Vercel AI SDK Integration

import { createHelicone } from '@helicone/ai-sdk-provider';import { generateText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const { text } = await generateText({  model: helicone('gemini-2.5-flash-lite'),  prompt: 'What is Helicone?'});console.log(text);

import { createHelicone } from '@helicone/ai-sdk-provider';import { streamText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const result = await streamText({  model: helicone('deepseek-v3.1-terminus'),  prompt: 'Write a short story about a robot learning to paint',  maxTokens: 300});for await (const chunk of result.textStream) {  process.stdout.write(chunk);}console.log('\n\nStream completed!');

import { createHelicone } from '@helicone/ai-sdk-provider';import { streamText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const result = streamText({  model: helicone("gpt-4o-mini", {    extraBody: {      helicone: {        tags: ["simple-stream-test"],        properties: {          test: "toUIMessageStreamResponse",        },      },    },  }),  prompt: 'Say "Hello streaming world!"',});const response = result.toUIMessageStreamResponse();console.log(  "Response headers:",  Object.fromEntries(response.headers.entries()));// Just checks that we can create it - actual consumption needs to be in a server

import { createHelicone } from '@helicone/ai-sdk-provider';import { generateText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});// Automatic routing (cheapest provider)const autoResult = await generateText({  model: helicone('gpt-4o'),  prompt: 'Hello!'});// Manual provider selectionconst manualResult = await generateText({  model: helicone('claude-4.5-sonnet/anthropic'),  prompt: 'Hello!'});// Multiple provider selection: first model/provider is used, if it fails, the second model/provider is used, and so on.const manualResult = await generateText({  model: helicone('claude-4.5-sonnet/anthropic,gpt-4o/openai'),  prompt: 'Hello!'});

import { createHelicone } from '@helicone/ai-sdk-provider';import { generateText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const result = await generateText({  model: helicone('claude-4.5-haiku', {    extraBody: {      helicone: {        sessionId: 'my-session',        userId: 'user-123',        properties: {          environment: 'production',          appVersion: '2.1.0',          feature: 'quantum-explanation'        }      }    }  }),  prompt: 'Explain quantum computing'});

import { createHelicone } from '@helicone/ai-sdk-provider';import { generateText, tool } from 'ai';import { z } from 'zod';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const result = await generateText({  model: helicone('gpt-4o'),  prompt: 'What is the weather like in San Francisco?',  tools: {    getWeather: tool({      description: 'Get weather for a location',      parameters: z.object({        location: z.string().describe('The city name')      }),      execute: async (args) => {        return `It's sunny in ${args.location}`;      }    })  }});console.log(result.text);

import { createHelicone } from "@helicone/ai-sdk-provider";import { Experimental_Agent as Agent, tool, jsonSchema, stepCountIs } from "ai";const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY!});const weatherAgent = new Agent({  model: helicone("claude-4.5-haiku"),  stopWhen: stepCountIs(5),  tools: {    getWeather: tool({      description: "Get the current weather for a location",      inputSchema: jsonSchema({        type: "object",        properties: {          location: {            type: "string",            description: "The city and state, e.g. San Francisco, CA",          },          unit: {            type: "string",            enum: ["celsius", "fahrenheit"],            default: "fahrenheit",            description: "Temperature unit",          },        },        required: ["location"],      }),      execute: async ({ location, unit }) => {        // Simulate weather API call        const temp =          unit === "celsius"            ? Math.floor(Math.random() * 30 + 5)            : Math.floor(Math.random() * 86 + 32);        const conditions = ["sunny", "cloudy", "rainy", "partly cloudy"][          Math.floor(Math.random() * 4)        ];        const result = {          location,          temperature: temp,          unit: unit || "fahrenheit",          conditions,          description: `It's ${conditions} in ${location} with a temperature of ${temp}°${unit?.charAt(0).toUpperCase() || "F"}.`,        };        console.log(`Result: ${JSON.stringify(result)}`);        return result;      },    }),    calculateWindChill: tool({      description: "Calculate wind chill temperature",      inputSchema: jsonSchema({        type: "object",        properties: {          temperature: {            type: "number",            description: "Temperature in Fahrenheit",          },          windSpeed: {            type: "number",            description: "Wind speed in mph",          },        },        required: ["temperature", "windSpeed"],      }),      execute: async ({ temperature, windSpeed }) => {        const windChill =          35.74 +          0.6215 * temperature -          35.75 * Math.pow(windSpeed, 0.16) +          0.4275 * temperature * Math.pow(windSpeed, 0.16);        const result = {          temperature,          windSpeed,          windChill: Math.round(windChill),          description: `With a temperature of ${temperature}°F and wind speed of ${windSpeed} mph, the wind chill feels like ${Math.round(windChill)}°F.`,        };        console.log(`Result: ${JSON.stringify(result)}`);        return result;      },    }),  },});try {  console.log("🌤️  Asking about weather in multiple cities...\n");  const result = await weatherAgent.generate({    prompt:      "You are a helpful weather assistant. When asked about weather, use the getWeather tool to provide accurate information.\n\nWhat is the weather like in San Francisco, CA and New York, NY? Also, if the wind speed in San Francisco is 15 mph, what would the wind chill feel like?"  });  console.log("=== Agent Response ===");  console.log(result.text);  console.log("\n=== Usage Statistics ===");  console.log(`Total tokens: ${result.usage?.totalTokens || "N/A"}`);  console.log(`Finish reason: ${result.finishReason}`);  console.log(`Steps taken: ${result.steps?.length || 0}`);  if (result.steps && result.steps.length > 0) {    console.log("\n=== Steps Breakdown ===");    result.steps.forEach((step, index) => {      console.log(`Step ${index + 1}: ${step.finishReason}`);      if (step.toolCalls && step.toolCalls.length > 0) {        console.log(          `  Tool calls: ${step.toolCalls.map((tc) => tc.toolName).join(", ")}`        );        step.toolCalls.forEach((tc, i) => {          console.log(            `    Tool ${i + 1}: ${tc.toolName}(${JSON.stringify(tc.input)})`          );        });      }    });  }} catch (error) {  console.error("❌ Error running agent:", error);  if (error instanceof Error) {    console.error("Error details:", error.message);  }}

import { createHelicone } from '@helicone/ai-sdk-provider';import type { WithHeliconePrompt } from '@helicone/ai-sdk-provider';import { generateText } from 'ai';const helicone = createHelicone({  apiKey: process.env.HELICONE_API_KEY});const result = await generateText({  model: helicone('gpt-4o', {    promptId: 'sg45wqc',    inputs: {      customer_name: 'Sarah Johnson',      issue_type: 'billing',      account_type: 'premium'    },    environment: 'production',    extraBody: {      helicone: {        sessionId: 'support-session-123',        properties: {          department: 'customer-support'        }      }    }  }),  messages: [{ role: 'user', content: 'placeholder' }]} as WithHeliconePrompt);