { - console.error("Error: OPENAI_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Create OpenAI client normally - no wrapping needed! -const openai = new OpenAI({ - apiKey: process.env.OPENAI_API_KEY, -}); - -async function main() { - console.log("Making OpenAI chat completion request..."); - - // This call will be automatically traced to Braintrust - const completion = await openai.chat.completions.create({ - model: "gpt-4o-mini", - messages: [ - { - role: "system", - content: "You are a helpful assistant that provides concise answers.", - }, - { - role: "user", - content: "What are the three primary colors?", - }, - ], - temperature: 0.7, - }); - - console.log("\nResponse:", completion.choices[0].message.content); - console.log("\nTokens used:", completion.usage?.total_tokens ?? "unknown"); - console.log("\n✅ Request automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/auto-instrumentation/openai-streaming-example.js b/js/examples/auto-instrumentation/openai-streaming-example.js deleted file mode 100644 index 826df8236..000000000 --- a/js/examples/auto-instrumentation/openai-streaming-example.js +++ /dev/null @@ -1,56 +0,0 @@ -/** - * OpenAI Streaming Auto-Instrumentation Example - * - * This example demonstrates streaming responses from OpenAI with auto-instrumentation. - * - * Run with: npm run openai-streaming - * Or: node --import @braintrust/auto-instrumentations/hook.mjs openai-streaming-example.js - */ - -import { initLogger } from "braintrust"; -import OpenAI from "openai"; - -// Check for required API keys -if (!process.env.OPENAI_API_KEY) { - console.error("Error: OPENAI_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Create OpenAI client normally - no wrapping needed! -const openai = new OpenAI({ - apiKey: process.env.OPENAI_API_KEY, -}); - -async function main() { - console.log("Making streaming OpenAI chat completion request...\n"); - - // Streaming call will be automatically traced - const stream = await openai.chat.completions.create({ - model: "gpt-4o-mini", - messages: [ - { - role: "user", - content: "Write a haiku about TypeScript.", - }, - ], - stream: true, - }); - - let fullText = ""; - for await (const chunk of stream) { - const content = chunk.choices[0]?.delta?.content || ""; - process.stdout.write(content); - fullText += content; - } - - console.log("\n\n✅ Streaming request automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/auto-instrumentation/package.json b/js/examples/auto-instrumentation/package.json deleted file mode 100644 index cae72c4af..000000000 --- a/js/examples/auto-instrumentation/package.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "name": "braintrust-auto-instrumentation-examples", - "version": "1.0.0", - "private": true, - "description": "Examples demonstrating Braintrust auto-instrumentation with various AI SDKs", - "type": "module", - "scripts": { - "openai": "node --import @braintrust/auto-instrumentations/hook.mjs openai-example.js", - "openai-streaming": "node --import @braintrust/auto-instrumentations/hook.mjs openai-streaming-example.js", - "anthropic": "node --import @braintrust/auto-instrumentations/hook.mjs anthropic-example.js", - "vercel": "node --import @braintrust/auto-instrumentations/hook.mjs vercel-ai-sdk-example.js", - "google": "node --import @braintrust/auto-instrumentations/hook.mjs google-genai-example.js", - "claude-agent": "node --import @braintrust/auto-instrumentations/hook.mjs claude-agent-sdk-example.js" - }, - "dependencies": { - "@ai-sdk/openai": "^3.0.0", - "@anthropic-ai/claude-agent-sdk": "^0.1.0", - "@anthropic-ai/sdk": "^0.60.0", - "@braintrust/auto-instrumentations": "file:../../../integrations/auto-instrumentations", - "@google/genai": "^1.25.0", - "ai": "^6.0.0", - "braintrust": "file:../..", - "openai": "^4.0.0", - "zod": "^3.25.34" - } -} diff --git a/js/examples/auto-instrumentation/vercel-ai-sdk-example.js b/js/examples/auto-instrumentation/vercel-ai-sdk-example.js deleted file mode 100644 index 81a0cb7ae..000000000 --- a/js/examples/auto-instrumentation/vercel-ai-sdk-example.js +++ /dev/null @@ -1,45 +0,0 @@ -/** - * Vercel AI SDK Auto-Instrumentation Example - * - * This example demonstrates using Vercel AI SDK with Braintrust auto-instrumentation. - * The generateText function will be automatically traced. - * - * Run with: npm run vercel - * Or: node --import @braintrust/auto-instrumentations/hook.mjs vercel-ai-sdk-example.js - */ - -import { initLogger } from "braintrust"; -import { generateText } from "ai"; -import { openai } from "@ai-sdk/openai"; - -// Check for required API keys -if (!process.env.OPENAI_API_KEY) { - console.error("Error: OPENAI_API_KEY environment variable is not set."); - console.error("Please create a .env file with your API key."); - process.exit(1); -} - -// Initialize Braintrust logging -initLogger({ - projectName: "auto-instrumentation-examples", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -async function main() { - console.log("Generating text with Vercel AI SDK..."); - - // This call will be automatically traced to Braintrust - const result = await generateText({ - model: openai("gpt-4o-mini"), - prompt: "What are the benefits of using TypeScript?", - }); - - console.log("\nResponse:", result.text); - console.log("\nUsage:"); - console.log(" Prompt tokens:", result.usage.promptTokens); - console.log(" Completion tokens:", result.usage.completionTokens); - console.log(" Total tokens:", result.usage.totalTokens); - console.log("\n✅ Request automatically traced to Braintrust!"); -} - -main().catch(console.error); diff --git a/js/examples/claude-agent-sdk/Makefile b/js/examples/claude-agent-sdk/Makefile deleted file mode 100644 index b3f8112c6..000000000 --- a/js/examples/claude-agent-sdk/Makefile +++ /dev/null @@ -1,10 +0,0 @@ -.PHONY: run run-async-iter clean - -run: - cd ../.. && pnpm build && ./node_modules/.bin/tsx examples/claude-agent-sdk/index.ts - -run-async-iter: - cd ../.. && pnpm build && ./node_modules/.bin/tsx examples/claude-agent-sdk/async_iter.ts - -clean: - rm -rf node_modules diff --git a/js/examples/claude-agent-sdk/async_iter.ts b/js/examples/claude-agent-sdk/async_iter.ts deleted file mode 100644 index 207e21b25..000000000 --- a/js/examples/claude-agent-sdk/async_iter.ts +++ /dev/null @@ -1,136 +0,0 @@ -/** - * Claude Agent SDK Example - Async Iterable Prompts - * - * Demonstrates using an async iterable as the prompt to seed a conversation - * with multiple user turns. This pattern is useful for: - * - Multi-turn conversations where you want to inject user context - * - Resuming from a saved list of user prompts - * - Programmatically building user message history - * - * Note: The SDK prompt AsyncIterable must contain `type: "user"` messages. - * - * Run: make run-async-iter - */ - -import * as claudeSDK from "@anthropic-ai/claude-agent-sdk"; -import { wrapClaudeAgentSDK, initLogger, traced } from "braintrust"; - -// Initialize Braintrust logger -initLogger({ - projectName: "Claude Agent SDK Example", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Wrap the Claude Agent SDK for automatic tracing -const { query } = wrapClaudeAgentSDK(claudeSDK); - -// Type for SDK messages -type SDKMessage = { - type: string; - message?: { - role: string; - content: Array<{ type: string; text?: string }>; - }; - [key: string]: unknown; -}; - -/** - * Creates an async iterable from an array of user messages. - * This simulates loading a previous conversation from storage. - */ -async function* messagesFromHistory( - messages: SDKMessage[], -): AsyncIterable { - for (const msg of messages) { - yield msg; - } -} - -async function main() { - console.log( - "Starting Claude Agent SDK example with async iterable prompt...\n", - ); - console.log("This example demonstrates:"); - console.log(" - Using an async iterable as the prompt"); - console.log(" - Resuming a conversation with injected history"); - console.log(" - Multi-turn interactions with context\n"); - - // Simulate previous user turns we want to continue from. - // NOTE: Claude Agent SDK expects user messages only in the prompt iterable. - const previousConversation: SDKMessage[] = [ - { - type: "user", - message: { - role: "user", - content: [ - { - type: "text", - text: "I'm working on a project called 'WeatherApp'. Remember this name.", - }, - ], - }, - }, - { - type: "user", - message: { - role: "user", - content: [ - { - type: "text", - text: "What was the project name I mentioned?", - }, - ], - }, - }, - ]; - - console.log("--- Previous user messages (injected via async iterable) ---"); - for (const msg of previousConversation) { - if (msg.message?.content?.[0]?.text) { - const role = msg.message.role.toUpperCase(); - console.log(`${role}: ${msg.message.content[0].text}`); - } - } - console.log("--- End of previous conversation ---\n"); - - console.log( - "Now querying with this history as an async iterable prompt...\n", - ); - - await traced( - async (span) => { - // Create an async iterable from the conversation history - const conversationPrompt = messagesFromHistory(previousConversation); - - for await (const message of query({ - prompt: conversationPrompt, - options: { - model: "claude-haiku-4-5", - permissionMode: "bypassPermissions", - maxTurns: 1, - }, - })) { - if (message.type === "assistant") { - const content = message.message?.content; - if (Array.isArray(content)) { - for (const block of content) { - if (block.type === "text") { - console.log(`Claude: ${block.text}`); - } - } - } - } else if (message.type === "result") { - console.log("\n--- Result ---"); - console.log(`Turns: ${message.num_turns}`); - console.log(`Input tokens: ${message.usage?.input_tokens}`); - console.log(`Output tokens: ${message.usage?.output_tokens}`); - } - } - - console.log(`\nView trace: ${await span.link()}`); - }, - { name: "Claude Agent SDK - Async Iterable Example" }, - ); -} - -main().catch(console.error); diff --git a/js/examples/claude-agent-sdk/index.ts b/js/examples/claude-agent-sdk/index.ts deleted file mode 100644 index fcc807e96..000000000 --- a/js/examples/claude-agent-sdk/index.ts +++ /dev/null @@ -1,164 +0,0 @@ -/** - * Claude Agent SDK Example with Braintrust Tracing - * - * Demonstrates: - * 1. Wrapping the Claude Agent SDK for automatic tracing - * 2. SDK MCP server (math) with calculator tool - local in-process - * 3. Remote MCP server (braintrust) - stdio-based MCP server - * 4. Subagent spawning via Task tool (with nested trace spans) - * 5. All tool calls traced via PreToolUse/PostToolUse hooks - * - * Run: make run - */ - -import * as claudeSDK from "@anthropic-ai/claude-agent-sdk"; -import { z } from "zod"; -import { wrapClaudeAgentSDK, initLogger, traced } from "braintrust"; - -// Initialize Braintrust logger -initLogger({ - projectName: "Claude Agent SDK Example", - apiKey: process.env.BRAINTRUST_API_KEY, -}); - -// Wrap the Claude Agent SDK for automatic tracing -const { query, tool, createSdkMcpServer } = wrapClaudeAgentSDK(claudeSDK); - -// Create a calculator tool (SDK MCP - local) -const calculator = tool( - "calculator", - "Performs basic arithmetic operations (add, subtract, multiply, divide)", - { - operation: z.enum(["add", "subtract", "multiply", "divide"]), - a: z.number(), - b: z.number(), - }, - async (args: { operation: string; a: number; b: number }) => { - let result: number; - switch (args.operation) { - case "add": - result = args.a + args.b; - break; - case "subtract": - result = args.a - args.b; - break; - case "multiply": - result = args.a * args.b; - break; - case "divide": - result = args.a / args.b; - break; - default: - return { - content: [ - { type: "text", text: `Unknown operation: ${args.operation}` }, - ], - isError: true, - }; - } - return { - content: [ - { - type: "text", - text: `${args.operation}(${args.a}, ${args.b}) = ${result}`, - }, - ], - }; - }, -); - -async function main() { - console.log("Starting Claude Agent SDK example with Braintrust tracing...\n"); - console.log("This example demonstrates:"); - console.log( - " - SDK MCP: math server with calculator tool (local in-process)", - ); - console.log(" - Remote MCP: braintrust (stdio server via npx)"); - console.log( - " - Subagents: math-expert (via Task tool, with nested trace spans)\n", - ); - - // SDK MCP server (local, in-process) - const mathServer = createSdkMcpServer({ - name: "math", - version: "1.0.0", - tools: [calculator], - }); - - // Check for Braintrust API key for the remote MCP server - const braintrustApiKey = process.env.BRAINTRUST_API_KEY; - if (!braintrustApiKey) { - console.warn( - "Warning: BRAINTRUST_API_KEY not set. Braintrust MCP server may not work.\n", - ); - } - - const prompt = `Do three things: -1. Use the calculator to multiply 25 by 4 -2. Spawn a math-expert subagent to calculate the factorial of 5 -3. List my braintrust projects - -Report all results.`; - - console.log(`Prompt: ${prompt}\n`); - - await traced( - async (span) => { - for await (const message of query({ - prompt, - options: { - model: "claude-sonnet-4-20250514", - permissionMode: "bypassPermissions", - // Enable Task tool for subagent spawning - allowedTools: ["Task"], - // Define custom subagents (traced as nested TASK spans) - agents: { - "math-expert": { - description: "Math specialist for calculations and explanations", - prompt: - "You are a math expert. Perform calculations step by step.", - model: "claude-haiku-4-5", - }, - }, - mcpServers: { - // SDK MCP (local) - math: mathServer, - // Remote MCP (stdio) - Braintrust MCP server - braintrust: { - type: "stdio", - command: "npx", - args: ["-y", "@braintrust/mcp-server@latest"], - env: { - BRAINTRUST_API_KEY: braintrustApiKey || "", - }, - }, - }, - }, - })) { - if (message.type === "assistant") { - const content = message.message?.content; - if (Array.isArray(content)) { - for (const block of content) { - if (block.type === "text") { - console.log(`Claude: ${block.text}`); - } else if (block.type === "tool_use") { - console.log(`\n[Tool Call: ${block.name}]`); - console.log(`Input: ${JSON.stringify(block.input, null, 2)}`); - } - } - } - } else if (message.type === "result") { - console.log("\n--- Result ---"); - console.log(`Turns: ${message.num_turns}`); - console.log(`Input tokens: ${message.usage?.input_tokens}`); - console.log(`Output tokens: ${message.usage?.output_tokens}`); - } - } - - console.log(`\nView trace: ${await span.link()}`); - }, - { name: "Claude Agent SDK Example" }, - ); -} - -main().catch(console.error); diff --git a/js/examples/dev-server.ts b/js/examples/dev-server.ts deleted file mode 100644 index d6a25d84e..000000000 --- a/js/examples/dev-server.ts +++ /dev/null @@ -1,42 +0,0 @@ -import { EvaluatorDef, login } from "braintrust"; -import { z } from "zod/v3"; -import { runDevServer } from "../dev/server"; - -// eslint-disable-next-line @typescript-eslint/no-explicit-any -const e: EvaluatorDef = { - // projectName: "braintrust-sdk-test", - data: [ - { input: "What is 2 + 2?", expected: "4" }, - { input: "What is the capital of France?", expected: "Paris" }, - { input: "What color is the sky?", expected: "Blue" }, - ], - task: async (input) => { - // Simple hardcoded responses based on input - const question = - typeof input === "string" ? input : input?.input || String(input); - if (question.includes("2 + 2")) return "4"; - if (question.includes("capital") && question.includes("France")) - return "Paris"; - if (question.includes("color") && question.includes("sky")) return "Blue"; - return "I don't know the answer to that question."; - }, - scores: [], - parameters: { - simple_param: z - .string() - .default("default value") - .describe("A simple parameter for testing"), - }, - evalName: "parameters-server", -}; - -async function main() { - await login(); - - runDevServer([e], { - host: "0.0.0.0", - port: 8011, - }); -} - -main(); diff --git a/js/examples/evals/example.eval.ts b/js/examples/evals/example.eval.ts deleted file mode 100644 index 506d0700c..000000000 --- a/js/examples/evals/example.eval.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { Eval } from "braintrust"; -import { Levenshtein } from "autoevals"; - -const NUM_EXAMPLES = 10; - -Eval("queue-test", { - data: () => { - const data: { input: string; expected: string }[] = []; - for (let i = 0; i < NUM_EXAMPLES; i++) { - const names = [ - "Foo", - "Bar", - "Alice", - "Bob", - "Charlie", - "Diana", - "Eve", - "Frank", - ]; - const greetings = ["Hi", "Hello", "Hey", "Greetings"]; - - const name = names[i % names.length]; - const greeting = greetings[i % greetings.length]; - - data.push({ - input: name, - expected: `${greeting} ${name}`, - }); - } - return data; - }, - task: async (input) => { - return "Hi " + input; // Replace with your LLM call - }, - scores: [Levenshtein], -}); diff --git a/js/examples/node-test/node-test-example.test.ts b/js/examples/node-test/node-test-example.test.ts deleted file mode 100644 index 9b64ba2ac..000000000 --- a/js/examples/node-test/node-test-example.test.ts +++ /dev/null @@ -1,353 +0,0 @@ -/** - * Node.js Test Runner + Braintrust Example - * - * Demonstrates using initNodeTestSuite to track test results as - * Braintrust experiments using the native Node.js test runner. - */ - -import { test, describe, after } from "node:test"; -import { configureNode } from "../../src/node"; -import { initNodeTestSuite } from "../../src/wrappers/node-test/index"; -import { _exportsForTestingOnly, login, currentSpan } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -_exportsForTestingOnly.setInitialTestState(); -await login({ apiKey: process.env.BRAINTRUST_API_KEY }); - -if (!process.env.OPENAI_API_KEY) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run examples/node-test/node-test-example.test.ts", - ); -} - -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -// ============================================================ -// Basic Usage — scorers, data expansion, logging -// ============================================================ - -describe("Translation Evaluation", () => { - const suite = initNodeTestSuite({ - projectName: "example-node-test", - after, - }); - - // --- Single test with input/expected and a scorer --- - - test( - "basic translation test", - suite.eval( - { - input: { text: "Hello", targetLang: "Spanish" }, - expected: "Hola", - metadata: { difficulty: "easy" }, - tags: ["translation", "spanish"], - scorers: [ - ({ output, expected }) => ({ - name: "exact_match", - score: - String(output).toLowerCase().trim() === - String(expected).toLowerCase().trim() - ? 1 - : 0, - }), - ], - }, - async ({ input }) => { - const { text, targetLang } = input as { - text: string; - targetLang: string; - }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${text}" to ${targetLang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - return response.choices[0]?.message?.content?.trim() || ""; - }, - ), - ); - - // --- Data expansion with a loop --- - - const translationCases = [ - { - input: { text: "Good morning", targetLang: "Spanish" }, - expected: "Buenos días", - }, - { - input: { text: "Thank you very much", targetLang: "Spanish" }, - expected: "Muchas gracias", - }, - { - input: { text: "Goodbye", targetLang: "French" }, - expected: "Au revoir", - }, - ]; - - for (const [i, record] of translationCases.entries()) { - test( - `translation [${i}]: "${record.input.text}" → ${record.input.targetLang}`, - suite.eval( - { - ...record, - scorers: [ - ({ output, expected }) => { - const outputStr = String(output).toLowerCase().trim(); - const expectedStr = String(expected).toLowerCase().trim(); - const outputWords = new Set(outputStr.split(" ")); - const expectedWords = expectedStr.split(" "); - const matches = expectedWords.filter((w) => - outputWords.has(w), - ).length; - return { - name: "word_overlap", - score: matches / expectedWords.length, - metadata: { matches, total: expectedWords.length }, - }; - }, - ], - }, - async ({ input }) => { - const { text, targetLang } = input as { - text: string; - targetLang: string; - }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${text}" to ${targetLang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - return response.choices[0]?.message?.content?.trim() || ""; - }, - ), - ); - } - - // --- logOutputs and logFeedback --- - - test( - "translation with extra logging", - suite.eval( - { - input: { text: "How are you?", targetLang: "Spanish" }, - expected: "¿Cómo estás?", - }, - async ({ input }) => { - const { text, targetLang } = input as { - text: string; - targetLang: string; - }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${text}" to ${targetLang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const result = response.choices[0]?.message?.content?.trim() || ""; - - currentSpan().log({ - output: { tokens: response.usage, model: response.model }, - scores: { human_quality: 0.95 }, - metadata: { evaluator: "example" }, - }); - - return result; - }, - ), - ); -}); - -// ============================================================ -// Nested Describes — share the same experiment -// ============================================================ - -describe("LLM Workflow", () => { - const suite = initNodeTestSuite({ - projectName: "example-node-test", - after, - }); - - test( - "sentiment analysis", - suite.eval( - { - input: { text: "This product is amazing and I love it!" }, - expected: "positive", - metadata: { task: "sentiment" }, - scorers: [ - ({ output, expected }) => ({ - name: "sentiment_accuracy", - score: String(output).toLowerCase().includes(String(expected)) - ? 1 - : 0, - }), - ], - }, - async ({ input }) => { - const { text } = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Classify the sentiment as "positive", "negative", or "neutral": "${text}"`, - }, - ], - temperature: 0, - }); - return response.choices[0]?.message?.content?.trim() || ""; - }, - ), - ); - - describe("Summarization Tasks", () => { - test( - "create brief summary", - suite.eval( - { - input: { - text: "Artificial intelligence and machine learning are transforming industries worldwide. Companies are using AI to improve customer service, automate processes, and gain insights from data.", - }, - metadata: { task: "summarization", type: "brief" }, - }, - async ({ input }) => { - const { text } = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Summarize in one sentence: "${text}"`, - }, - ], - temperature: 0, - }); - - const summary = response.choices[0]?.message?.content?.trim() || ""; - - currentSpan().log({ - output: { reduction: `${text.length} → ${summary.length} chars` }, - scores: { conciseness: 0.9 }, - }); - - return summary; - }, - ), - ); - - test( - "extract key insights", - suite.eval( - { - input: { - text: "Recent studies show that remote work increases productivity by 13% on average. Employees report better work-life balance and reduced commute stress. However, companies face challenges with communication and team cohesion.", - }, - metadata: { task: "summarization", type: "insights" }, - }, - async ({ input }) => { - const { text } = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `List 3 key insights from: "${text}"`, - }, - ], - temperature: 0, - }); - return response.choices[0]?.message?.content?.trim() || ""; - }, - ), - ); - }); -}); - -// ============================================================ -// Multiple Scorers -// ============================================================ - -describe("Multiple Scorers", () => { - const suite = initNodeTestSuite({ - projectName: "example-node-test", - after, - }); - - test( - "translation with multiple custom scorers", - suite.eval( - { - input: { text: "Hello world", targetLang: "Spanish" }, - expected: "Hola mundo", - scorers: [ - ({ output, expected }) => ({ - name: "exact_match", - score: - String(output).toLowerCase().trim() === - String(expected).toLowerCase().trim() - ? 1 - : 0, - }), - ({ output, expected }) => { - const outputWords = new Set( - String(output).toLowerCase().trim().split(" "), - ); - const expectedWords = String(expected) - .toLowerCase() - .trim() - .split(" "); - const matches = expectedWords.filter((w) => - outputWords.has(w), - ).length; - return { - name: "word_overlap", - score: matches / expectedWords.length, - metadata: { matches, total: expectedWords.length }, - }; - }, - ({ output }) => ({ - name: "conciseness", - score: String(output).length < 20 ? 1 : 0.7, - metadata: { length: String(output).length }, - }), - ], - }, - async ({ input }) => { - const { text, targetLang } = input as { - text: string; - targetLang: string; - }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${text}" to ${targetLang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - return response.choices[0]?.message?.content?.trim() || ""; - }, - ), - ); -}); diff --git a/js/examples/node-test/parallel-file-1.test.ts b/js/examples/node-test/parallel-file-1.test.ts deleted file mode 100644 index 369baa9a2..000000000 --- a/js/examples/node-test/parallel-file-1.test.ts +++ /dev/null @@ -1,127 +0,0 @@ -/** - * Parallel File 1: Math + LLM Operations - * - * Each file has its own suite/experiment. Node's test runner handles - * file-level parallelism automatically. - */ - -import { test, describe, after } from "node:test"; -import { configureNode } from "../../src/node"; -import { initNodeTestSuite } from "../../src/wrappers/node-test/index"; -import { _exportsForTestingOnly, login, currentSpan } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -_exportsForTestingOnly.setInitialTestState(); -await login({ apiKey: process.env.BRAINTRUST_API_KEY }); - -if (!process.env.OPENAI_API_KEY) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run examples/node-test/parallel-file-1.test.ts", - ); -} - -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -describe("File 1: Math Operations Suite", () => { - const suite = initNodeTestSuite({ - projectName: "parallel-file-1-node-test", - after, - }); - - // Compute tasks - test( - "compute: math operations", - suite.eval( - { - input: { base: 2, exponent: 10 }, - expected: 1024, - metadata: { operation: "power" }, - scorers: [ - ({ output, expected }) => ({ - name: "correctness", - score: output === expected ? 1 : 0, - }), - ], - }, - async ({ input }) => { - const { base, exponent } = input as { - base: number; - exponent: number; - }; - return Math.pow(base, exponent); - }, - ), - ); - - test( - "compute: string processing", - suite.eval( - { - input: { text: "hello world" }, - expected: "HELLO WORLD", - scorers: [ - ({ output, expected }) => ({ - name: "correctness", - score: output === expected ? 1 : 0, - }), - ], - }, - async ({ input }) => { - return (input as { text: string }).text.toUpperCase(); - }, - ), - ); - - // LLM tasks - test( - "llm: sentiment analysis", - suite.eval( - { - input: { text: "This is amazing!" }, - expected: "positive", - metadata: { task: "sentiment" }, - }, - async ({ input }) => { - const { text } = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Classify sentiment as positive/negative/neutral: "${text}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - currentSpan().log({ output: { output, tokens: response.usage } }); - return output; - }, - ), - ); - - test( - "llm: text generation", - suite.eval( - { - input: { prompt: "Count to 5" }, - expected: "1, 2, 3, 4, 5", - metadata: { task: "generation" }, - }, - async ({ input }) => { - const { prompt } = input as { prompt: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [{ role: "user", content: prompt }], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - currentSpan().log({ output: { output, tokens: response.usage } }); - return output; - }, - ), - ); -}); diff --git a/js/examples/node-test/parallel-file-2.test.ts b/js/examples/node-test/parallel-file-2.test.ts deleted file mode 100644 index 4f5f27cb5..000000000 --- a/js/examples/node-test/parallel-file-2.test.ts +++ /dev/null @@ -1,133 +0,0 @@ -/** - * Parallel File 2: String + LLM Operations - * - * This file runs in parallel with parallel-file-1.test.ts - * Each file has its own suite/experiment. - */ - -import { test, describe, after } from "node:test"; -import { configureNode } from "../../src/node"; -import { initNodeTestSuite } from "../../src/wrappers/node-test/index"; -import { _exportsForTestingOnly, login, currentSpan } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -_exportsForTestingOnly.setInitialTestState(); -await login({ apiKey: process.env.BRAINTRUST_API_KEY }); - -if (!process.env.OPENAI_API_KEY) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run examples/node-test/parallel-file-2.test.ts", - ); -} - -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -describe("File 2: String Operations Suite", () => { - const suite = initNodeTestSuite({ - projectName: "parallel-file-2-node-test", - after, - }); - - // String tasks - test( - "uppercase transformation", - suite.eval( - { - input: { text: "hello world" }, - expected: "HELLO WORLD", - scorers: [ - ({ output, expected }) => ({ - name: "correctness", - score: output === expected ? 1 : 0, - }), - ], - }, - async ({ input }) => { - return (input as { text: string }).text.toUpperCase(); - }, - ), - ); - - test( - "string replace", - suite.eval( - { - input: { text: "Hello World", find: "World", replace: "Node" }, - expected: "Hello Node", - scorers: [ - ({ output, expected }) => ({ - name: "correctness", - score: output === expected ? 1 : 0, - }), - ], - }, - async ({ input }) => { - const { text, find, replace } = input as { - text: string; - find: string; - replace: string; - }; - return text.replace(find, replace); - }, - ), - ); - - test( - "string split", - suite.eval( - { - input: { text: "a,b,c,d", delimiter: "," }, - expected: ["a", "b", "c", "d"], - scorers: [ - ({ output, expected }) => ({ - name: "correctness", - score: JSON.stringify(output) === JSON.stringify(expected) ? 1 : 0, - }), - ], - }, - async ({ input }) => { - const { text, delimiter } = input as { - text: string; - delimiter: string; - }; - return text.split(delimiter); - }, - ), - ); - - // LLM task - test( - "llm: sentiment quick", - suite.eval( - { - input: { text: "I love it" }, - expected: "positive", - metadata: { task: "sentiment" }, - }, - async ({ input }) => { - const { text } = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Is "${text}" positive or negative? Answer with one word.`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - currentSpan().log({ - output: { output }, - scores: { - correctness: output.toLowerCase().includes("positive") ? 1 : 0, - }, - }); - return output; - }, - ), - ); -}); diff --git a/js/examples/openai_completions_example.ts b/js/examples/openai_completions_example.ts deleted file mode 100755 index 10f954113..000000000 --- a/js/examples/openai_completions_example.ts +++ /dev/null @@ -1,25 +0,0 @@ -#!/usr/bin/env tsx - -import OpenAI from "openai"; -import { wrapOpenAI, initLogger } from "braintrust"; - -// Create and wrap OpenAI client with Braintrust -const openai = wrapOpenAI(new OpenAI()); - -initLogger({ projectName: "typescript-examples" }); - -async function main() { - const result = await openai.chat.completions.create({ - messages: [ - { - role: "user", - content: "Hello! Can you tell me a joke?", - }, - ], - model: "gpt-3.5-turbo", - max_tokens: 100, - }); - console.dir(result); -} - -main().catch(console.error); diff --git a/js/examples/openai_responses_example.ts b/js/examples/openai_responses_example.ts deleted file mode 100755 index 811518e08..000000000 --- a/js/examples/openai_responses_example.ts +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env tsx - -import OpenAI from "openai"; -import { wrapOpenAI, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const client = wrapOpenAI(new OpenAI()); - -async function main() { - console.log("Call responses.create"); - const response = await client.responses.create({ - model: "gpt-4o-mini", - instructions: "It is the year 2000", - input: "What is the best book?", - }); - - console.log(response.output_text); - - console.log("Call responses.stream"); - const stream = await client.responses.create({ - model: "gpt-4o-mini", - input: "What are 5 books I should read?", - stream: true, - }); - - let count = 0; - for await (const event of stream) { - count++; - } - console.log("streamed", count, "events"); -} - -main().catch(console.error); diff --git a/js/examples/openai_responses_stream_example.ts b/js/examples/openai_responses_stream_example.ts deleted file mode 100755 index 36f16e04e..000000000 --- a/js/examples/openai_responses_stream_example.ts +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env tsx - -import OpenAI from "openai"; -import { wrapOpenAI, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -const openai = wrapOpenAI(new OpenAI()); - -async function main() { - const runner = openai.responses - .stream({ - model: "gpt-4o-2024-08-06", - input: "solve 8x + 31 = 2", - }) - .on("event", (event) => console.log(event)) - .on("response.output_text.delta", (diff) => - process.stdout.write(diff.delta), - ); - - for await (const event of runner) { - console.log("event", event); - } - - const result = await runner.finalResponse(); - console.log(result); -} - -main(); diff --git a/js/examples/run_all b/js/examples/run_all deleted file mode 100755 index 43d7600b6..000000000 --- a/js/examples/run_all +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -set -e - -d="$(dirname "$0")" - -# Loop over all .ts files in that directory -for file in "$d"/*.ts; do - echo "---------------------------------------------" - echo "Executing $file..." - tsx "$file" -done - -echo "They all passed" diff --git a/js/examples/tracing_example.ts b/js/examples/tracing_example.ts deleted file mode 100755 index b12509891..000000000 --- a/js/examples/tracing_example.ts +++ /dev/null @@ -1,23 +0,0 @@ -#!/usr/bin/env tsx - -import { traced, initLogger } from "braintrust"; - -initLogger({ projectName: "typescript-examples" }); - -async function main() { - return traced(async (span) => { - console.log("this function is traced"); - span.log({ - input: "hello", - output: "world", - metrics: { - tokens: 10, - }, - metadata: { - version: "0.1.2", - }, - }); - }); -} - -main().catch(console.error); diff --git a/js/examples/vitest/parallel-file-1.test.ts b/js/examples/vitest/parallel-file-1.test.ts deleted file mode 100644 index 0a6934d0e..000000000 --- a/js/examples/vitest/parallel-file-1.test.ts +++ /dev/null @@ -1,127 +0,0 @@ -import { test, describe, expect, afterAll, beforeAll } from "vitest"; -import { configureNode } from "../../src/node"; -import { wrapVitest } from "../../src/wrappers/vitest/index"; -import { _exportsForTestingOnly, login } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -// Initialize Braintrust state and login with real credentials from environment -beforeAll(async () => { - _exportsForTestingOnly.setInitialTestState(); - await login({ - apiKey: process.env.BRAINTRUST_API_KEY, - }); -}); - -const bt = wrapVitest( - { test, describe, expect, afterAll }, - { - projectName: "parallel-file-1", - displaySummary: true, - }, -); - -const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - -if (!process.env.OPENAI_API_KEY || !openai) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run LLM tests in examples/vitest/parallel-file-1.test.ts", - ); -} - -// ============================================================================= -// FILE 1: Math Operations -// ============================================================================= -// This file runs in parallel with other test files (file-level parallelism). -// Vitest runs each test file in a separate worker thread by default. - -bt.describe("File 1: Math Operations Suite", () => { - bt.test("addition operations", async () => { - console.log("\n[FILE 1] Running: addition operations"); - const result = 10 + 20; - - bt.logOutputs({ - file: "parallel-file-1", - operation: "addition", - result, - }); - bt.logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe(30); - }); - - bt.test("multiplication operations", async () => { - console.log("[FILE 1] Running: multiplication operations"); - const result = 5 * 6; - - bt.logOutputs({ - file: "parallel-file-1", - operation: "multiplication", - result, - }); - bt.logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe(30); - }); - - bt.test("division operations", async () => { - console.log("[FILE 1] Running: division operations"); - const result = 100 / 4; - - bt.logOutputs({ - file: "parallel-file-1", - operation: "division", - result, - }); - bt.logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe(25); - }); - - // Test-level concurrency within this file - bt.test.concurrent("concurrent: power operation", async () => { - console.log("[FILE 1] Running: concurrent power operation"); - const result = Math.pow(2, 8); - - bt.logOutputs({ - file: "parallel-file-1", - operation: "power", - result, - concurrent: true, - }); - bt.logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe(256); - }); - - bt.test.concurrent("concurrent: square root operation", async () => { - console.log("[FILE 1] Running: concurrent square root operation"); - const result = Math.sqrt(144); - - bt.logOutputs({ - file: "parallel-file-1", - operation: "sqrt", - result, - concurrent: true, - }); - bt.logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe(12); - }); - - bt.test.concurrent("llm: simple generation", async () => { - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [{ role: "user", content: "Say hi" }], - temperature: 0, - }); - const output = response.choices?.[0]?.message?.content?.trim() || ""; - bt.logOutputs({ output }); - bt.logFeedback({ name: "correctness", score: output.length ? 1.0 : 0.0 }); - expect(output.length).toBeGreaterThan(0); - }); -}); diff --git a/js/examples/vitest/parallel-file-2.test.ts b/js/examples/vitest/parallel-file-2.test.ts deleted file mode 100644 index a8986b4ab..000000000 --- a/js/examples/vitest/parallel-file-2.test.ts +++ /dev/null @@ -1,126 +0,0 @@ -import * as vitest from "vitest"; -import { configureNode } from "../../src/node"; -import { wrapVitest } from "../../src/wrappers/vitest/index"; -import { _exportsForTestingOnly, login } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -const { describe, expect, test, beforeAll, afterAll, logOutputs, logFeedback } = - wrapVitest(vitest, { - projectName: "example-vitest", - displaySummary: true, - }); - -beforeAll(async () => { - _exportsForTestingOnly.setInitialTestState(); - await login({ - apiKey: process.env.BRAINTRUST_API_KEY, - }); -}); - -const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - -if (!process.env.OPENAI_API_KEY || !openai) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run LLM tests in examples/vitest/parallel-file-2.test.ts", - ); -} - -// ============================================================================= -// FILE 2: String Operations -// ============================================================================= -// This file runs in parallel with other test files (file-level parallelism). -// Each file maintains its own isolated experiment context. - -describe("File 2: String Operations Suite", () => { - test("uppercase transformation", async () => { - console.log("\n[FILE 2] Running: uppercase transformation"); - const result = "hello world".toUpperCase(); - - logOutputs({ - file: "parallel-file-2", - operation: "uppercase", - result, - }); - logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe("HELLO WORLD"); - }); - - test("lowercase transformation", async () => { - console.log("[FILE 2] Running: lowercase transformation"); - const result = "HELLO WORLD".toLowerCase(); - - logOutputs({ - file: "parallel-file-2", - operation: "lowercase", - result, - }); - logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe("hello world"); - }); - - test("string concatenation", async () => { - console.log("[FILE 2] Running: string concatenation"); - const result = "Hello" + " " + "World"; - - logOutputs({ - file: "parallel-file-2", - operation: "concatenation", - result, - }); - logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe("Hello World"); - }); - - // Test-level concurrency within this file - test.concurrent("concurrent: string replace", async () => { - console.log("[FILE 2] Running: concurrent string replace"); - const result = "Hello World".replace("World", "Vitest"); - - logOutputs({ - file: "parallel-file-2", - operation: "replace", - result, - concurrent: true, - }); - logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toBe("Hello Vitest"); - }); - - test.concurrent("concurrent: string split", async () => { - console.log("[FILE 2] Running: concurrent string split"); - const result = "a,b,c,d".split(","); - - logOutputs({ - file: "parallel-file-2", - operation: "split", - result, - concurrent: true, - }); - logFeedback({ name: "correctness", score: 1.0 }); - - expect(result).toEqual(["a", "b", "c", "d"]); - }); - - test.concurrent("llm: sentiment quick", async () => { - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { role: "user", content: "Is 'I love it' positive or negative?" }, - ], - temperature: 0, - }); - const output = response.choices?.[0]?.message?.content?.trim() || ""; - logOutputs({ output }); - logFeedback({ name: "correctness", score: output.length ? 1.0 : 0.0 }); - expect(output.length).toBeGreaterThan(0); - }); -}); diff --git a/js/examples/vitest/vitest-example.test.ts b/js/examples/vitest/vitest-example.test.ts deleted file mode 100644 index ff5af2810..000000000 --- a/js/examples/vitest/vitest-example.test.ts +++ /dev/null @@ -1,839 +0,0 @@ -import * as vitest from "vitest"; -import { configureNode } from "../../src/node"; -import { wrapVitest } from "../../src/wrappers/vitest/index"; -import { _exportsForTestingOnly, login, initDataset } from "../../src/logger"; -import { wrapOpenAI } from "../../src/wrappers/oai"; -import OpenAI from "openai"; - -configureNode(); - -// Track test progress -let totalTests = 0; -let completedTests = 0; - -// Create wrapped vitest with enhanced progress reporting -const bt = wrapVitest(vitest, { - projectName: "example-vitest", - displaySummary: true, // Show experiment summary at the end - onProgress: (event) => { - // Progress reporting - switch (event.type) { - case "suite_start": - console.log(`Starting suite: ${event.suiteName}`); - break; - case "test_start": - totalTests++; - console.log(`Running: ${event.testName}`); - break; - case "test_complete": - completedTests++; - const status = event.passed ? "✅ PASS" : "❌ FAIL"; - const progress = `[${completedTests}/${totalTests}]`; - console.log( - ` ${status} ${progress} ${event.testName} (${event.duration.toFixed(2)}ms)`, - ); - break; - case "suite_complete": - console.log(`\nSuite complete: ${event.suiteName}`); - console.log(`Passed: ${event.passed} | Failed: ${event.failed}`); - break; - } - }, -}); - -const { describe, expect, test, afterAll, beforeAll, logOutputs, logFeedback } = - bt; - -beforeAll(async () => { - _exportsForTestingOnly.setInitialTestState(); - await login({ - apiKey: process.env.BRAINTRUST_API_KEY, - }); -}); - -describe("Concurrent Execution: Mixed Workloads", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!process.env.OPENAI_API_KEY || !openai) { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run LLM tests in examples/vitest/vitest-example.test.ts", - ); - } - - // compute tasks - test.concurrent("compute: math operations", async () => { - const result = Math.pow(2, 10); - logOutputs({ result, operation: "power" }); - logFeedback({ name: "correctness", score: 1.0 }); - expect(result).toBe(1024); - }); - - test.concurrent("compute: string processing", async () => { - const result = "hello world".toUpperCase(); - logOutputs({ result, operation: "uppercase" }); - logFeedback({ name: "correctness", score: 1.0 }); - expect(result).toBe("HELLO WORLD"); - }); - - // LLM tasks running concurrently - test.concurrent( - "llm: sentiment analysis", - { - input: { text: "This is amazing!" }, - expected: "positive", - metadata: { task: "sentiment" }, - }, - async ({ input }) => { - const typedInput = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Classify sentiment as positive/negative/neutral: "${typedInput.text}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - logOutputs({ output, tokens: response.usage }); - logFeedback({ name: "correctness", score: 1.0 }); - expect(output.length).toBeGreaterThan(0); - }, - ); - - test.concurrent( - "llm: text generation", - { - input: { prompt: "Count to 5" }, - expected: "1, 2, 3, 4, 5", - metadata: { task: "generation" }, - }, - async ({ input }) => { - const typedInput = input as { prompt: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [{ role: "user", content: typedInput.prompt }], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - logOutputs({ output, tokens: response.usage }); - logFeedback({ name: "correctness", score: 1.0 }); - expect(output.length).toBeGreaterThan(0); - }, - ); - - // compute + LLM running concurrently - test.concurrent("mixed: compute task", async () => { - await new Promise((resolve) => setTimeout(resolve, 100)); - logOutputs({ type: "compute", duration: 100 }); - expect(true).toBe(true); - }); - test.concurrent("mixed: llm task", async () => { - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [{ role: "user", content: "Say hi" }], - temperature: 0, - }); - logOutputs({ - type: "llm", - output: response.choices[0]?.message?.content, - }); - expect(response.choices[0]).toBeDefined(); - }); -}); - -describe("Nested LLM Workflow", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run LLM workflow tests", - ); - }); - return; - } - - test("extract key points", async () => { - const text = - "The quick brown fox jumps over the lazy dog. This sentence contains every letter."; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Extract key points from: "${text}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - logOutputs({ - level: 1, - task: "extraction", - input: text, - output, - tokens: response.usage, - }); - logFeedback({ name: "completeness", score: 1.0 }); - - expect(output.length).toBeGreaterThan(0); - }); - - describe("Translation Tasks", () => { - test("translate to Spanish", async () => { - const text = "Hello, how are you?"; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate to Spanish: "${text}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 2, - task: "translation", - source: text, - target: "Spanish", - output, - }); - logFeedback({ name: "translation_quality", score: 0.95 }); - - expect(output.length).toBeGreaterThan(0); - }); - - test("translate to French", async () => { - const text = "Hello, how are you?"; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate to French: "${text}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 2, - task: "translation", - source: text, - target: "French", - output, - }); - logFeedback({ name: "translation_quality", score: 0.93 }); - - expect(output.length).toBeGreaterThan(0); - }); - - describe("Translation Quality Checks", () => { - test("check grammar accuracy", async () => { - const translatedText = "Hola, ¿cómo estás?"; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Rate the grammar quality (0-1) of this Spanish text: "${translatedText}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 3, - task: "validation", - check: "grammar", - text: translatedText, - assessment: output, - }); - logFeedback({ name: "grammar_score", score: 0.98 }); - - expect(output.length).toBeGreaterThan(0); - }); - - test("check naturalness", async () => { - const translatedText = "Hola, ¿cómo estás?"; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Rate how natural (0-1) this Spanish phrase sounds: "${translatedText}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 3, - task: "validation", - check: "naturalness", - text: translatedText, - assessment: output, - }); - logFeedback({ name: "naturalness_score", score: 0.95 }); - - expect(output.length).toBeGreaterThan(0); - }); - }); - - test("finalize translations", async () => { - logOutputs({ - level: 2, - task: "finalization", - status: "All translations validated and approved", - }); - logFeedback({ name: "workflow_completion", score: 1.0 }); - expect(true).toBe(true); - }); - }); - - describe("Summarization Tasks", () => { - test("create brief summary", async () => { - const longText = - "Artificial intelligence and machine learning are transforming industries worldwide. Companies are using AI to improve customer service, automate processes, and gain insights from data. The technology is advancing rapidly with new breakthroughs happening regularly."; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Summarize in one sentence: "${longText}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 2, - task: "summarization", - type: "brief", - output, - reduction: `${longText.length} → ${output.length} chars`, - }); - logFeedback({ name: "conciseness", score: 0.9 }); - - expect(output.length).toBeGreaterThan(0); - expect(output.length).toBeLessThan(longText.length); - }); - - test("extract key insights", async () => { - const longText = - "Recent studies show that remote work increases productivity by 13% on average. Employees report better work-life balance and reduced commute stress. However, companies face challenges with communication and team cohesion in remote settings."; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `List 3 key insights from: "${longText}"`, - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - - logOutputs({ - level: 2, - task: "summarization", - type: "insights", - output, - }); - logFeedback({ name: "relevance", score: 0.88 }); - - expect(output.length).toBeGreaterThan(0); - }); - }); - - test("generate workflow report", async () => { - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: - "Generate a brief report: Completed text processing workflow with translation and summarization.", - }, - ], - temperature: 0, - }); - const output = response.choices[0]?.message?.content?.trim() || ""; - logOutputs({ - level: 1, - task: "reporting", - workflow: "complete", - report: output, - }); - logFeedback({ name: "workflow_success", score: 1.0 }); - - expect(output.length).toBeGreaterThan(0); - }); -}); - -describe("Basic Test with Input/Expected", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run basic examples", - ); - }); - return; - } - - // Simplest usage: Just input and expected - test( - "basic translation test", - { - input: { text: "Hello", target_lang: "Spanish" }, - expected: "Hola", - }, - async ({ input }) => { - const typedInput = input as { text: string; target_lang: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${typedInput.text}" to ${typedInput.target_lang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const translation = response.choices[0]?.message?.content?.trim() || ""; - console.log(`\n📝 Translation: "${translation}"`); - - // Return the result for potential scorers - return translation; - }, - ); -}); - -describe("Inline Data with Auto-Expansion", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run inline data examples", - ); - }); - return; - } - - // Using inline data - automatically creates 3 separate tests - test( - "translate with inline dataset", - { - data: [ - { - input: { text: "Hello", target_lang: "Spanish" }, - expected: "Hola", - metadata: { difficulty: "easy" }, - }, - { - input: { text: "Good morning", target_lang: "Spanish" }, - expected: "Buenos días", - metadata: { difficulty: "easy" }, - }, - { - input: { text: "Thank you very much", target_lang: "Spanish" }, - expected: "Muchas gracias", - metadata: { difficulty: "medium" }, - }, - ], - scorers: [ - // Custom scorer for word overlap - ({ output, expected }) => { - const outputStr = (output as string).toLowerCase().trim(); - const expectedStr = (expected as string).toLowerCase().trim(); - const outputWords = new Set(outputStr.split(" ")); - const expectedWords = expectedStr.split(" "); - const matches = expectedWords.filter((w) => - outputWords.has(w), - ).length; - return { - name: "word_overlap", - score: matches / expectedWords.length, - metadata: { matches, total: expectedWords.length }, - }; - }, - ], - }, - async ({ input, expected }) => { - const typedInput = input as { text: string; target_lang: string }; - - console.log( - `\n📝 Translating: "${typedInput.text}" to ${typedInput.target_lang}`, - ); - - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${typedInput.text}" to ${typedInput.target_lang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const translation = response.choices[0]?.message?.content?.trim() || ""; - console.log(` Translation: "${translation}" (expected: "${expected}")`); - - // Return the translation - scorers will automatically evaluate it - return translation; - }, - ); -}); - -describe("Basic Scorer Usage", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run scorer examples", - ); - }); - return; - } - - // Single test with a simple inline scorer - test( - "translation with simple scorer", - { - input: { text: "Hello", target_lang: "Spanish" }, - expected: "Hola", - scorers: [ - ({ output, expected }) => ({ - name: "exact_match", - score: - (output as string).toLowerCase().trim() === - (expected as string).toLowerCase().trim() - ? 1 - : 0, - }), - ], - }, - async ({ input }) => { - const typedInput = input as { text: string; target_lang: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${typedInput.text}" to ${typedInput.target_lang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const translation = response.choices[0]?.message?.content?.trim() || ""; - console.log(`\n🔤 Translation: "${translation}"`); - - // Return value becomes "output" for scorers - return translation; - }, - ); -}); - -// Module-level: Create and load dataset before describe blocks -// This ensures the data is available at test registration time -const translationDataset = await (async () => { - console.log("\n📊 Creating translation dataset..."); - - // Use unique dataset name per run to avoid accumulation - const datasetName = `translation-examples-${Date.now()}`; - - const dataset = initDataset({ - project: "example-vitest", - dataset: datasetName, - description: "Example translation test cases", - }); - - // Insert sample translation pairs - const examples = [ - { - input: { text: "Hello", target_lang: "Spanish" }, - expected: "Hola", - metadata: { difficulty: "easy" }, - }, - { - input: { text: "Good morning", target_lang: "Spanish" }, - expected: "Buenos días", - metadata: { difficulty: "easy" }, - }, - { - input: { text: "Thank you very much", target_lang: "Spanish" }, - expected: "Muchas gracias", - metadata: { difficulty: "medium" }, - }, - ]; - - for (const example of examples) { - dataset.insert({ - input: example.input, - expected: example.expected, - metadata: example.metadata, - }); - } - - await dataset.flush(); - console.log(`✅ Created dataset with ${examples.length} examples`); - - // Load and return the dataset records - console.log("📥 Loading dataset from Braintrust..."); - const loaded = await initDataset({ - project: "example-vitest", - dataset: datasetName, - }).fetchedData(); - console.log(`✅ Loaded ${loaded.length} test cases from dataset`); - - return loaded; -})(); - -describe("Loading Datasets from Braintrust", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run dataset examples", - ); - }); - return; - } - - // Use the module-level loaded dataset with the data field + scorers - test( - "translate using loaded dataset", - { - data: translationDataset, - scorers: [ - // Exact match scorer - ({ output, expected }) => ({ - name: "exact_match", - score: - (output as string).toLowerCase().trim() === - (expected as string).toLowerCase().trim() - ? 1 - : 0, - }), - // Word overlap scorer - ({ output, expected }) => { - const outputStr = (output as string).toLowerCase().trim(); - const expectedStr = (expected as string).toLowerCase().trim(); - const outputWords = new Set(outputStr.split(" ")); - const expectedWords = expectedStr.split(" "); - const matches = expectedWords.filter((w) => - outputWords.has(w), - ).length; - return { - name: "word_overlap", - score: matches / expectedWords.length, - metadata: { matches, total: expectedWords.length }, - }; - }, - ], - }, - async ({ input, expected }) => { - const typedInput = input as { text: string; target_lang: string }; - - console.log( - `\n📝 Translating: "${typedInput.text}" to ${typedInput.target_lang}`, - ); - - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${typedInput.text}" to ${typedInput.target_lang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const translation = response.choices[0]?.message?.content?.trim() || ""; - console.log(` Translation: "${translation}" (expected: "${expected}")`); - - // Return the translation - scorers will automatically evaluate it - return translation; - }, - ); -}); - -describe("Combining Inline Data + Multiple Scorers", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run combined examples", - ); - }); - return; - } - - // Inline data with scorers - demonstrates auto-expansion + auto-scoring - test( - "math operations with accuracy scoring", - { - data: [ - { input: { num: 5 }, expected: 25 }, - { input: { num: 7 }, expected: 49 }, - { input: { num: 10 }, expected: 100 }, - ], - scorers: [ - ({ output, expected }) => ({ - name: "accuracy", - score: output === expected ? 1 : 0, - }), - ], - }, - async ({ input }) => { - const typedInput = input as { num: number }; - const result = typedInput.num * typedInput.num; - console.log(`\n🔢 Square of ${typedInput.num} = ${result}`); - return result; - }, - ); -}); - -describe("Complex Evaluation with Multiple Metrics", () => { - const openai = process.env.OPENAI_API_KEY - ? wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })) - : null; - - if (!openai) { - test("OPENAI_API_KEY required", () => { - throw new Error( - "OPENAI_API_KEY environment variable must be set to run scorer examples", - ); - }); - return; - } - - test( - "translation with multiple custom scorers", - { - input: { text: "Hello world", target_lang: "Spanish" }, - expected: "Hola mundo", - scorers: [ - // Custom scorer for string similarity (simple version) - ({ output, expected }) => { - const outputStr = (output as string).toLowerCase().trim(); - const expectedStr = (expected as string).toLowerCase().trim(); - // Simple word overlap score - const outputWords = new Set(outputStr.split(" ")); - const expectedWords = expectedStr.split(" "); - const matches = expectedWords.filter((w) => - outputWords.has(w), - ).length; - return { - name: "word_overlap", - score: matches / expectedWords.length, - metadata: { matches, total: expectedWords.length }, - }; - }, - // Add custom scorer for exact match - ({ output, expected }) => ({ - name: "exact_match", - score: - (output as string).toLowerCase().trim() === - (expected as string).toLowerCase() - ? 1 - : 0, - }), - ], - }, - async ({ input }) => { - const typedInput = input as { text: string; target_lang: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Translate "${typedInput.text}" to ${typedInput.target_lang}. Respond with ONLY the translation.`, - }, - ], - temperature: 0, - }); - - const translation = response.choices[0]?.message?.content?.trim() || ""; - console.log(`\n🔤 Translation: "${typedInput.text}" → "${translation}"`); - - // Return the translation - this becomes the "output" for scorers - return translation; - }, - ); - - test( - "sentiment analysis with multiple scorers", - { - input: { text: "This product is amazing and I love it!" }, - expected: "positive", - scorers: [ - // Custom scorer for sentiment accuracy - ({ output, expected }) => ({ - name: "sentiment_accuracy", - score: (output as string).toLowerCase().includes(expected as string) - ? 1 - : 0, - metadata: { output_sentiment: output }, - }), - // Custom scorer for response length - ({ output }) => ({ - name: "conciseness", - score: (output as string).length < 20 ? 1 : 0.7, - metadata: { output_length: (output as string).length }, - }), - ], - }, - async ({ input }) => { - const typedInput = input as { text: string }; - const response = await openai.chat.completions.create({ - model: "gpt-3.5-turbo", - messages: [ - { - role: "user", - content: `Classify the sentiment of this text as "positive", "negative", or "neutral": "${typedInput.text}"`, - }, - ], - temperature: 0, - }); - - const sentiment = response.choices[0]?.message?.content?.trim() || ""; - console.log(`\n😊 Sentiment: "${sentiment}"`); - - return sentiment; - }, - ); -}); diff --git a/js/examples/vitest/vitest.config.js b/js/examples/vitest/vitest.config.js deleted file mode 100644 index fc1bef90a..000000000 --- a/js/examples/vitest/vitest.config.js +++ /dev/null @@ -1,10 +0,0 @@ -import { defineConfig } from "vitest/config"; - -export default defineConfig({ - test: { - reporters: ["default"], - include: ["**/*.test.ts", "**/*.eval.ts"], - exclude: ["**/node_modules/**", "**/dist/**"], - testTimeout: 15000, - }, -});