{"version":3,"sources":["../src/cli.ts","../src/storage/index.ts","../src/core/context.ts","../src/cloud/pricingFetcher.ts","../src/config/index.ts","../src/cloud/index.ts","../src/features/hooks.ts","../src/features/recommendations.ts","../src/core/Tracker.ts","../src/dashboard/server.ts"],"sourcesContent":["#!/usr/bin/env node\nimport { Command } from 'commander';\nimport chalk from 'chalk';\nimport { tracker } from './core/Tracker.js';\nimport { createDashboardServer } from './dashboard/server.js';\nimport open from 'open';\n\nconst program = new Command();\n\nprogram\n .name('ai-cost-tracker')\n .description('CLI to track AI usage and costs')\n .version('1.0.0');\n\nprogram\n .command('dev')\n .description('Start the local development dashboard')\n .option('-p, --port ', 'Port to run the dashboard on', '3001')\n .action(async (options) => {\n const port = parseInt(options.port);\n createDashboardServer(port);\n await open(`http://localhost:${port}`);\n });\n\nprogram\n .command('report')\n .description('Print a usage report')\n .action(async () => {\n const report = await tracker.getUsageReport();\n const suggestions = await tracker.getOptimizationSuggestions();\n\n console.log(chalk.bold('\\nAI Cost Report'));\n console.log(chalk.gray('--------------\\n'));\n\n console.log(chalk.blue.bold('Model Usage'));\n for (const [model, stats] of Object.entries(report.usageByModel)) {\n console.log(`${chalk.yellow(model)}: ${chalk.green(`$${stats.cost.toFixed(4)}`)} (${stats.tokens} tokens, ${stats.count} reqs)`);\n }\n\n console.log(chalk.blue.bold('\\nProvider Usage'));\n for (const [provider, stats] of Object.entries(report.usageByProvider)) {\n console.log(`${chalk.magenta(provider)}: ${chalk.green(`$${stats.cost.toFixed(4)}`)} (${stats.tokens} tokens)`);\n }\n\n console.log(chalk.gray('\\n--------------'));\n console.log(`${chalk.bold('Total Tokens Used:')} ${chalk.cyan(report.totalTokens)}`);\n console.log(`${chalk.bold('Total Cost:')} ${chalk.green(`$${report.totalCost.toFixed(4)}`)}`);\n\n if (suggestions.length > 0) {\n console.log(chalk.magenta.bold('\\nOptimization Suggestions'));\n suggestions.forEach(s => {\n console.log(`${chalk.blue('āžœ')} ${chalk.bold(s.currentModel)} ā†’ ${chalk.bold(s.suggestedModel)}`);\n console.log(` ${chalk.gray(s.reason)}`);\n console.log(` ${chalk.green(`Potential Savings: ~${s.potentialSavings}%`)}`);\n });\n }\n console.log('');\n });\n\nprogram\n .command('upgrade')\n .description('Learn about Pro features and upgrade your account')\n .action(() => {\n console.log(chalk.bold.magenta('\\nšŸš€ AI Cost Tracker Pro'));\n console.log(chalk.gray('Take your AI monitoring to the next level.\\n'));\n \n console.log(chalk.bold('Pro Features:'));\n console.log(`${chalk.green('āœ”')} ${chalk.bold('Team-wide Analytics')}: Aggregate data across all developers.`);\n console.log(`${chalk.green('āœ”')} ${chalk.bold('Cost Alerts')}: Get notified before you break the budget.`);\n console.log(`${chalk.green('āœ”')} ${chalk.bold('Advanced Recommendations')}: Save up to 80% on token costs.`);\n console.log(`${chalk.green('āœ”')} ${chalk.bold('Webhooks & Hooks')}: Integrate tracking into your CI/CD pipelines.`);\n \n console.log(chalk.bold('\\nPlans:'));\n console.log(`- ${chalk.bold('Pro ($19/mo)')}: Perfect for individual power users.`);\n console.log(`- ${chalk.bold('Team ($79/mo)')}: Best for growing engineering teams.`);\n \n console.log(`\\nReady to upgrade? Visit: ${chalk.cyan.underline('https://ai-cost-tracker.com/pricing')}`);\n console.log(chalk.gray('Or run the local dashboard to see your current savings.\\n'));\n });\n\nprogram\n .command('init-readme')\n .description('Generate a badge for your project README')\n .action(() => {\n console.log(chalk.bold('\\nšŸ›”ļø README Badge Generator'));\n console.log(chalk.gray('Add this to your README.md to show you track AI costs responsibly.\\n'));\n \n const badge = '[![AI Cost Tracked](https://img.shields.io/badge/AI_Cost-Tracked-blue?style=flat-square&logo=openai)](https://github.com/your-repo)';\n \n console.log(chalk.bgBlack.white(` ${badge} `));\n console.log(chalk.gray('\\nCopy the markdown above into your README.md file.\\n'));\n });\n\nprogram.parse();\n","import fs from 'fs';\nimport path from 'path';\nimport { UsageMetrics } from '../types/index.js';\n\nexport interface StorageProvider {\n save(metrics: UsageMetrics): Promise;\n getAll(): Promise;\n}\n\nexport class JSONStorage implements StorageProvider {\n private filePath: string;\n\n constructor(filePath?: string) {\n this.filePath = filePath || path.join(process.cwd(), '.ai-usage.json');\n }\n\n private async ensureFile(): Promise {\n if (!fs.existsSync(this.filePath)) {\n await fs.promises.writeFile(this.filePath, JSON.stringify([], null, 2));\n }\n }\n\n async save(metrics: UsageMetrics): Promise {\n await this.ensureFile();\n const data = await this.getAll();\n data.push(metrics);\n await fs.promises.writeFile(this.filePath, JSON.stringify(data, null, 2));\n }\n\n async getAll(): Promise {\n await this.ensureFile();\n const content = await fs.promises.readFile(this.filePath, 'utf-8');\n try {\n return JSON.parse(content);\n } catch (e) {\n return [];\n }\n }\n}\n","import { AsyncLocalStorage } from 'async_hooks';\nimport { AITrackingContext } from '../types/index.js';\n\nconst contextStorage = new AsyncLocalStorage();\n\nexport function setGlobalAIContext(context: AITrackingContext) {\n // This sets a default for the current execution context\n const current = contextStorage.getStore() || {};\n contextStorage.enterWith({ ...current, ...context });\n}\n\nexport function getGlobalAIContext(): AITrackingContext {\n return contextStorage.getStore() || {};\n}\n\nexport { contextStorage };\n","import fs from 'fs';\nimport path from 'path';\nimport { OPENAI_COSTS, ANTHROPIC_COSTS, GEMINI_COSTS } from '../utils/costs.js';\n\nconst CACHE_FILE = path.join(process.cwd(), '.ai-pricing.json');\nconst CACHE_TTL_MS = 24 * 60 * 60 * 1000; // 24 hours\nconst PRICING_URL = 'https://ai-monitor-740g.onrender.com/api/v1/meta/pricing';\n\nfunction applyPricing(data: any) {\n if (data?.pricing?.openai) Object.assign(OPENAI_COSTS, data.pricing.openai);\n if (data?.pricing?.anthropic) Object.assign(ANTHROPIC_COSTS, data.pricing.anthropic);\n if (data?.pricing?.google) Object.assign(GEMINI_COSTS, data.pricing.google);\n}\n\nexport function loadFromCache(): boolean {\n try {\n if (!fs.existsSync(CACHE_FILE)) return false;\n \n const raw = fs.readFileSync(CACHE_FILE, 'utf-8');\n const data = JSON.parse(raw);\n \n // Check TTL\n const age = Date.now() - new Date(data.lastUpdated).getTime();\n if (age > CACHE_TTL_MS) return false; // Cache is stale\n \n applyPricing(data);\n return true;\n } catch {\n return false;\n }\n}\n\nexport async function fetchLatestPricing(): Promise {\n try {\n const response = await fetch(PRICING_URL, { signal: AbortSignal.timeout(5000) });\n if (!response.ok) throw new Error(`HTTP ${response.status}`);\n \n const data = await response.json();\n \n // Persist to cache\n fs.writeFileSync(CACHE_FILE, JSON.stringify(data, null, 2), 'utf-8');\n \n applyPricing(data);\n console.log('[AI Cost Tracker] Pricing updated from cloud ā˜ļø');\n } catch {\n // Silent fail ā€” hardcoded fallbacks in costs.ts remain active\n }\n}\n","import { AITrackerConfig } from '../types/index.js';\nimport { loadFromCache, fetchLatestPricing } from '../cloud/pricingFetcher.js';\n\nlet config: AITrackerConfig = {};\n\nexport function configureAITracker(newConfig: AITrackerConfig) {\n config = { ...config, ...newConfig };\n\n // 1. Load cached pricing immediately (sync, no latency)\n loadFromCache();\n\n // 2. Refresh from cloud in background (non-blocking)\n fetchLatestPricing().catch(() => {\n // Silent fail ā€” hardcoded defaults in costs.ts remain active\n });\n}\n\nexport function getConfig(): AITrackerConfig {\n return config;\n}\n\nexport function isProEnabled(): boolean {\n return !!config.apiKey;\n}\n","import { UsageMetrics } from '../types/index.js';\nimport { getConfig } from '../config/index.js';\n\nexport async function sendUsageToCloud(metrics: UsageMetrics, retries = 3): Promise {\n const config = getConfig();\n if (!config.apiKey) return;\n\n const endpoint = config.endpoint || 'https://ai-monitor-backend.onrender.com/api/v1/usage';\n\n const attemptSync = async (remaining: number): Promise => {\n try {\n const response = await fetch(endpoint, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'x-api-key': config.apiKey!,\n },\n body: JSON.stringify(metrics),\n });\n\n if (!response.ok && remaining > 0) {\n // Simple exponential backoff or just wait a bit\n await new Promise(resolve => setTimeout(resolve, 1000 * (4 - remaining)));\n return attemptSync(remaining - 1);\n }\n } catch (error) {\n if (remaining > 0) {\n await new Promise(resolve => setTimeout(resolve, 1000 * (4 - remaining)));\n return attemptSync(remaining - 1);\n }\n // Fail silently as requested\n }\n };\n\n // Start attempt (non-blocking call from Tracker usually, but here we just return the promise)\n return attemptSync(retries);\n}\n\n// Alias for backward compatibility if needed, but the user specifically asked for sendUsageToCloud\nexport const trackToCloud = sendUsageToCloud;\n","import { UsageMetrics } from '../types/index.js';\nimport { requirePro } from '../core/gating.js';\nimport EventEmitter from 'events';\n\nconst usageEmitter = new EventEmitter();\n\nexport function onUsage(callback: (data: UsageMetrics) => void) {\n requirePro('Real-Time Hooks');\n usageEmitter.on('usage', callback);\n}\n\nexport function emitUsage(metrics: UsageMetrics) {\n usageEmitter.emit('usage', metrics);\n}\n","import { UsageMetrics } from '../types/index.js';\n\nexport interface ModelMetadata {\n id: string;\n name: string;\n provider: string;\n promptCost: number; // per 1M tokens\n completionCost: number; // per 1M tokens\n capability: number; // 1-5 scale\n}\n\nconst MODELS: Record = {\n 'gpt-4': { id: 'gpt-4', name: 'GPT-4', provider: 'OpenAI', promptCost: 30, completionCost: 60, capability: 5 },\n 'gpt-4o': { id: 'gpt-4o', name: 'GPT-4o', provider: 'OpenAI', promptCost: 5, completionCost: 15, capability: 5 },\n 'gpt-4o-mini': { id: 'gpt-4o-mini', name: 'GPT-4o-mini', provider: 'OpenAI', promptCost: 0.15, completionCost: 0.60, capability: 3 },\n 'claude-3-opus': { id: 'claude-3-opus', name: 'Claude 3 Opus', provider: 'Anthropic', promptCost: 15, completionCost: 75, capability: 5 },\n 'claude-3-5-sonnet': { id: 'claude-3-5-sonnet', name: 'Claude 3.5 Sonnet', provider: 'Anthropic', promptCost: 3, completionCost: 15, capability: 5 },\n 'claude-3-haiku': { id: 'claude-3-haiku', name: 'Claude 3 Haiku', provider: 'Anthropic', promptCost: 0.25, completionCost: 1.25, capability: 2 },\n 'gemini-2.5-pro': { id: 'gemini-2.5-pro', name: 'Gemini 2.5 Pro', provider: 'Google', promptCost: 1.25, completionCost: 3.75, capability: 5 },\n 'gemini-2.5-flash': { id: 'gemini-2.5-flash', name: 'Gemini 2.5 Flash', provider: 'Google', promptCost: 0.1, completionCost: 0.4, capability: 3 },\n};\n\nexport interface Recommendation {\n currentModel: string;\n suggestedModel: string;\n reason: string;\n potentialSavings: number; // percentage\n}\n\nexport function generateRecommendations(metrics: UsageMetrics[]): Recommendation[] {\n const modelUsage = metrics.reduce((acc: any, m) => {\n acc[m.model] = (acc[m.model] || 0) + m.cost;\n return acc;\n }, {});\n\n const recommendations: Recommendation[] = [];\n\n // Logic: If using GPT-4, suggest GPT-4o\n if (modelUsage['gpt-4']) {\n recommendations.push({\n currentModel: 'gpt-4',\n suggestedModel: 'gpt-4o',\n reason: 'GPT-4o is faster and ~80% cheaper for the same capability level.',\n potentialSavings: 80\n });\n }\n\n // Logic: If using Opus, suggest Sonnet 3.5\n if (modelUsage['claude-3-opus']) {\n recommendations.push({\n currentModel: 'claude-3-opus',\n suggestedModel: 'claude-3-5-sonnet',\n reason: 'Claude 3.5 Sonnet is significantly cheaper and outperforms Opus in many benchmarks.',\n potentialSavings: 75\n });\n }\n\n // Logic: If using Gemini 2.5 Pro, suggest Gemini 2.5 Flash\n if (modelUsage['gemini-2.5-pro']) {\n recommendations.push({\n currentModel: 'gemini-2.5-pro',\n suggestedModel: 'gemini-2.5-flash',\n reason: 'Gemini 2.5 Flash is significantly cheaper and offers excellent performance for high-volume tasks.',\n potentialSavings: 90\n });\n }\n\n // Logic: If high volume but low complexity tasks (simple heuristic: low token count/req)\n // This could be expanded later with task_type metadata\n \n return recommendations;\n}\n","import { UsageMetrics, UsageReport, AITrackingContext } from '../types/index.js';\nimport { StorageProvider, JSONStorage } from '../storage/index.js';\nimport { getGlobalAIContext } from './context.js';\nimport { sendUsageToCloud } from '../cloud/index.js';\nimport { isProEnabled } from '../config/index.js';\nimport { emitUsage } from '../features/hooks.js';\nimport { generateRecommendations, Recommendation } from '../features/recommendations.js';\nimport chalk from 'chalk';\n\nexport class CostTracker {\n private storage: StorageProvider;\n\n constructor(storage?: StorageProvider) {\n this.storage = storage || new JSONStorage();\n }\n\n async recordUsage(metrics: UsageMetrics, context?: AITrackingContext): Promise {\n const globalContext = getGlobalAIContext();\n \n // Merge context: Request context overrides Global context\n const finalMetrics: UsageMetrics = {\n ...globalContext,\n ...metrics,\n ...context, // Request specific context\n timestamp: metrics.timestamp || Date.now(),\n };\n\n await this.storage.save(finalMetrics);\n\n // Sync to cloud if Pro is enabled (non-blocking)\n if (isProEnabled()) {\n emitUsage(finalMetrics);\n sendUsageToCloud(finalMetrics).catch(() => {\n // Silent failure already handled in sendUsageToCloud\n });\n } else {\n // Viral Nudges for Free users\n this.checkMilestones(finalMetrics.totalTokens);\n this.maybeLogNudge();\n }\n }\n\n private async checkMilestones(newTokens: number): Promise {\n const report = await this.getUsageReport();\n const oldTokens = report.totalTokens - newTokens;\n \n const milestone = 10000;\n if (Math.floor(report.totalTokens / milestone) > Math.floor(oldTokens / milestone)) {\n console.log(chalk.green(`\\nāœØ Milestone Reached: You've tracked over ${chalk.bold(Math.floor(report.totalTokens / milestone) * milestone)} tokens with AI Cost Tracker!`));\n console.log(chalk.gray(`View your full report with: ${chalk.cyan('npx ai-cost-tracker report')}\\n`));\n }\n }\n\n private maybeLogNudge(): void {\n // 5% chance to show a subtle nudge\n if (Math.random() < 0.05) {\n const nudges = [\n \"šŸš€ Tip: Upgrade to Pro for team-wide analytics and cost alerts.\",\n \"šŸ’” Pro users save an average of 30% using advanced model recommendations.\",\n \"šŸ“± Monitor your costs on the go with our SaaS dashboard.\",\n \"šŸ”— Run 'npx ai-cost-tracker upgrade' to see what you're missing!\",\n ];\n const nudge = nudges[Math.floor(Math.random() * nudges.length)];\n console.log(chalk.blue.dim(`\\n${nudge}\\n`));\n }\n }\n\n async getUsageReport(): Promise {\n const allMetrics = await this.storage.getAll();\n \n const report: UsageReport = {\n totalCost: 0,\n totalTokens: 0,\n usageByModel: {},\n usageByProvider: {},\n };\n\n for (const m of allMetrics) {\n report.totalCost += m.cost;\n report.totalTokens += m.totalTokens;\n\n // Model grouping\n if (!report.usageByModel[m.model]) {\n report.usageByModel[m.model] = { cost: 0, tokens: 0, count: 0 };\n }\n report.usageByModel[m.model].cost += m.cost;\n report.usageByModel[m.model].tokens += m.totalTokens;\n report.usageByModel[m.model].count += 1;\n\n // Provider grouping\n if (!report.usageByProvider[m.provider]) {\n report.usageByProvider[m.provider] = { cost: 0, tokens: 0, count: 0 };\n }\n report.usageByProvider[m.provider].cost += m.cost;\n report.usageByProvider[m.provider].tokens += m.totalTokens;\n report.usageByProvider[m.provider].count += 1;\n }\n\n // Round total cost\n report.totalCost = Number(report.totalCost.toFixed(6));\n \n // Round model costs\n for (const model in report.usageByModel) {\n report.usageByModel[model].cost = Number(report.usageByModel[model].cost.toFixed(6));\n }\n\n return report;\n }\n\n async getTotalCost(): Promise {\n const report = await this.getUsageReport();\n return report.totalCost;\n }\n\n async getUsageByUser(userId: string): Promise {\n const all = await this.storage.getAll();\n return all.filter(m => m.userId === userId);\n }\n\n async getUsageByFeature(feature: string): Promise {\n const all = await this.storage.getAll();\n return all.filter(m => m.feature === feature);\n }\n\n async getUsageByTeam(teamId: string): Promise {\n const all = await this.storage.getAll();\n return all.filter(m => m.teamId === teamId);\n }\n\n async getOptimizationSuggestions(): Promise {\n const all = await this.storage.getAll();\n return generateRecommendations(all);\n }\n}\n\n// Singleton for easy access\nexport const tracker = new CostTracker();\n","import express from 'express';\nimport cors from 'cors';\nimport { tracker } from '../core/Tracker.js';\nimport { JSONStorage } from '../storage/index.js';\nimport path from 'path';\nimport fs from 'fs';\nimport { fileURLToPath } from 'url';\n\n// Robust __dirname for both ESM and CJS\nconst getDirname = () => {\n try {\n return path.dirname(fileURLToPath(import.meta.url));\n } catch (e) {\n return __dirname;\n }\n};\n\nconst currentDir = getDirname();\n\nexport function createDashboardServer(port: number = 3001) {\n const app = express();\n \n app.use(cors());\n app.use(express.json());\n\n // Serve static files from the built UI\n const uiPath = path.resolve(currentDir, '../../dashboard-ui/dist');\n console.log(`[ai-cost-tracker] Serving dashboard from: ${uiPath}`);\n \n if (!fs.existsSync(uiPath)) {\n console.error(`[ai-cost-tracker] āŒ Dashboard UI directory not found at: ${uiPath}`);\n }\n \n app.use(express.static(uiPath));\n\n app.get('/api/usage', async (req, res) => {\n try {\n const storage = (tracker as any).storage as JSONStorage;\n const usage = await storage.getAll();\n res.json(usage);\n } catch (error) {\n res.status(500).json({ error: 'Failed to fetch usage' });\n }\n });\n\n app.get('/api/analytics', async (req, res) => {\n try {\n const report = await tracker.getUsageReport();\n res.json(report);\n } catch (error) {\n res.status(500).json({ error: 'Failed to fetch analytics' });\n }\n });\n\n app.get('/api/recommendations', async (req, res) => {\n try {\n const suggestions = await tracker.getOptimizationSuggestions();\n res.json(suggestions);\n } catch (error) {\n res.status(500).json({ error: 'Failed to fetch recommendations' });\n }\n });\n\n // Fallback to index.html for SPA\n app.get('*', (req, res) => {\n res.sendFile(path.join(uiPath, 'index.html'));\n });\n\n return app.listen(port, () => {\n console.log(`[ai-cost-tracker] Dashboard running at http://localhost:${port}`);\n 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