updates

8 files changed, 2867 insertions, 0 deletions

diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-advanced-features-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-advanced-features-setup.md
new file mode 100644
index 0000000..9a7a7b2
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-advanced-features-setup.md
@@ -0,0 +1,569 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Set up advanced AI SDK features including reasoning models, computer use, and cutting-edge capabilities
+argument-hint: "[reasoning|computer-use|generative-ui|edge-optimization|experimental]"
+---
+
+## Set up Advanced AI SDK Features
+
+Configure cutting-edge AI SDK capabilities for: $ARGUMENTS
+
+### Current Advanced Features Analysis
+
+Existing advanced implementations: !`grep -r "experimental\|reasoning\|computer\|streamUI" . --include="*.ts" --include="*.tsx" | head -5`
+
+Provider-specific features: !`grep -r "o1-preview\|deepseek\|computer_20241022\|responses" . --include="*.ts" | head -5`
+
+Experimental configurations: !`grep -r "experimental_" . --include="*.ts" | head -5`
+
+### Advanced Feature Categories
+
+**Reasoning Models**: O1-Preview, O3-mini, DeepSeek R1 with thinking capabilities
+**Computer Use**: Claude 3.5 Sonnet screen interaction and automation
+**Generative UI**: Dynamic component streaming with streamUI
+**Edge Optimization**: Vercel Edge Runtime performance enhancements
+**Experimental**: Cutting-edge AI SDK experimental features
+
+### Your Task
+
+1. **Analyze project requirements** for advanced AI capabilities
+2. **Configure reasoning models** with thinking mode and extended context
+3. **Set up computer use tools** for automation and testing
+4. **Implement generative UI** with dynamic component generation
+5. **Optimize for edge deployment** with performance enhancements
+6. **Enable experimental features** safely with proper fallbacks
+7. **Add comprehensive monitoring** for advanced feature usage
+8. **Create testing strategies** for cutting-edge capabilities
+
+### Implementation Requirements
+
+#### Reasoning Models Integration
+
+- O1-Preview and O3-mini setup with thinking tokens
+- DeepSeek R1 configuration for enhanced reasoning
+- Thinking mode visibility and streaming
+- Extended context window management
+- Reasoning-specific prompt engineering
+
+#### Computer Use Capabilities
+
+- Claude 3.5 Sonnet computer use tool setup
+- Screen interaction and automation
+- Browser automation and testing
+- File system operations
+- Cross-platform compatibility
+
+#### Generative UI Features
+
+- streamUI implementation for dynamic components
+- Real-time component generation
+- Interactive widget creation
+- Chart and visualization streaming
+- Form and dashboard generation
+
+### Expected Deliverables
+
+1. **Advanced provider configurations** with reasoning and computer use
+2. **Generative UI implementation** with component streaming
+3. **Edge runtime optimizations** for global deployment
+4. **Experimental features setup** with safety controls
+5. **Performance monitoring** for advanced capabilities
+6. **Testing suite** covering all advanced features
+7. **Documentation** with examples and best practices
+
+### Advanced Provider Setup
+
+#### Reasoning Models Configuration
+
+```typescript
+// lib/reasoning-providers.ts
+import { openai } from '@ai-sdk/openai';
+import { createOpenAI } from '@ai-sdk/openai';
+
+// OpenAI O1 Models
+export const o1Preview = openai('o1-preview', {
+  // Reasoning-specific configuration
+  experimental_reasoning: true,
+  experimental_thinkingMode: 'visible',
+  maxCompletionTokens: 32768,
+  temperature: 1.0, // Fixed for reasoning models
+});
+
+export const o3Mini = openai('o3-mini', {
+  experimental_reasoning: true,
+  experimental_thinkingTokens: true,
+  experimental_thinkingMode: 'visible',
+  maxCompletionTokens: 65536,
+});
+
+// DeepSeek R1
+export const deepseekR1 = createOpenAI({
+  apiKey: process.env.DEEPSEEK_API_KEY,
+  baseURL: 'https://api.deepseek.com/v1',
+})('deepseek-reasoner', {
+  experimental_reasoning: true,
+  experimental_thinkingTokens: true,
+  maxTokens: 8192,
+});
+
+// Reasoning model selector
+export function selectReasoningModel(complexity: 'simple' | 'complex' | 'mathematical') {
+  switch (complexity) {
+    case 'mathematical':
+      return o1Preview; // Best for math and logic
+    case 'complex':
+      return o3Mini; // Good for complex reasoning
+    case 'simple':
+      return deepseekR1; // Fast for simple reasoning
+    default:
+      return o1Preview;
+  }
+}
+```
+
+#### Computer Use Implementation
+
+```typescript
+// lib/computer-use.ts
+import { anthropic } from '@ai-sdk/anthropic';
+import { tool } from 'ai';
+import { z } from 'zod';
+
+export const computerUseTool = anthropic.tools.computer_20241022({
+  displayWidthPx: 1920,
+  displayHeightPx: 1080,
+  execute: async ({ action, coordinate, text }) => {
+    // Implement safe computer interactions
+    return await executeComputerAction(action, coordinate, text);
+  },
+});
+
+export const browserAutomationTool = tool({
+  description: 'Automate browser interactions for testing and data collection',
+  inputSchema: z.object({
+    url: z.string().url(),
+    actions: z.array(z.object({
+      type: z.enum(['navigate', 'click', 'type', 'wait', 'screenshot']),
+      selector: z.string().optional(),
+      text: z.string().optional(),
+    })),
+  }),
+  execute: async ({ url, actions }) => {
+    const results = [];
+    
+    for (const action of actions) {
+      const result = await executeBrowserAction(action, url);
+      results.push(result);
+      
+      if (!result.success) break; // Stop on error
+    }
+    
+    return { success: true, results };
+  },
+});
+
+// Safe computer action execution with permissions
+async function executeComputerAction(action: string, coordinate?: [number, number], text?: string) {
+  // Security checks
+  const allowedActions = ['screenshot', 'click', 'type', 'scroll'];
+  if (!allowedActions.includes(action)) {
+    throw new Error(`Action not allowed: ${action}`);
+  }
+  
+  // Rate limiting
+  await checkRateLimit(`computer_${action}`);
+  
+  // Execute action based on platform
+  switch (action) {
+    case 'screenshot':
+      return await takeScreenshot();
+    case 'click':
+      if (!coordinate) throw new Error('Click requires coordinates');
+      return await performClick(coordinate);
+    case 'type':
+      if (!text) throw new Error('Type requires text');
+      return await typeText(text);
+    case 'scroll':
+      return await performScroll(text || 'down');
+    default:
+      throw new Error(`Unsupported action: ${action}`);
+  }
+}
+```
+
+#### Generative UI Setup
+
+```typescript
+// app/api/ui/route.ts
+import { streamUI } from 'ai/rsc';
+import { anthropic } from '@ai-sdk/anthropic';
+import { z } from 'zod';
+
+export async function POST(req: Request) {
+  const { messages } = await req.json();
+
+  const result = streamUI({
+    model: anthropic('claude-3-sonnet-20240229'),
+    messages,
+    text: ({ content }) => <div>{content}</div>,
+    
+    tools: {
+      createChart: {
+        description: 'Generate interactive charts and visualizations',
+        inputSchema: z.object({
+          type: z.enum(['bar', 'line', 'pie', 'scatter', 'heatmap']),
+          data: z.array(z.record(z.any())),
+          title: z.string(),
+          options: z.record(z.any()).optional(),
+        }),
+        generate: async ({ type, data, title, options }) => {
+          const { default: Chart } = await import('@/components/dynamic-chart');
+          return <Chart type={type} data={data} title={title} options={options} />;
+        },
+      },
+      
+      createForm: {
+        description: 'Generate dynamic forms with validation',
+        inputSchema: z.object({
+          fields: z.array(z.object({
+            name: z.string(),
+            type: z.enum(['text', 'email', 'number', 'select', 'textarea']),
+            required: z.boolean(),
+            options: z.array(z.string()).optional(),
+          })),
+          title: z.string(),
+          onSubmit: z.string().optional(), // Callback name
+        }),
+        generate: async ({ fields, title, onSubmit }) => {
+          const { default: DynamicForm } = await import('@/components/dynamic-form');
+          return <DynamicForm fields={fields} title={title} onSubmit={onSubmit} />;
+        },
+      },
+      
+      createDashboard: {
+        description: 'Build interactive dashboards with multiple widgets',
+        inputSchema: z.object({
+          layout: z.enum(['grid', 'flex', 'sidebar']),
+          widgets: z.array(z.object({
+            type: z.enum(['metric', 'chart', 'table', 'list']),
+            title: z.string(),
+            data: z.any(),
+            size: z.enum(['small', 'medium', 'large']).optional(),
+          })),
+        }),
+        generate: async ({ layout, widgets }) => {
+          const { default: Dashboard } = await import('@/components/dynamic-dashboard');
+          return <Dashboard layout={layout} widgets={widgets} />;
+        },
+      },
+    },
+  });
+
+  return result.toDataStreamResponse();
+}
+```
+
+### Edge Optimization Configuration
+
+```typescript
+// next.config.js - Advanced edge configuration
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  experimental: {
+    runtime: 'edge',
+    serverComponentsExternalPackages: [
+      '@ai-sdk/anthropic',
+      '@ai-sdk/openai',
+      '@ai-sdk/google',
+    ],
+    // Advanced streaming
+    streaming: {
+      compression: true,
+      keepAlive: true,
+      timeout: 300000, // 5 minutes
+    },
+    // Edge-specific features
+    edgeRuntime: {
+      unsafeEval: false, // Security
+      allowMiddlewareResponseBody: true,
+    },
+  },
+  
+  webpack: (config, { nextRuntime, isServer }) => {
+    if (nextRuntime === 'edge') {
+      // Edge runtime optimizations
+      config.resolve.fallback = {
+        ...config.resolve.fallback,
+        fs: false,
+        net: false,
+        tls: false,
+        crypto: false,
+      };
+      
+      // Reduce bundle size for edge
+      config.externals = [
+        ...(config.externals || []),
+        'sharp', // Image processing
+        'canvas', // Canvas operations
+      ];
+    }
+    
+    return config;
+  },
+  
+  // Advanced headers for performance
+  headers: async () => [
+    {
+      source: '/api/:path*',
+      headers: [
+        {
+          key: 'Cache-Control',
+          value: 'public, max-age=0, s-maxage=3600, stale-while-revalidate=86400',
+        },
+        {
+          key: 'X-Content-Type-Options',
+          value: 'nosniff',
+        },
+        {
+          key: 'X-Frame-Options',
+          value: 'DENY',
+        },
+        {
+          key: 'X-XSS-Protection',
+          value: '1; mode=block',
+        },
+      ],
+    },
+  ],
+};
+
+module.exports = nextConfig;
+```
+
+### Experimental Features Configuration
+
+```typescript
+// lib/experimental-features.ts
+import { streamText, generateObject, streamUI } from 'ai';
+
+export const experimentalConfig = {
+  // Multi-modal streaming
+  multimodalStreaming: true,
+  
+  // Advanced tool calling
+  toolCallStreaming: true,
+  continueSteps: true,
+  
+  // Reasoning capabilities
+  reasoning: true,
+  thinkingMode: 'visible',
+  thinkingTokens: true,
+  
+  // Performance optimizations
+  streamingTimeouts: {
+    streamingTimeout: 30000,
+    completeTimeout: 120000,
+    keepAliveInterval: 5000,
+  },
+  
+  // Memory management
+  memoryManagement: {
+    maxTokensInMemory: 50000,
+    enableGarbageCollection: true,
+    cleanupInterval: 60000,
+  },
+  
+  // Connection optimization
+  connectionOptimization: {
+    enableCompression: true,
+    enableKeepAlive: true,
+    connectionPooling: true,
+  },
+};
+
+// Experimental feature wrapper
+export function withExperimentalFeatures<T extends Function>(fn: T): T {
+  return (async (...args: any[]) => {
+    try {
+      // Enable experimental features for this call
+      const result = await fn(...args);
+      
+      // Track experimental feature usage
+      await trackExperimentalUsage(fn.name, true);
+      
+      return result;
+    } catch (error) {
+      // Fallback to stable version on experimental failure
+      console.warn(`Experimental feature ${fn.name} failed, falling back:`, error);
+      
+      await trackExperimentalUsage(fn.name, false);
+      
+      // Implement fallback logic here
+      throw error; // or return fallback result
+    }
+  }) as T;
+}
+
+// Feature flag system
+export class FeatureFlags {
+  private static flags = new Map<string, boolean>();
+  
+  static async initialize() {
+    // Load feature flags from environment or external service
+    this.flags.set('reasoning_models', process.env.ENABLE_REASONING === 'true');
+    this.flags.set('computer_use', process.env.ENABLE_COMPUTER_USE === 'true');
+    this.flags.set('generative_ui', process.env.ENABLE_GENERATIVE_UI === 'true');
+    this.flags.set('edge_optimization', process.env.ENABLE_EDGE_OPT === 'true');
+  }
+  
+  static isEnabled(feature: string): boolean {
+    return this.flags.get(feature) ?? false;
+  }
+  
+  static enable(feature: string) {
+    this.flags.set(feature, true);
+  }
+  
+  static disable(feature: string) {
+    this.flags.set(feature, false);
+  }
+}
+
+async function trackExperimentalUsage(feature: string, success: boolean) {
+  // Track experimental feature usage for monitoring
+  const usage = {
+    feature,
+    success,
+    timestamp: Date.now(),
+    environment: process.env.NODE_ENV,
+  };
+  
+  // Send to analytics service
+  console.log('Experimental feature usage:', usage);
+}
+```
+
+### Advanced Monitoring and Analytics
+
+```typescript
+// lib/advanced-monitoring.ts
+export class AdvancedMonitoring {
+  static async recordAdvancedMetric(
+    feature: string,
+    metric: string,
+    value: number,
+    metadata: Record<string, any> = {}
+  ) {
+    const record = {
+      feature,
+      metric,
+      value,
+      metadata,
+      timestamp: Date.now(),
+      environment: process.env.NODE_ENV,
+      region: process.env.VERCEL_REGION || 'unknown',
+    };
+    
+    // Send to monitoring service
+    await this.sendToMonitoring(record);
+  }
+  
+  static async recordReasoningMetrics(
+    model: string,
+    thinkingTokens: number,
+    completionTokens: number,
+    success: boolean
+  ) {
+    await this.recordAdvancedMetric('reasoning', 'token_usage', thinkingTokens + completionTokens, {
+      model,
+      thinking_tokens: thinkingTokens,
+      completion_tokens: completionTokens,
+      success,
+    });
+  }
+  
+  static async recordComputerUseMetrics(
+    action: string,
+    duration: number,
+    success: boolean
+  ) {
+    await this.recordAdvancedMetric('computer_use', 'action_duration', duration, {
+      action,
+      success,
+    });
+  }
+  
+  static async recordGenerativeUIMetrics(
+    componentType: string,
+    renderTime: number,
+    complexity: 'low' | 'medium' | 'high'
+  ) {
+    await this.recordAdvancedMetric('generative_ui', 'render_time', renderTime, {
+      component_type: componentType,
+      complexity,
+    });
+  }
+  
+  private static async sendToMonitoring(record: any) {
+    // Implementation depends on your monitoring service
+    // Examples: DataDog, New Relic, Custom Analytics
+    console.log('Advanced Monitoring:', record);
+  }
+}
+```
+
+### Testing Advanced Features
+
+```typescript
+// tests/advanced-features.test.ts
+import { describe, it, expect } from 'vitest';
+import { experimentalConfig, FeatureFlags } from '@/lib/experimental-features';
+
+describe('Advanced Features', () => {
+  beforeAll(async () => {
+    await FeatureFlags.initialize();
+  });
+  
+  it('should handle reasoning models', async () => {
+    if (!FeatureFlags.isEnabled('reasoning_models')) {
+      return; // Skip if not enabled
+    }
+    
+    const result = await testReasoningModel();
+    expect(result.success).toBe(true);
+    expect(result.thinking_tokens).toBeGreaterThan(0);
+  });
+  
+  it('should execute computer use safely', async () => {
+    if (!FeatureFlags.isEnabled('computer_use')) {
+      return;
+    }
+    
+    const result = await testComputerUse();
+    expect(result.screenshot).toBeDefined();
+    expect(result.actions).toBeInstanceOf(Array);
+  });
+  
+  it('should generate UI components', async () => {
+    if (!FeatureFlags.isEnabled('generative_ui')) {
+      return;
+    }
+    
+    const component = await testGenerativeUI();
+    expect(component).toBeDefined();
+    expect(component.type).toBe('chart');
+  });
+});
+```
+
+### Security Considerations
+
+- **Feature flags**: Control advanced features with environment variables
+- **Rate limiting**: Implement strict limits for resource-intensive features
+- **Permissions**: Computer use requires explicit user permissions
+- **Monitoring**: Track all advanced feature usage and errors
+- **Fallbacks**: Always have stable alternatives for experimental features
+- **Testing**: Comprehensive testing in isolated environments
+- **Documentation**: Clear usage guidelines and safety measures
+
+Focus on building cutting-edge AI applications that push the boundaries of what's possible while maintaining security, reliability, and user safety.
\ No newline at end of file
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-chat-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-chat-setup.md
new file mode 100644
index 0000000..d794c10
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-chat-setup.md
@@ -0,0 +1,58 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Set up a complete AI chat interface with streaming
+argument-hint: "[basic|advanced|multimodal|rag|agent]"
+---
+
+## Set up AI Chat Interface
+
+Create a production-ready chat interface with the Vercel AI SDK based on the specified type: $ARGUMENTS
+
+### Project Context
+
+Current project structure: !`find . -type f -name "*.json" -o -name "*.ts" -o -name "*.tsx" | head -10`
+
+Current dependencies: !`cat package.json | jq '.dependencies // {}' 2>/dev/null || echo "No package.json found"`
+
+### Requirements Analysis
+
+Based on the requested chat type ($ARGUMENTS), I'll implement:
+
+**Basic Chat**: Simple text-based streaming chat interface
+**Advanced Chat**: Enhanced UI with message history, error handling, and optimizations  
+**Multimodal Chat**: Support for images, PDFs, and file uploads
+**RAG Chat**: Retrieval-augmented generation with knowledge base
+**Agent Chat**: Tool-calling agents with function execution
+
+### Your Task
+
+1. **Analyze the current project structure** to understand the existing setup
+2. **Install required dependencies** if not already present
+3. **Create the appropriate chat implementation** based on the specified type
+4. **Set up the API route** with proper streaming and error handling
+5. **Implement the React component** with modern UI patterns
+6. **Add proper TypeScript types** for type safety
+7. **Include error boundaries** and loading states
+8. **Test the implementation** and provide usage instructions
+
+### Implementation Guidelines
+
+- Use the latest AI SDK patterns and best practices
+- Implement proper error handling and loading states
+- Add TypeScript types for all interfaces
+- Follow Next.js App Router conventions
+- Include proper accessibility features
+- Use modern React patterns (hooks, Suspense, etc.)
+- Add responsive design considerations
+- Implement proper security measures
+
+### Expected Deliverables
+
+1. API route handler (`app/api/chat/route.ts`)
+2. Chat component (`components/chat.tsx` or similar)
+3. Required TypeScript types
+4. Updated package.json dependencies
+5. Basic styling (Tailwind classes)
+6. Usage documentation and examples
+
+Focus on creating a robust, production-ready implementation that follows AI SDK best practices and modern web development standards.
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-experimental-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-experimental-setup.md
new file mode 100644
index 0000000..80d83ff
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-experimental-setup.md
@@ -0,0 +1,793 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Enable cutting-edge experimental AI SDK features safely
+argument-hint: "[beta|experimental|research|custom]"
+---
+
+## Enable Experimental AI SDK Features
+
+Configure and safely enable cutting-edge AI SDK experimental features: $ARGUMENTS
+
+### Current Experimental Status
+
+Existing experimental features: !`grep -r "experimental\|beta\|alpha" . --include="*.ts" --include="*.json" | head -5`
+
+Feature flags: !`grep -r "ENABLE_\|FLAG_" .env* 2>/dev/null | head -3 || echo "No feature flags found"`
+
+Advanced configurations: !`grep -r "streamingTimeouts\|thinkingMode\|toolCallStreaming" . --include="*.ts" | head -5`
+
+### Experimental Feature Categories
+
+**Beta Features**: Stable experimental features ready for production testing
+**Experimental**: Cutting-edge features in active development
+**Research**: Bleeding-edge research features for experimentation
+**Custom**: Custom experimental implementations and modifications
+
+### Your Task
+
+1. **Analyze experimental feature landscape** and identify safe options
+2. **Implement feature flag system** for controlled rollouts
+3. **Configure experimental AI SDK options** with proper safeguards
+4. **Set up A/B testing framework** for feature validation
+5. **Add monitoring and telemetry** for experimental features
+6. **Create fallback mechanisms** for experimental feature failures
+7. **Implement gradual rollout strategy** with user controls
+8. **Add comprehensive testing** for experimental features
+
+### Implementation Requirements
+
+#### Feature Flag System
+
+- Environment-based feature control
+- User-level feature toggles
+- Percentage-based rollouts
+- Real-time feature flag updates
+- Fallback mechanisms for failures
+
+#### Safety Measures
+
+- Automatic fallback to stable features
+- Error isolation and reporting
+- Performance impact monitoring
+- User experience protection
+- Data integrity guarantees
+
+#### Experimental Configuration
+
+- Advanced streaming options
+- Cutting-edge model features
+- Research-level AI capabilities
+- Custom provider integrations
+- Performance optimizations
+
+### Expected Deliverables
+
+1. **Feature flag system** with environment and user controls
+2. **Experimental AI SDK configurations** with safety controls
+3. **A/B testing framework** for feature validation
+4. **Monitoring and telemetry** for experimental features
+5. **Fallback mechanisms** for reliability
+6. **Documentation** for experimental feature usage
+7. **Testing suite** covering experimental scenarios
+
+### Feature Flag Infrastructure
+
+#### Core Feature Flag System
+
+```typescript
+// lib/experimental/feature-flags.ts
+interface FeatureFlag {
+  name: string;
+  enabled: boolean;
+  rolloutPercentage: number;
+  conditions?: {
+    userIds?: string[];
+    environments?: string[];
+    regions?: string[];
+    custom?: (context: any) => boolean;
+  };
+  metadata?: {
+    description: string;
+    added: string;
+    owner: string;
+    stableDate?: string;
+  };
+}
+
+export class ExperimentalFeatureManager {
+  private static instance: ExperimentalFeatureManager;
+  private flags: Map<string, FeatureFlag> = new Map();
+  private context: any = {};
+
+  static getInstance(): ExperimentalFeatureManager {
+    if (!ExperimentalFeatureManager.instance) {
+      ExperimentalFeatureManager.instance = new ExperimentalFeatureManager();
+    }
+    return ExperimentalFeatureManager.instance;
+  }
+
+  async initialize(context: any = {}) {
+    this.context = context;
+    await this.loadFeatureFlags();
+  }
+
+  private async loadFeatureFlags() {
+    // Load from environment variables
+    const envFlags = this.loadFromEnvironment();
+    
+    // Load from external service (optional)
+    const remoteFlags = await this.loadFromRemoteService();
+    
+    // Merge flags with priority: remote > environment > defaults
+    const allFlags = { ...this.getDefaultFlags(), ...envFlags, ...remoteFlags };
+    
+    Object.entries(allFlags).forEach(([name, flag]) => {
+      this.flags.set(name, flag as FeatureFlag);
+    });
+  }
+
+  private getDefaultFlags(): Record<string, FeatureFlag> {
+    return {
+      'reasoning-models': {
+        name: 'reasoning-models',
+        enabled: false,
+        rolloutPercentage: 0,
+        metadata: {
+          description: 'Enable O1, O3-mini, and DeepSeek reasoning models',
+          added: '2024-12-01',
+          owner: 'ai-team',
+        },
+      },
+      'computer-use': {
+        name: 'computer-use',
+        enabled: false,
+        rolloutPercentage: 0,
+        conditions: {
+          environments: ['development', 'staging'],
+        },
+        metadata: {
+          description: 'Enable Claude 3.5 Sonnet computer use capabilities',
+          added: '2024-12-01',
+          owner: 'automation-team',
+        },
+      },
+      'generative-ui': {
+        name: 'generative-ui',
+        enabled: true,
+        rolloutPercentage: 100,
+        metadata: {
+          description: 'Enable streamUI for dynamic component generation',
+          added: '2024-11-01',
+          owner: 'ui-team',
+        },
+      },
+      'advanced-streaming': {
+        name: 'advanced-streaming',
+        enabled: true,
+        rolloutPercentage: 50,
+        metadata: {
+          description: 'Advanced streaming patterns with multi-step and waitUntil',
+          added: '2024-11-15',
+          owner: 'streaming-team',
+        },
+      },
+      'edge-optimization': {
+        name: 'edge-optimization',
+        enabled: true,
+        rolloutPercentage: 75,
+        conditions: {
+          environments: ['production', 'staging'],
+        },
+        metadata: {
+          description: 'Vercel Edge Runtime optimizations',
+          added: '2024-10-01',
+          owner: 'performance-team',
+        },
+      },
+      'natural-language-sql': {
+        name: 'natural-language-sql',
+        enabled: false,
+        rolloutPercentage: 25,
+        conditions: {
+          custom: (context) => context.hasDatabase === true,
+        },
+        metadata: {
+          description: 'Natural language to SQL conversion',
+          added: '2024-12-10',
+          owner: 'data-team',
+        },
+      },
+    };
+  }
+
+  private loadFromEnvironment(): Record<string, Partial<FeatureFlag>> {
+    const flags: Record<string, Partial<FeatureFlag>> = {};
+    
+    // Load from environment variables
+    if (process.env.ENABLE_REASONING_MODELS === 'true') {
+      flags['reasoning-models'] = { enabled: true, rolloutPercentage: 100 };
+    }
+    
+    if (process.env.ENABLE_COMPUTER_USE === 'true') {
+      flags['computer-use'] = { enabled: true, rolloutPercentage: 100 };
+    }
+    
+    if (process.env.ENABLE_GENERATIVE_UI === 'true') {
+      flags['generative-ui'] = { enabled: true, rolloutPercentage: 100 };
+    }
+    
+    if (process.env.ENABLE_ADVANCED_STREAMING === 'true') {
+      flags['advanced-streaming'] = { enabled: true, rolloutPercentage: 100 };
+    }
+    
+    if (process.env.ENABLE_EDGE_OPTIMIZATION === 'true') {
+      flags['edge-optimization'] = { enabled: true, rolloutPercentage: 100 };
+    }
+    
+    return flags;
+  }
+
+  private async loadFromRemoteService(): Promise<Record<string, Partial<FeatureFlag>>> {
+    // Optional: Load from external feature flag service
+    try {
+      if (process.env.FEATURE_FLAG_SERVICE_URL) {
+        const response = await fetch(process.env.FEATURE_FLAG_SERVICE_URL, {
+          headers: {
+            'Authorization': `Bearer ${process.env.FEATURE_FLAG_API_KEY}`,
+          },
+        });
+        
+        if (response.ok) {
+          return await response.json();
+        }
+      }
+    } catch (error) {
+      console.warn('Failed to load remote feature flags:', error);
+    }
+    
+    return {};
+  }
+
+  isEnabled(flagName: string, userId?: string): boolean {
+    const flag = this.flags.get(flagName);
+    if (!flag) return false;
+
+    // Check basic enabled status
+    if (!flag.enabled) return false;
+
+    // Check conditions
+    if (flag.conditions) {
+      if (flag.conditions.userIds && userId) {
+        if (!flag.conditions.userIds.includes(userId)) return false;
+      }
+      
+      if (flag.conditions.environments) {
+        const env = process.env.NODE_ENV || 'development';
+        if (!flag.conditions.environments.includes(env)) return false;
+      }
+      
+      if (flag.conditions.regions) {
+        const region = process.env.VERCEL_REGION || 'local';
+        if (!flag.conditions.regions.includes(region)) return false;
+      }
+      
+      if (flag.conditions.custom) {
+        if (!flag.conditions.custom(this.context)) return false;
+      }
+    }
+
+    // Check rollout percentage
+    if (flag.rolloutPercentage < 100) {
+      const hash = this.getUserHash(userId || 'anonymous', flagName);
+      if (hash % 100 >= flag.rolloutPercentage) return false;
+    }
+
+    return true;
+  }
+
+  private getUserHash(userId: string, flagName: string): number {
+    // Simple hash function for consistent user bucketing
+    let hash = 0;
+    const str = `${userId}-${flagName}`;
+    for (let i = 0; i < str.length; i++) {
+      const char = str.charCodeAt(i);
+      hash = ((hash << 5) - hash) + char;
+      hash = hash & hash; // Convert to 32-bit integer
+    }
+    return Math.abs(hash);
+  }
+
+  getAllFlags(): Map<string, FeatureFlag> {
+    return new Map(this.flags);
+  }
+
+  updateFlag(flagName: string, updates: Partial<FeatureFlag>) {
+    const existing = this.flags.get(flagName);
+    if (existing) {
+      this.flags.set(flagName, { ...existing, ...updates });
+    }
+  }
+
+  async trackFeatureUsage(flagName: string, userId?: string, metadata?: any) {
+    const usage = {
+      flag: flagName,
+      userId,
+      timestamp: Date.now(),
+      context: this.context,
+      metadata,
+    };
+
+    // Send to analytics service
+    await this.sendUsageToAnalytics(usage);
+  }
+
+  private async sendUsageToAnalytics(usage: any) {
+    try {
+      if (process.env.ANALYTICS_ENDPOINT) {
+        await fetch(process.env.ANALYTICS_ENDPOINT, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify(usage),
+        });
+      }
+    } catch (error) {
+      console.warn('Failed to send feature usage analytics:', error);
+    }
+  }
+}
+
+// Singleton instance
+export const featureFlags = ExperimentalFeatureManager.getInstance();
+```
+
+#### Experimental AI SDK Wrapper
+
+```typescript
+// lib/experimental/ai-sdk-experimental.ts
+import { streamText, generateText, streamUI, generateObject } from 'ai';
+import { featureFlags } from './feature-flags';
+
+export interface ExperimentalOptions {
+  userId?: string;
+  fallbackOnError?: boolean;
+  trackUsage?: boolean;
+}
+
+export class ExperimentalAISDK {
+  
+  static async streamText(config: any, options: ExperimentalOptions = {}) {
+    const { userId, fallbackOnError = true, trackUsage = true } = options;
+    
+    // Apply experimental features based on flags
+    const experimentalConfig = await this.applyExperimentalFeatures(config, userId);
+    
+    try {
+      const result = streamText(experimentalConfig);
+      
+      if (trackUsage) {
+        await this.trackExperimentalUsage(experimentalConfig, userId);
+      }
+      
+      return result;
+    } catch (error) {
+      if (fallbackOnError) {
+        console.warn('Experimental feature failed, falling back to stable:', error);
+        return streamText(config); // Fallback to original config
+      }
+      throw error;
+    }
+  }
+
+  static async generateText(config: any, options: ExperimentalOptions = {}) {
+    const { userId, fallbackOnError = true, trackUsage = true } = options;
+    
+    const experimentalConfig = await this.applyExperimentalFeatures(config, userId);
+    
+    try {
+      const result = await generateText(experimentalConfig);
+      
+      if (trackUsage) {
+        await this.trackExperimentalUsage(experimentalConfig, userId);
+      }
+      
+      return result;
+    } catch (error) {
+      if (fallbackOnError) {
+        console.warn('Experimental feature failed, falling back to stable:', error);
+        return generateText(config);
+      }
+      throw error;
+    }
+  }
+
+  static async streamUI(config: any, options: ExperimentalOptions = {}) {
+    const { userId, fallbackOnError = true, trackUsage = true } = options;
+    
+    if (!featureFlags.isEnabled('generative-ui', userId)) {
+      throw new Error('Generative UI is not enabled for this user');
+    }
+    
+    try {
+      const result = streamUI(config);
+      
+      if (trackUsage) {
+        await featureFlags.trackFeatureUsage('generative-ui', userId, {
+          toolCount: Object.keys(config.tools || {}).length,
+        });
+      }
+      
+      return result;
+    } catch (error) {
+      if (fallbackOnError) {
+        // Fallback to regular text streaming
+        console.warn('StreamUI failed, falling back to streamText:', error);
+        return streamText({
+          ...config,
+          text: ({ content }) => content, // Simple text output
+        });
+      }
+      throw error;
+    }
+  }
+
+  private static async applyExperimentalFeatures(config: any, userId?: string) {
+    const experimentalConfig = { ...config };
+
+    // Advanced streaming features
+    if (featureFlags.isEnabled('advanced-streaming', userId)) {
+      experimentalConfig.experimental_streamingTimeouts = {
+        streamingTimeout: 45000,
+        completeTimeout: 120000,
+        keepAliveInterval: 5000,
+      };
+      
+      experimentalConfig.experimental_toolCallStreaming = true;
+      experimentalConfig.experimental_continueSteps = true;
+      
+      await featureFlags.trackFeatureUsage('advanced-streaming', userId);
+    }
+
+    // Reasoning models
+    if (featureFlags.isEnabled('reasoning-models', userId)) {
+      if (config.model?.includes?.('o1') || config.model?.includes?.('reasoner')) {
+        experimentalConfig.experimental_reasoning = true;
+        experimentalConfig.experimental_thinkingMode = 'visible';
+        experimentalConfig.experimental_thinkingTokens = true;
+        
+        await featureFlags.trackFeatureUsage('reasoning-models', userId);
+      }
+    }
+
+    // Edge optimizations
+    if (featureFlags.isEnabled('edge-optimization', userId)) {
+      experimentalConfig.experimental_edgeOptimization = {
+        enableCompression: true,
+        enableKeepAlive: true,
+        connectionPooling: true,
+      };
+      
+      experimentalConfig.experimental_memoryManagement = {
+        maxTokensInMemory: 25000,
+        enableGarbageCollection: true,
+        cleanupInterval: 30000,
+      };
+      
+      await featureFlags.trackFeatureUsage('edge-optimization', userId);
+    }
+
+    return experimentalConfig;
+  }
+
+  private static async trackExperimentalUsage(config: any, userId?: string) {
+    const experimentalFeatures = [];
+    
+    if (config.experimental_streamingTimeouts) {
+      experimentalFeatures.push('advanced-streaming');
+    }
+    
+    if (config.experimental_reasoning) {
+      experimentalFeatures.push('reasoning-models');
+    }
+    
+    if (config.experimental_edgeOptimization) {
+      experimentalFeatures.push('edge-optimization');
+    }
+
+    for (const feature of experimentalFeatures) {
+      await featureFlags.trackFeatureUsage(feature, userId, {
+        configuration: Object.keys(config).filter(k => k.startsWith('experimental_')),
+      });
+    }
+  }
+}
+```
+
+### A/B Testing Framework
+
+#### Experiment Configuration
+
+```typescript
+// lib/experimental/ab-testing.ts
+export interface Experiment {
+  id: string;
+  name: string;
+  description: string;
+  status: 'draft' | 'running' | 'paused' | 'completed';
+  variants: {
+    id: string;
+    name: string;
+    percentage: number;
+    config: any;
+  }[];
+  targetAudience?: {
+    userIds?: string[];
+    percentage?: number;
+    conditions?: any;
+  };
+  metrics: string[];
+  startDate: Date;
+  endDate?: Date;
+}
+
+export class ExperimentManager {
+  private static instance: ExperimentManager;
+  private experiments: Map<string, Experiment> = new Map();
+
+  static getInstance(): ExperimentManager {
+    if (!ExperimentManager.instance) {
+      ExperimentManager.instance = new ExperimentManager();
+    }
+    return ExperimentManager.instance;
+  }
+
+  async initialize() {
+    await this.loadExperiments();
+  }
+
+  private async loadExperiments() {
+    // Load experiments from configuration
+    const defaultExperiments: Experiment[] = [
+      {
+        id: 'reasoning-vs-standard',
+        name: 'Reasoning Models vs Standard Models',
+        description: 'Compare performance of O1 reasoning models vs standard models',
+        status: 'running',
+        variants: [
+          { id: 'control', name: 'Standard Model', percentage: 50, config: { useReasoning: false } },
+          { id: 'treatment', name: 'Reasoning Model', percentage: 50, config: { useReasoning: true } },
+        ],
+        targetAudience: { percentage: 10 },
+        metrics: ['response_quality', 'latency', 'cost', 'user_satisfaction'],
+        startDate: new Date('2024-12-01'),
+        endDate: new Date('2024-12-31'),
+      },
+      {
+        id: 'streaming-optimization',
+        name: 'Advanced Streaming vs Basic Streaming',
+        description: 'Test advanced streaming features vs basic streaming',
+        status: 'running',
+        variants: [
+          { id: 'control', name: 'Basic Streaming', percentage: 70, config: { advancedStreaming: false } },
+          { id: 'treatment', name: 'Advanced Streaming', percentage: 30, config: { advancedStreaming: true } },
+        ],
+        metrics: ['latency', 'error_rate', 'user_engagement'],
+        startDate: new Date('2024-11-15'),
+        endDate: new Date('2024-12-15'),
+      },
+    ];
+
+    defaultExperiments.forEach(exp => {
+      this.experiments.set(exp.id, exp);
+    });
+  }
+
+  getVariant(experimentId: string, userId: string): any {
+    const experiment = this.experiments.get(experimentId);
+    if (!experiment || experiment.status !== 'running') {
+      return null;
+    }
+
+    // Check if user is in target audience
+    if (!this.isUserInAudience(experiment, userId)) {
+      return null;
+    }
+
+    // Determine variant based on user hash
+    const hash = this.getUserHash(userId, experimentId);
+    let cumulativePercentage = 0;
+    
+    for (const variant of experiment.variants) {
+      cumulativePercentage += variant.percentage;
+      if (hash % 100 < cumulativePercentage) {
+        return variant;
+      }
+    }
+
+    return experiment.variants[0]; // Fallback to first variant
+  }
+
+  private isUserInAudience(experiment: Experiment, userId: string): boolean {
+    if (!experiment.targetAudience) return true;
+
+    if (experiment.targetAudience.userIds) {
+      return experiment.targetAudience.userIds.includes(userId);
+    }
+
+    if (experiment.targetAudience.percentage) {
+      const hash = this.getUserHash(userId, experiment.id);
+      return (hash % 100) < experiment.targetAudience.percentage;
+    }
+
+    return true;
+  }
+
+  private getUserHash(userId: string, experimentId: string): number {
+    let hash = 0;
+    const str = `${userId}-${experimentId}`;
+    for (let i = 0; i < str.length; i++) {
+      const char = str.charCodeAt(i);
+      hash = ((hash << 5) - hash) + char;
+      hash = hash & hash;
+    }
+    return Math.abs(hash);
+  }
+
+  async recordMetric(experimentId: string, userId: string, metric: string, value: number) {
+    const variant = this.getVariant(experimentId, userId);
+    if (!variant) return;
+
+    const record = {
+      experimentId,
+      variantId: variant.id,
+      userId,
+      metric,
+      value,
+      timestamp: Date.now(),
+    };
+
+    await this.sendMetricToAnalytics(record);
+  }
+
+  private async sendMetricToAnalytics(record: any) {
+    try {
+      if (process.env.EXPERIMENT_ANALYTICS_ENDPOINT) {
+        await fetch(process.env.EXPERIMENT_ANALYTICS_ENDPOINT, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify(record),
+        });
+      }
+    } catch (error) {
+      console.warn('Failed to send experiment metric:', error);
+    }
+  }
+}
+
+export const experiments = ExperimentManager.getInstance();
+```
+
+### API Integration
+
+#### Experimental API Route
+
+```typescript
+// app/api/experimental/chat/route.ts
+import { ExperimentalAISDK } from '@/lib/experimental/ai-sdk-experimental';
+import { experiments } from '@/lib/experimental/ab-testing';
+import { anthropic } from '@ai-sdk/anthropic';
+
+export const runtime = 'edge';
+export const maxDuration = 300;
+
+export async function POST(req: Request) {
+  const { messages, userId } = await req.json();
+
+  try {
+    // Get experiment variant
+    const reasoningExperiment = experiments.getVariant('reasoning-vs-standard', userId);
+    const streamingExperiment = experiments.getVariant('streaming-optimization', userId);
+
+    // Configure based on experiments
+    const config = {
+      model: reasoningExperiment?.config.useReasoning 
+        ? anthropic('claude-3-sonnet-20240229')  // Would use O1 in real implementation
+        : anthropic('claude-3-sonnet-20240229'),
+      messages,
+    };
+
+    // Use experimental SDK
+    const result = await ExperimentalAISDK.streamText(config, {
+      userId,
+      fallbackOnError: true,
+      trackUsage: true,
+    });
+
+    // Record experiment metrics
+    if (reasoningExperiment) {
+      // This would be implemented with actual metrics
+      await experiments.recordMetric('reasoning-vs-standard', userId, 'request_count', 1);
+    }
+
+    return result.toUIMessageStreamResponse();
+
+  } catch (error) {
+    console.error('Experimental chat error:', error);
+    
+    // Fallback to stable implementation
+    const result = await ExperimentalAISDK.streamText({
+      model: anthropic('claude-3-sonnet-20240229'),
+      messages,
+    }, { userId, fallbackOnError: false });
+
+    return result.toUIMessageStreamResponse();
+  }
+}
+```
+
+### Monitoring and Safety
+
+#### Experimental Feature Monitoring
+
+```typescript
+// lib/experimental/monitoring.ts
+export class ExperimentalMonitoring {
+  static async recordFeaturePerformance(
+    featureName: string,
+    metrics: {
+      latency?: number;
+      errorRate?: number;
+      userSatisfaction?: number;
+      cost?: number;
+    },
+    userId?: string
+  ) {
+    const record = {
+      feature: featureName,
+      metrics,
+      userId,
+      timestamp: Date.now(),
+      environment: process.env.NODE_ENV,
+      version: process.env.APP_VERSION || 'unknown',
+    };
+
+    await this.sendToMonitoring(record);
+  }
+
+  static async checkFeatureHealth(featureName: string): Promise<boolean> {
+    // Check if feature is performing within acceptable parameters
+    const healthData = await this.getFeatureHealthData(featureName);
+    
+    return (
+      healthData.errorRate < 0.05 && // Less than 5% error rate
+      healthData.latency < 5000 && // Less than 5 second latency
+      healthData.userSatisfaction > 0.7 // Above 70% satisfaction
+    );
+  }
+
+  private static async sendToMonitoring(record: any) {
+    // Send to monitoring service
+    console.log('Experimental Feature Metrics:', record);
+  }
+
+  private static async getFeatureHealthData(featureName: string) {
+    // This would fetch real health data from monitoring service
+    return {
+      errorRate: Math.random() * 0.1,
+      latency: Math.random() * 3000,
+      userSatisfaction: 0.7 + (Math.random() * 0.3),
+    };
+  }
+}
+```
+
+### Usage and Safety Guidelines
+
+- **Feature flags**: Control experimental features with environment variables and remote configuration
+- **A/B testing**: Run controlled experiments to validate new features
+- **Fallback mechanisms**: Always provide stable alternatives for experimental features
+- **Monitoring**: Track performance and user experience of experimental features
+- **Gradual rollout**: Start with small percentages and increase based on performance
+- **Safety checks**: Implement automatic feature disabling for poor performance
+- **Documentation**: Document experimental features and their expected behavior
+
+Focus on building a robust experimental framework that enables safe innovation while protecting user experience and system stability.
\ No newline at end of file
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-monitoring-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-monitoring-setup.md
new file mode 100644
index 0000000..ee3baac
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-monitoring-setup.md
@@ -0,0 +1,807 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Set up comprehensive monitoring and analytics for AI SDK applications
+argument-hint: "[performance|usage|costs|errors|analytics|dashboard]"
+---
+
+## Set up AI SDK Monitoring and Analytics
+
+Configure comprehensive monitoring and analytics for AI SDK applications: $ARGUMENTS
+
+### Current Monitoring Analysis
+
+Existing monitoring setup: !`grep -r "monitoring\|analytics\|metrics" . --include="*.ts" --include="*.tsx" | head -5`
+
+Performance tracking: !`grep -r "performance\|latency\|duration" . --include="*.ts" | head -5`
+
+Error handling: !`grep -r "error\|catch\|throw" . --include="*.ts" | head -5`
+
+### Monitoring Categories
+
+**Performance**: Latency, throughput, response times, edge performance
+**Usage**: Token consumption, request patterns, user behavior
+**Costs**: Provider costs, usage optimization, budget tracking
+**Errors**: Error rates, failure patterns, recovery metrics
+**Analytics**: User insights, feature adoption, performance trends
+**Dashboard**: Real-time monitoring, alerts, visualization
+
+### Your Task
+
+1. **Analyze monitoring requirements** for comprehensive AI SDK observability
+2. **Implement performance tracking** with latency and throughput metrics
+3. **Set up usage analytics** for token consumption and cost tracking
+4. **Configure error monitoring** with detailed error classification
+5. **Build real-time dashboard** for monitoring AI SDK applications
+6. **Add alerting system** for performance and error thresholds
+7. **Create analytics reports** for insights and optimization
+8. **Integrate with external services** (DataDog, New Relic, etc.)
+
+### Implementation Requirements
+
+#### Performance Monitoring
+
+- Request/response latency tracking
+- Streaming performance metrics
+- Provider response time monitoring
+- Edge runtime performance tracking
+- Memory and CPU usage monitoring
+
+#### Usage Analytics
+
+- Token consumption tracking by provider and model
+- Request volume and patterns
+- User behavior and feature adoption
+- Geographic usage distribution
+- Time-based usage patterns
+
+#### Cost Management
+
+- Real-time cost calculation across providers
+- Budget tracking and alerting
+- Cost optimization recommendations
+- Provider cost comparison
+- Usage forecasting and planning
+
+### Expected Deliverables
+
+1. **Performance monitoring system** with real-time metrics
+2. **Usage analytics dashboard** with cost tracking
+3. **Error monitoring and alerting** with detailed classification
+4. **Custom analytics implementation** for AI SDK specific metrics
+5. **Integration setup** for external monitoring services
+6. **Real-time dashboard** with visualizations and alerts
+7. **Monitoring documentation** with setup and usage guides
+
+### Performance Monitoring Implementation
+
+#### Core Monitoring Infrastructure
+
+```typescript
+// lib/monitoring/core.ts
+import { performance } from 'perf_hooks';
+
+export interface MetricData {
+  name: string;
+  value: number;
+  timestamp: number;
+  tags: Record<string, string>;
+  metadata?: Record<string, any>;
+}
+
+export interface PerformanceMetrics {
+  latency: number;
+  tokens: {
+    input: number;
+    output: number;
+    total: number;
+  };
+  cost: number;
+  provider: string;
+  model: string;
+  success: boolean;
+  errorType?: string;
+}
+
+export class AISDKMonitor {
+  private static instance: AISDKMonitor;
+  private metrics: MetricData[] = [];
+  private performanceData: Map<string, PerformanceMetrics> = new Map();
+
+  static getInstance(): AISDKMonitor {
+    if (!AISDKMonitor.instance) {
+      AISDKMonitor.instance = new AISDKMonitor();
+    }
+    return AISDKMonitor.instance;
+  }
+
+  // Record basic metrics
+  recordMetric(name: string, value: number, tags: Record<string, string> = {}) {
+    const metric: MetricData = {
+      name,
+      value,
+      timestamp: Date.now(),
+      tags: {
+        ...tags,
+        environment: process.env.NODE_ENV || 'development',
+        region: process.env.VERCEL_REGION || 'local',
+      },
+    };
+
+    this.metrics.push(metric);
+    this.sendToExternalServices(metric);
+  }
+
+  // Record comprehensive performance metrics
+  recordPerformance(requestId: string, metrics: PerformanceMetrics) {
+    this.performanceData.set(requestId, metrics);
+    
+    // Record individual metrics
+    this.recordMetric('ai_request_latency', metrics.latency, {
+      provider: metrics.provider,
+      model: metrics.model,
+      success: metrics.success.toString(),
+    });
+
+    this.recordMetric('ai_token_usage', metrics.tokens.total, {
+      provider: metrics.provider,
+      model: metrics.model,
+      type: 'total',
+    });
+
+    this.recordMetric('ai_request_cost', metrics.cost, {
+      provider: metrics.provider,
+      model: metrics.model,
+    });
+
+    if (!metrics.success && metrics.errorType) {
+      this.recordMetric('ai_error_count', 1, {
+        provider: metrics.provider,
+        model: metrics.model,
+        error_type: metrics.errorType,
+      });
+    }
+  }
+
+  // Get performance analytics
+  getPerformanceAnalytics(timeRange: { start: Date; end: Date }) {
+    const filteredMetrics = this.metrics.filter(m => 
+      m.timestamp >= timeRange.start.getTime() && 
+      m.timestamp <= timeRange.end.getTime()
+    );
+
+    return {
+      totalRequests: filteredMetrics.filter(m => m.name === 'ai_request_latency').length,
+      averageLatency: this.calculateAverage(filteredMetrics, 'ai_request_latency'),
+      totalTokens: this.calculateSum(filteredMetrics, 'ai_token_usage'),
+      totalCost: this.calculateSum(filteredMetrics, 'ai_request_cost'),
+      errorRate: this.calculateErrorRate(filteredMetrics),
+      providerBreakdown: this.getProviderBreakdown(filteredMetrics),
+    };
+  }
+
+  private calculateAverage(metrics: MetricData[], metricName: string): number {
+    const relevant = metrics.filter(m => m.name === metricName);
+    if (relevant.length === 0) return 0;
+    return relevant.reduce((sum, m) => sum + m.value, 0) / relevant.length;
+  }
+
+  private calculateSum(metrics: MetricData[], metricName: string): number {
+    return metrics
+      .filter(m => m.name === metricName)
+      .reduce((sum, m) => sum + m.value, 0);
+  }
+
+  private calculateErrorRate(metrics: MetricData[]): number {
+    const totalRequests = metrics.filter(m => m.name === 'ai_request_latency').length;
+    const errors = metrics.filter(m => m.name === 'ai_error_count').length;
+    return totalRequests > 0 ? errors / totalRequests : 0;
+  }
+
+  private getProviderBreakdown(metrics: MetricData[]) {
+    const providers = new Map<string, { requests: number; tokens: number; cost: number }>();
+    
+    metrics.forEach(metric => {
+      const provider = metric.tags.provider;
+      if (!provider) return;
+
+      if (!providers.has(provider)) {
+        providers.set(provider, { requests: 0, tokens: 0, cost: 0 });
+      }
+
+      const data = providers.get(provider)!;
+      
+      switch (metric.name) {
+        case 'ai_request_latency':
+          data.requests += 1;
+          break;
+        case 'ai_token_usage':
+          data.tokens += metric.value;
+          break;
+        case 'ai_request_cost':
+          data.cost += metric.value;
+          break;
+      }
+    });
+
+    return Object.fromEntries(providers);
+  }
+
+  private sendToExternalServices(metric: MetricData) {
+    // Send to various monitoring services
+    if (process.env.DATADOG_API_KEY) {
+      this.sendToDataDog(metric);
+    }
+    
+    if (process.env.NEW_RELIC_LICENSE_KEY) {
+      this.sendToNewRelic(metric);
+    }
+    
+    if (process.env.CUSTOM_ANALYTICS_ENDPOINT) {
+      this.sendToCustomAnalytics(metric);
+    }
+  }
+
+  private async sendToDataDog(metric: MetricData) {
+    // DataDog implementation
+    try {
+      const response = await fetch('https://api.datadoghq.com/api/v1/series', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'DD-API-KEY': process.env.DATADOG_API_KEY!,
+        },
+        body: JSON.stringify({
+          series: [{
+            metric: `aisdk.${metric.name}`,
+            points: [[metric.timestamp / 1000, metric.value]],
+            tags: Object.entries(metric.tags).map(([k, v]) => `${k}:${v}`),
+          }],
+        }),
+      });
+      
+      if (!response.ok) {
+        console.error('Failed to send metric to DataDog:', response.statusText);
+      }
+    } catch (error) {
+      console.error('DataDog metric send error:', error);
+    }
+  }
+
+  private async sendToNewRelic(metric: MetricData) {
+    // New Relic implementation
+    try {
+      const response = await fetch('https://metric-api.newrelic.com/metric/v1', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+          'Api-Key': process.env.NEW_RELIC_LICENSE_KEY!,
+        },
+        body: JSON.stringify([{
+          metrics: [{
+            name: `aisdk.${metric.name}`,
+            type: 'gauge',
+            value: metric.value,
+            timestamp: metric.timestamp,
+            attributes: metric.tags,
+          }],
+        }]),
+      });
+      
+      if (!response.ok) {
+        console.error('Failed to send metric to New Relic:', response.statusText);
+      }
+    } catch (error) {
+      console.error('New Relic metric send error:', error);
+    }
+  }
+
+  private async sendToCustomAnalytics(metric: MetricData) {
+    // Custom analytics endpoint
+    try {
+      await fetch(process.env.CUSTOM_ANALYTICS_ENDPOINT!, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(metric),
+      });
+    } catch (error) {
+      console.error('Custom analytics send error:', error);
+    }
+  }
+}
+
+// Singleton instance
+export const monitor = AISDKMonitor.getInstance();
+```
+
+#### AI SDK Integration Middleware
+
+```typescript
+// lib/monitoring/middleware.ts
+import { streamText, generateText } from 'ai';
+import { monitor, PerformanceMetrics } from './core';
+import { calculateCost } from './cost-calculator';
+
+export function withMonitoring<T extends Function>(fn: T, context: string): T {
+  return (async (...args: any[]) => {
+    const requestId = generateRequestId();
+    const startTime = performance.now();
+    
+    try {
+      const result = await fn(...args);
+      
+      // Extract metrics from result
+      const endTime = performance.now();
+      const latency = endTime - startTime;
+      
+      const metrics: PerformanceMetrics = {
+        latency,
+        tokens: extractTokenUsage(result),
+        cost: calculateCost(extractTokenUsage(result), extractModelInfo(result)),
+        provider: extractProvider(result) || 'unknown',
+        model: extractModel(result) || 'unknown',
+        success: true,
+      };
+      
+      monitor.recordPerformance(requestId, metrics);
+      
+      return result;
+      
+    } catch (error) {
+      const endTime = performance.now();
+      const latency = endTime - startTime;
+      
+      const metrics: PerformanceMetrics = {
+        latency,
+        tokens: { input: 0, output: 0, total: 0 },
+        cost: 0,
+        provider: 'unknown',
+        model: 'unknown',
+        success: false,
+        errorType: error.constructor.name,
+      };
+      
+      monitor.recordPerformance(requestId, metrics);
+      
+      throw error;
+    }
+  }) as T;
+}
+
+// Enhanced streaming with monitoring
+export const monitoredStreamText = withMonitoring(streamText, 'stream_text');
+export const monitoredGenerateText = withMonitoring(generateText, 'generate_text');
+
+function generateRequestId(): string {
+  return `req_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+}
+
+function extractTokenUsage(result: any) {
+  if (result?.usage) {
+    return {
+      input: result.usage.promptTokens || 0,
+      output: result.usage.completionTokens || 0,
+      total: result.usage.totalTokens || 0,
+    };
+  }
+  return { input: 0, output: 0, total: 0 };
+}
+
+function extractProvider(result: any): string | null {
+  // Extract provider from model configuration
+  if (result?.model?._provider) {
+    return result.model._provider;
+  }
+  return null;
+}
+
+function extractModel(result: any): string | null {
+  if (result?.model?.modelId) {
+    return result.model.modelId;
+  }
+  return null;
+}
+
+function extractModelInfo(result: any) {
+  return {
+    provider: extractProvider(result),
+    model: extractModel(result),
+  };
+}
+```
+
+#### Cost Calculation System
+
+```typescript
+// lib/monitoring/cost-calculator.ts
+interface ProviderPricing {
+  [model: string]: {
+    input: number;  // Cost per 1K input tokens
+    output: number; // Cost per 1K output tokens
+  };
+}
+
+const PROVIDER_PRICING: Record<string, ProviderPricing> = {
+  anthropic: {
+    'claude-3-haiku-20240307': { input: 0.00025, output: 0.00125 },
+    'claude-3-sonnet-20240229': { input: 0.003, output: 0.015 },
+    'claude-3-opus-20240229': { input: 0.015, output: 0.075 },
+    'claude-3-5-sonnet-20241022': { input: 0.003, output: 0.015 },
+  },
+  openai: {
+    'gpt-3.5-turbo': { input: 0.0015, output: 0.002 },
+    'gpt-4': { input: 0.03, output: 0.06 },
+    'gpt-4-turbo': { input: 0.01, output: 0.03 },
+    'gpt-4o': { input: 0.005, output: 0.015 },
+    'o1-preview': { input: 0.015, output: 0.06 },
+    'o1-mini': { input: 0.003, output: 0.012 },
+  },
+  google: {
+    'gemini-pro': { input: 0.0005, output: 0.0015 },
+    'gemini-pro-vision': { input: 0.0005, output: 0.0015 },
+  },
+  cohere: {
+    'command': { input: 0.0015, output: 0.002 },
+    'command-light': { input: 0.0003, output: 0.0006 },
+  },
+};
+
+export function calculateCost(
+  tokens: { input: number; output: number; total: number },
+  modelInfo: { provider: string | null; model: string | null }
+): number {
+  if (!modelInfo.provider || !modelInfo.model) {
+    return 0;
+  }
+
+  const pricing = PROVIDER_PRICING[modelInfo.provider]?.[modelInfo.model];
+  if (!pricing) {
+    console.warn(`No pricing data for ${modelInfo.provider}:${modelInfo.model}`);
+    return 0;
+  }
+
+  const inputCost = (tokens.input / 1000) * pricing.input;
+  const outputCost = (tokens.output / 1000) * pricing.output;
+  
+  return inputCost + outputCost;
+}
+
+export class CostTracker {
+  private static dailyCosts = new Map<string, number>();
+  private static monthlyCosts = new Map<string, number>();
+  
+  static recordCost(cost: number, provider: string, model: string) {
+    const today = new Date().toISOString().split('T')[0];
+    const month = today.substring(0, 7);
+    
+    // Daily tracking
+    const dailyKey = `${today}:${provider}:${model}`;
+    this.dailyCosts.set(dailyKey, (this.dailyCosts.get(dailyKey) || 0) + cost);
+    
+    // Monthly tracking
+    const monthlyKey = `${month}:${provider}:${model}`;
+    this.monthlyCosts.set(monthlyKey, (this.monthlyCosts.get(monthlyKey) || 0) + cost);
+    
+    // Check budget alerts
+    this.checkBudgetAlerts(cost, provider);
+  }
+  
+  static getDailyCost(date?: string): number {
+    const targetDate = date || new Date().toISOString().split('T')[0];
+    let total = 0;
+    
+    for (const [key, cost] of this.dailyCosts.entries()) {
+      if (key.startsWith(targetDate)) {
+        total += cost;
+      }
+    }
+    
+    return total;
+  }
+  
+  static getMonthlyCost(month?: string): number {
+    const targetMonth = month || new Date().toISOString().substring(0, 7);
+    let total = 0;
+    
+    for (const [key, cost] of this.monthlyCosts.entries()) {
+      if (key.startsWith(targetMonth)) {
+        total += cost;
+      }
+    }
+    
+    return total;
+  }
+  
+  static getProviderBreakdown(timeRange: 'daily' | 'monthly' = 'daily') {
+    const costs = timeRange === 'daily' ? this.dailyCosts : this.monthlyCosts;
+    const breakdown = new Map<string, number>();
+    
+    for (const [key, cost] of costs.entries()) {
+      const provider = key.split(':')[1];
+      breakdown.set(provider, (breakdown.get(provider) || 0) + cost);
+    }
+    
+    return Object.fromEntries(breakdown);
+  }
+  
+  private static checkBudgetAlerts(cost: number, provider: string) {
+    const dailyBudget = parseFloat(process.env.DAILY_AI_BUDGET || '50');
+    const monthlyBudget = parseFloat(process.env.MONTHLY_AI_BUDGET || '1000');
+    
+    const dailyCost = this.getDailyCost();
+    const monthlyCost = this.getMonthlyCost();
+    
+    if (dailyCost > dailyBudget * 0.9) {
+      this.sendBudgetAlert('daily', dailyCost, dailyBudget);
+    }
+    
+    if (monthlyCost > monthlyBudget * 0.9) {
+      this.sendBudgetAlert('monthly', monthlyCost, monthlyBudget);
+    }
+  }
+  
+  private static sendBudgetAlert(period: string, current: number, budget: number) {
+    const alert = {
+      type: 'budget_alert',
+      period,
+      current_cost: current,
+      budget,
+      utilization: current / budget,
+      timestamp: new Date().toISOString(),
+    };
+    
+    // Send alert (email, Slack, etc.)
+    console.warn('BUDGET ALERT:', alert);
+    
+    // You could integrate with notification services here
+    if (process.env.SLACK_WEBHOOK_URL) {
+      this.sendSlackAlert(alert);
+    }
+  }
+  
+  private static async sendSlackAlert(alert: any) {
+    try {
+      await fetch(process.env.SLACK_WEBHOOK_URL!, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          text: `🚨 AI Budget Alert: ${alert.period} spending (${alert.current_cost.toFixed(2)}) is at ${(alert.utilization * 100).toFixed(1)}% of budget ($${alert.budget})`,
+        }),
+      });
+    } catch (error) {
+      console.error('Failed to send Slack alert:', error);
+    }
+  }
+}
+```
+
+### Real-Time Dashboard Implementation
+
+```typescript
+// app/api/monitoring/dashboard/route.ts
+export async function GET(req: Request) {
+  const monitor = AISDKMonitor.getInstance();
+  const { searchParams } = new URL(req.url);
+  const timeRange = searchParams.get('range') || '1h';
+  
+  const endTime = new Date();
+  const startTime = new Date(endTime.getTime() - parseTimeRange(timeRange));
+  
+  const analytics = monitor.getPerformanceAnalytics({ start: startTime, end: endTime });
+  const costBreakdown = CostTracker.getProviderBreakdown('daily');
+  
+  const dashboard = {
+    timeRange,
+    overview: {
+      totalRequests: analytics.totalRequests,
+      averageLatency: Math.round(analytics.averageLatency),
+      totalTokens: analytics.totalTokens,
+      totalCost: analytics.totalCost.toFixed(4),
+      errorRate: (analytics.errorRate * 100).toFixed(2) + '%',
+    },
+    providers: analytics.providerBreakdown,
+    costs: {
+      daily: CostTracker.getDailyCost().toFixed(4),
+      monthly: CostTracker.getMonthlyCost().toFixed(4),
+      breakdown: costBreakdown,
+    },
+    alerts: await getActiveAlerts(),
+  };
+  
+  return Response.json(dashboard);
+}
+
+function parseTimeRange(range: string): number {
+  const unit = range.slice(-1);
+  const value = parseInt(range.slice(0, -1));
+  
+  switch (unit) {
+    case 'h': return value * 60 * 60 * 1000;
+    case 'd': return value * 24 * 60 * 60 * 1000;
+    case 'w': return value * 7 * 24 * 60 * 60 * 1000;
+    default: return 60 * 60 * 1000; // Default to 1 hour
+  }
+}
+
+async function getActiveAlerts() {
+  // Return active alerts from your alerting system
+  return [];
+}
+```
+
+#### React Dashboard Component
+
+```typescript
+// components/monitoring-dashboard.tsx
+'use client';
+
+import { useEffect, useState } from 'react';
+import { LineChart, Line, BarChart, Bar, PieChart, Pie, Cell, ResponsiveContainer, XAxis, YAxis, Tooltip, Legend } from 'recharts';
+
+interface DashboardData {
+  overview: {
+    totalRequests: number;
+    averageLatency: number;
+    totalTokens: number;
+    totalCost: string;
+    errorRate: string;
+  };
+  providers: Record<string, any>;
+  costs: {
+    daily: string;
+    monthly: string;
+    breakdown: Record<string, number>;
+  };
+}
+
+export default function MonitoringDashboard() {
+  const [data, setData] = useState<DashboardData | null>(null);
+  const [timeRange, setTimeRange] = useState('1h');
+  const [loading, setLoading] = useState(true);
+
+  useEffect(() => {
+    fetchDashboardData();
+    const interval = setInterval(fetchDashboardData, 30000); // Refresh every 30 seconds
+    return () => clearInterval(interval);
+  }, [timeRange]);
+
+  const fetchDashboardData = async () => {
+    try {
+      const response = await fetch(`/api/monitoring/dashboard?range=${timeRange}`);
+      const dashboardData = await response.json();
+      setData(dashboardData);
+    } catch (error) {
+      console.error('Failed to fetch dashboard data:', error);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  if (loading) {
+    return <div className="flex justify-center items-center h-64">Loading dashboard...</div>;
+  }
+
+  if (!data) {
+    return <div className="text-center text-red-500">Failed to load dashboard data</div>;
+  }
+
+  const providerColors = ['#8884d8', '#82ca9d', '#ffc658', '#ff7c7c', '#8dd1e1'];
+  const costBreakdownData = Object.entries(data.costs.breakdown).map(([provider, cost]) => ({
+    provider,
+    cost: parseFloat(cost.toFixed(4)),
+  }));
+
+  return (
+    <div className="p-6 max-w-7xl mx-auto">
+      <div className="mb-6 flex justify-between items-center">
+        <h1 className="text-3xl font-bold">AI SDK Monitoring Dashboard</h1>
+        <select 
+          value={timeRange} 
+          onChange={(e) => setTimeRange(e.target.value)}
+          className="border rounded px-3 py-2"
+        >
+          <option value="1h">Last Hour</option>
+          <option value="6h">Last 6 Hours</option>
+          <option value="1d">Last Day</option>
+          <option value="7d">Last Week</option>
+        </select>
+      </div>
+
+      {/* Overview Cards */}
+      <div className="grid grid-cols-1 md:grid-cols-5 gap-6 mb-8">
+        <div className="bg-white p-4 rounded-lg shadow">
+          <h3 className="text-sm font-medium text-gray-500">Total Requests</h3>
+          <p className="text-2xl font-bold">{data.overview.totalRequests.toLocaleString()}</p>
+        </div>
+        <div className="bg-white p-4 rounded-lg shadow">
+          <h3 className="text-sm font-medium text-gray-500">Avg Latency</h3>
+          <p className="text-2xl font-bold">{data.overview.averageLatency}ms</p>
+        </div>
+        <div className="bg-white p-4 rounded-lg shadow">
+          <h3 className="text-sm font-medium text-gray-500">Total Tokens</h3>
+          <p className="text-2xl font-bold">{data.overview.totalTokens.toLocaleString()}</p>
+        </div>
+        <div className="bg-white p-4 rounded-lg shadow">
+          <h3 className="text-sm font-medium text-gray-500">Total Cost</h3>
+          <p className="text-2xl font-bold">${data.overview.totalCost}</p>
+        </div>
+        <div className="bg-white p-4 rounded-lg shadow">
+          <h3 className="text-sm font-medium text-gray-500">Error Rate</h3>
+          <p className="text-2xl font-bold text-red-500">{data.overview.errorRate}</p>
+        </div>
+      </div>
+
+      {/* Charts */}
+      <div className="grid grid-cols-1 lg:grid-cols-2 gap-8">
+        {/* Cost Breakdown */}
+        <div className="bg-white p-6 rounded-lg shadow">
+          <h3 className="text-lg font-semibold mb-4">Cost Breakdown by Provider</h3>
+          <ResponsiveContainer width="100%" height={300}>
+            <PieChart>
+              <Pie
+                data={costBreakdownData}
+                dataKey="cost"
+                nameKey="provider"
+                cx="50%"
+                cy="50%"
+                outerRadius={100}
+                label={({ provider, cost }) => `${provider}: $${cost}`}
+              >
+                {costBreakdownData.map((entry, index) => (
+                  <Cell key={`cell-${index}`} fill={providerColors[index % providerColors.length]} />
+                ))}
+              </Pie>
+              <Tooltip />
+            </PieChart>
+          </ResponsiveContainer>
+        </div>
+
+        {/* Provider Usage */}
+        <div className="bg-white p-6 rounded-lg shadow">
+          <h3 className="text-lg font-semibold mb-4">Provider Usage</h3>
+          <ResponsiveContainer width="100%" height={300}>
+            <BarChart data={Object.entries(data.providers).map(([provider, stats]) => ({
+              provider,
+              requests: stats.requests,
+              tokens: stats.tokens,
+            }))}>
+              <XAxis dataKey="provider" />
+              <YAxis />
+              <Tooltip />
+              <Legend />
+              <Bar dataKey="requests" fill="#8884d8" name="Requests" />
+              <Bar dataKey="tokens" fill="#82ca9d" name="Tokens (thousands)" />
+            </BarChart>
+          </ResponsiveContainer>
+        </div>
+      </div>
+
+      {/* Cost Summary */}
+      <div className="mt-8 bg-white p-6 rounded-lg shadow">
+        <h3 className="text-lg font-semibold mb-4">Cost Summary</h3>
+        <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
+          <div>
+            <p className="text-sm text-gray-500">Daily Cost</p>
+            <p className="text-xl font-bold">${data.costs.daily}</p>
+          </div>
+          <div>
+            <p className="text-sm text-gray-500">Monthly Cost</p>
+            <p className="text-xl font-bold">${data.costs.monthly}</p>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}
+```
+
+### Integration with Existing Code
+
+- **API Routes**: Wrap with monitoring middleware automatically
+- **Streaming**: Built-in performance tracking for streaming responses
+- **Error Handling**: Automatic error classification and reporting
+- **Cost Tracking**: Real-time cost calculation across all providers
+- **Alerting**: Budget and performance threshold alerting
+- **Dashboard**: Real-time monitoring dashboard with visualizations
+- **External Services**: Integration with DataDog, New Relic, custom analytics
+
+Focus on building comprehensive monitoring that provides actionable insights for AI SDK applications while maintaining low overhead and high accuracy.
\ No newline at end of file
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-provider-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-provider-setup.md
new file mode 100644
index 0000000..0d83374
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-provider-setup.md
@@ -0,0 +1,169 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Configure AI providers and multi-provider setup
+argument-hint: "[single|multi|fallback] [anthropic|openai|google|cohere]"
+---
+
+## Set up AI Provider Configuration
+
+Configure robust AI provider setup with the Vercel AI SDK: $ARGUMENTS
+
+### Current Configuration Analysis
+
+Existing provider setup: !`grep -r "@ai-sdk/" . --include="*.ts" --include="*.tsx" | head -5`
+
+Environment variables: !`grep -r "API_KEY\|_KEY" .env* 2>/dev/null | head -5 || echo "No API keys found in .env files"`
+
+Provider imports: !`grep -r "from '@ai-sdk/" . --include="*.ts" | head -10`
+
+### Configuration Strategy
+
+**Single Provider**: Focus on one provider with optimal configuration
+**Multi Provider**: Set up multiple providers with consistent interface
+**Fallback**: Implement automatic failover between providers
+
+### Your Task
+
+1. **Analyze current provider setup** and identify improvements needed
+2. **Design provider architecture** with proper abstraction layers
+3. **Implement configuration management** with environment handling
+4. **Set up provider fallback logic** for reliability
+5. **Add usage tracking and cost monitoring** for optimization
+6. **Create provider health checks** for monitoring
+7. **Implement proper error handling** and recovery
+8. **Add comprehensive testing** for all providers
+
+### Implementation Requirements
+
+#### Environment Configuration
+
+- Secure API key management
+- Environment-specific configurations
+- Provider availability detection
+- Default provider selection
+- Feature flag support for provider switching
+
+#### Provider Abstraction
+
+- Unified interface across all providers
+- Model capability mapping
+- Feature detection and adaptation
+- Consistent error handling
+- Performance monitoring integration
+
+#### Fallback and Reliability
+
+- Automatic provider failover
+- Health check implementation
+- Circuit breaker patterns
+- Retry logic with exponential backoff
+- Graceful degradation strategies
+
+### Expected Deliverables
+
+1. **Provider configuration system** with environment management
+2. **Multi-provider client wrapper** with unified interface
+3. **Fallback and health monitoring** implementation
+4. **Usage tracking and analytics** system
+5. **Cost optimization utilities** for model selection
+6. **Testing suite** covering all provider scenarios
+7. **Documentation** with setup guides and best practices
+
+### Provider-Specific Optimizations
+
+#### Anthropic Configuration
+
+- Claude model selection (Haiku, Sonnet, Opus, Claude 4)
+- Extended thinking capabilities setup
+- Prompt caching configuration
+- Tool use optimization
+- Context window management
+
+#### OpenAI Configuration
+
+- Model selection (GPT-3.5, GPT-4, GPT-4o, O1)
+- Responses API integration
+- Function calling optimization
+- Structured output configuration
+- Built-in tool integration (web search)
+
+#### Google Configuration
+
+- Gemini model variants setup
+- Search grounding capabilities
+- Multimodal processing optimization
+- Safety settings configuration
+- Thinking mode integration
+
+#### Cohere Configuration
+
+- Command model setup
+- RAG optimization
+- Embedding integration
+- Multilingual support
+- Custom model fine-tuning
+
+### Cost Management
+
+#### Usage Tracking
+
+- Token usage monitoring across providers
+- Cost calculation and reporting
+- Budget limits and alerting
+- Usage pattern analysis
+- Optimization recommendations
+
+#### Model Selection
+
+- Intelligent model routing based on task complexity
+- Cost-performance optimization
+- Usage-based provider selection
+- Dynamic model switching
+- A/B testing for provider performance
+
+### Security and Compliance
+
+#### API Key Management
+
+- Secure key storage and rotation
+- Environment variable validation
+- Access control and permissions
+- Audit logging for API usage
+- Compliance reporting
+
+#### Data Privacy
+
+- Request/response logging controls
+- Data retention policies
+- Regional data handling
+- Privacy-preserving configurations
+- Compliance monitoring
+
+### Monitoring and Observability
+
+#### Health Monitoring
+
+- Provider availability checks
+- Latency and performance monitoring
+- Error rate tracking
+- Success rate analysis
+- Capacity utilization monitoring
+
+#### Analytics and Reporting
+
+- Usage dashboards and reports
+- Cost analysis and forecasting
+- Performance benchmarking
+- User behavior analysis
+- Provider comparison metrics
+
+### Testing Strategy
+
+- Provider connectivity tests
+- Failover scenario testing
+- Performance and load testing
+- Cost calculation validation
+- Security and compliance testing
+- Integration testing with applications
+
+Focus on building a robust, cost-effective, and reliable multi-provider architecture that ensures high availability and optimal performance while maintaining security and compliance standards.
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-rag-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-rag-setup.md
new file mode 100644
index 0000000..5003af9
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-rag-setup.md
@@ -0,0 +1,252 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Set up RAG (Retrieval-Augmented Generation) system
+argument-hint: "[basic|advanced|conversational|agentic]"
+---
+
+## Set up RAG (Retrieval-Augmented Generation) System
+
+Create a comprehensive RAG implementation with embeddings, vector storage, and retrieval: $ARGUMENTS
+
+### Current Project Analysis
+
+Existing database setup: !`find . -name "*schema*" -o -name "*migration*" -o -name "drizzle.config.*" | head -5`
+
+Vector database configuration: !`grep -r "vector\|embedding" . --include="*.ts" --include="*.sql" | head -5`
+
+AI SDK integration: !`grep -r "embed\|embedMany" . --include="*.ts" | head -5`
+
+### RAG Implementation Types
+
+**Basic RAG**: Simple query → retrieve → generate pipeline
+**Advanced RAG**: Multi-query, re-ranking, hybrid search, filtering
+**Conversational RAG**: Context-aware retrieval with chat history
+**Agentic RAG**: Tool-based retrieval with dynamic knowledge access
+
+### Your Task
+
+1. **Analyze current data infrastructure** and vector storage capabilities
+2. **Design embedding and chunking strategy** for optimal retrieval
+3. **Set up vector database** with proper indexing and search
+4. **Implement embedding pipeline** with batch processing
+5. **Create retrieval system** with similarity search and ranking
+6. **Build RAG generation pipeline** with context injection
+7. **Add evaluation metrics** for retrieval and generation quality
+8. **Implement comprehensive testing** for all RAG components
+
+### Implementation Requirements
+
+#### Data Processing Pipeline
+
+- Document ingestion and preprocessing
+- Intelligent chunking strategies (sentence, semantic, sliding window)
+- Metadata extraction and enrichment
+- Batch embedding generation with rate limiting
+- Deduplication and quality filtering
+
+#### Vector Storage and Search
+
+- Database setup (PostgreSQL + pgvector, Pinecone, Supabase, etc.)
+- Proper indexing (HNSW, IVFFlat) for performance
+- Similarity search with filtering and ranking
+- Hybrid search combining vector and text search
+- Metadata filtering and faceted search
+
+#### RAG Generation
+
+- Context selection and ranking
+- Prompt engineering for RAG scenarios
+- Context window management
+- Response grounding and source attribution
+- Quality control and relevance scoring
+
+### Expected Deliverables
+
+1. **Document processing pipeline** with chunking and embedding
+2. **Vector database setup** with optimized indexing
+3. **Retrieval system** with advanced search capabilities
+4. **RAG generation API** with streaming support
+5. **Evaluation framework** for quality measurement
+6. **Admin interface** for content management
+7. **Comprehensive documentation** and examples
+
+### Database Schema Design
+
+#### PostgreSQL with pgvector
+
+```sql
+-- Enable vector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Documents table
+CREATE TABLE documents (
+    id SERIAL PRIMARY KEY,
+    title VARCHAR(255),
+    content TEXT NOT NULL,
+    metadata JSONB,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW()
+);
+
+-- Chunks table
+CREATE TABLE document_chunks (
+    id SERIAL PRIMARY KEY,
+    document_id INTEGER REFERENCES documents(id) ON DELETE CASCADE,
+    content TEXT NOT NULL,
+    chunk_index INTEGER,
+    metadata JSONB,
+    embedding VECTOR(1536),
+    created_at TIMESTAMP DEFAULT NOW()
+);
+
+-- Indexes for performance
+CREATE INDEX ON document_chunks USING hnsw (embedding vector_cosine_ops);
+CREATE INDEX ON document_chunks (document_id);
+CREATE INDEX ON documents USING gin (metadata);
+```
+
+#### Drizzle ORM Schema
+
+```typescript
+export const documents = pgTable('documents', {
+  id: serial('id').primaryKey(),
+  title: varchar('title', { length: 255 }),
+  content: text('content').notNull(),
+  metadata: jsonb('metadata'),
+  createdAt: timestamp('created_at').defaultNow(),
+  updatedAt: timestamp('updated_at').defaultNow(),
+});
+
+export const documentChunks = pgTable(
+  'document_chunks',
+  {
+    id: serial('id').primaryKey(),
+    documentId: integer('document_id').references(() => documents.id, {
+      onDelete: 'cascade',
+    }),
+    content: text('content').notNull(),
+    chunkIndex: integer('chunk_index'),
+    metadata: jsonb('metadata'),
+    embedding: vector('embedding', { dimensions: 1536 }),
+    createdAt: timestamp('created_at').defaultNow(),
+  },
+  (table) => ({
+    embeddingIndex: index('embedding_idx').using(
+      'hnsw',
+      table.embedding.op('vector_cosine_ops'),
+    ),
+    documentIdIndex: index('document_id_idx').on(table.documentId),
+  }),
+);
+```
+
+### Embedding Strategy
+
+#### Chunking Algorithms
+
+- **Sentence-based**: Split on sentence boundaries for coherent chunks
+- **Semantic**: Use NLP models to identify semantic boundaries
+- **Sliding window**: Overlapping chunks to preserve context
+- **Recursive**: Hierarchical chunking for different granularities
+
+#### Model Selection
+
+- **OpenAI**: text-embedding-3-small/large for versatility
+- **Cohere**: embed-english-v3.0 for specialized domains
+- **Local models**: Sentence-transformers for privacy/cost
+- **Multilingual**: Support for multiple languages
+
+### Advanced RAG Patterns
+
+#### Multi-Query RAG
+
+```typescript
+async function multiQueryRAG(userQuery: string) {
+  // Generate multiple query variants
+  const queryVariants = await generateQueryVariants(userQuery);
+  
+  // Retrieve for each variant
+  const retrievalResults = await Promise.all(
+    queryVariants.map(query => retrieveDocuments(query))
+  );
+  
+  // Combine and re-rank results
+  const combinedResults = combineAndRerankResults(retrievalResults);
+  
+  return combinedResults;
+}
+```
+
+#### Conversational RAG
+
+```typescript
+async function conversationalRAG(messages: Message[], query: string) {
+  // Extract conversation context
+  const conversationContext = extractContext(messages);
+  
+  // Generate context-aware query
+  const contextualQuery = await generateContextualQuery(query, conversationContext);
+  
+  // Retrieve with conversation awareness
+  const documents = await retrieveWithContext(contextualQuery, conversationContext);
+  
+  return documents;
+}
+```
+
+### Quality Evaluation
+
+#### Retrieval Metrics
+
+- **Precision@K**: Relevant documents in top-K results
+- **Recall@K**: Coverage of relevant documents
+- **MRR**: Mean Reciprocal Rank of first relevant document
+- **NDCG**: Normalized Discounted Cumulative Gain
+
+#### Generation Metrics
+
+- **Faithfulness**: Response grounded in retrieved context
+- **Relevance**: Response relevance to user query
+- **Completeness**: Coverage of important information
+- **Coherence**: Logical flow and readability
+
+### Testing and Validation
+
+#### Unit Testing
+
+- Embedding generation accuracy
+- Chunking algorithm correctness
+- Similarity search precision
+- Database operations integrity
+
+#### Integration Testing
+
+- End-to-end RAG pipeline
+- Performance under load
+- Quality with various document types
+- Scalability testing
+
+#### Evaluation Testing
+
+- Golden dataset evaluation
+- A/B testing with different strategies
+- User feedback collection
+- Continuous quality monitoring
+
+### Performance Optimization
+
+#### Database Optimization
+
+- Proper indexing strategies (HNSW vs IVFFlat)
+- Connection pooling and caching
+- Query optimization and profiling
+- Horizontal scaling considerations
+
+#### Embedding Optimization
+
+- Batch processing for efficiency
+- Caching frequently used embeddings
+- Model quantization for speed
+- Parallel processing pipelines
+
+Focus on building a production-ready RAG system that provides accurate, relevant, and fast retrieval-augmented generation with proper evaluation and optimization strategies.
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-streaming-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-streaming-setup.md
new file mode 100644
index 0000000..0d65349
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-streaming-setup.md
@@ -0,0 +1,82 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Set up streaming AI responses with proper error handling
+argument-hint: "[text|object|chat|completion]"
+---
+
+## Set up AI Streaming Implementation
+
+Create a robust streaming AI implementation with the Vercel AI SDK for: $ARGUMENTS
+
+### Current Project Analysis
+
+Project structure: !`find . -type f -name "*.ts" -o -name "*.tsx" | grep -E "(api|components|lib)" | head -10`
+
+Existing AI SDK setup: !`grep -r "from 'ai'" . --include="*.ts" --include="*.tsx" | head -5`
+
+Package dependencies: !`cat package.json | jq '.dependencies | to_entries[] | select(.key | contains("ai")) | "\(.key): \(.value)"' -r 2>/dev/null || echo "No AI dependencies found"`
+
+### Streaming Type Analysis
+
+**Text Streaming**: Real-time text generation with token-by-token updates
+**Object Streaming**: Structured data streaming with partial object updates  
+**Chat Streaming**: Conversational interfaces with message history
+**Completion Streaming**: Single-turn completions with progressive updates
+
+### Your Task
+
+1. **Assess current streaming setup** and identify gaps
+2. **Implement the appropriate streaming pattern** based on the specified type
+3. **Create robust error handling** for stream interruptions and failures
+4. **Add proper loading states** and user feedback
+5. **Implement stream cancellation** for better UX
+6. **Set up proper TypeScript types** for streaming responses
+7. **Add performance optimizations** (chunking, backpressure handling)
+8. **Include comprehensive testing** for edge cases
+
+### Implementation Requirements
+
+#### Server-Side Streaming
+
+- Proper route handler setup with `maxDuration`
+- Model configuration with appropriate parameters
+- Stream response formatting with `toUIMessageStreamResponse()` or `toTextStreamResponse()`
+- Abort signal handling for stream cancellation
+- Error boundaries and fallback responses
+
+#### Client-Side Streaming
+
+- React hooks for stream management (`useChat`, `useCompletion`, `useObject`)
+- Progressive UI updates with optimistic rendering
+- Loading states and stream status indicators
+- Error handling with retry mechanisms
+- Stream interruption and cancellation
+
+#### Performance Considerations
+
+- Appropriate chunk sizing for smooth updates
+- Memory management for long streams
+- Connection pooling and reuse
+- Backpressure handling for slow consumers
+- Optimization for mobile and slow connections
+
+### Expected Deliverables
+
+1. **Streaming API route** with proper configuration
+2. **React streaming component** with modern patterns
+3. **TypeScript interfaces** for streaming data
+4. **Error handling implementation** with recovery
+5. **Performance optimizations** for production
+6. **Testing suite** for streaming functionality
+7. **Documentation** with usage examples
+
+### Testing Requirements
+
+- Stream start and completion scenarios
+- Network interruption and recovery
+- Concurrent stream handling
+- Error conditions and fallbacks
+- Performance under load
+- Mobile and slow connection testing
+
+Focus on building a production-ready streaming implementation that provides excellent user experience with proper error handling and performance optimization.
diff --git a/tooling/vercel-ai-sdk/.claude/commands/ai-tools-setup.md b/tooling/vercel-ai-sdk/.claude/commands/ai-tools-setup.md
new file mode 100644
index 0000000..9e33b6f
--- /dev/null
+++ b/tooling/vercel-ai-sdk/.claude/commands/ai-tools-setup.md
@@ -0,0 +1,137 @@
+---
+allowed-tools: Read, Write, Edit, MultiEdit, Bash
+description: Create AI tools and function calling capabilities
+argument-hint: "[simple|database|api|multimodal|agent]"
+---
+
+## Set up AI Tools and Function Calling
+
+Create comprehensive AI tool integrations with the Vercel AI SDK for: $ARGUMENTS
+
+### Current Project Analysis
+
+Existing tool implementations: !`grep -r "import.*tool" . --include="*.ts" --include="*.tsx" | head -5`
+
+API integrations: !`grep -r "fetch\|axios" . --include="*.ts" | head -5`
+
+Database setup: !`find . -name "*schema*" -o -name "*db*" -o -name "*database*" | grep -v node_modules | head -5`
+
+### Tool Type Requirements
+
+**Simple Tools**: Basic utility functions (calculator, formatter, validator)
+**Database Tools**: Safe database queries, data retrieval, analytics
+**API Tools**: External service integrations, webhooks, data fetching
+**Multimodal Tools**: Image processing, document analysis, file handling
+**Agent Tools**: Complex workflows, multi-step operations, decision making
+
+### Your Task
+
+1. **Analyze the project needs** and identify appropriate tool types
+2. **Design tool schemas** with proper Zod validation
+3. **Implement secure execution logic** with error handling
+4. **Set up proper authentication** and authorization
+5. **Add comprehensive input validation** and sanitization
+6. **Implement rate limiting** and usage monitoring
+7. **Create tool testing suite** for reliability
+8. **Document tool usage** and examples
+
+### Implementation Guidelines
+
+#### Tool Definition Patterns
+
+```typescript
+// Basic tool structure
+const toolName = tool({
+  description: 'Clear description of what the tool does',
+  inputSchema: z.object({
+    param: z.string().describe('Parameter description'),
+  }),
+  execute: async ({ param }) => {
+    // Implementation with proper error handling
+    try {
+      const result = await performOperation(param);
+      return { success: true, data: result };
+    } catch (error) {
+      return { success: false, error: error.message };
+    }
+  },
+});
+```
+
+#### Security Considerations
+
+- Input validation and sanitization
+- Authentication and authorization checks
+- Rate limiting and abuse prevention
+- Secure API key management
+- Output filtering and validation
+- Audit logging for sensitive operations
+
+#### Error Handling
+
+- Graceful failure modes
+- Informative error messages
+- Retry mechanisms for transient failures
+- Fallback strategies
+- Circuit breaker patterns
+- Monitoring and alerting
+
+### Expected Deliverables
+
+1. **Tool definitions** with proper schemas and validation
+2. **Execution implementations** with robust error handling
+3. **Agent integration** with multi-step capabilities
+4. **Security middleware** for authentication and rate limiting
+5. **Testing suite** covering all tool scenarios
+6. **Usage analytics** and monitoring
+7. **Documentation** with examples and best practices
+
+### Tool Categories to Implement
+
+#### Data & Analytics Tools
+
+- Database query execution
+- Data aggregation and analysis
+- Report generation
+- Chart and visualization creation
+
+#### External Integration Tools
+
+- REST API clients
+- Webhook handlers
+- File processing and storage
+- Email and notification services
+
+#### Utility Tools
+
+- Text processing and formatting
+- Mathematical calculations
+- Data validation and transformation
+- Code generation and analysis
+
+#### Advanced Agent Tools
+
+- Multi-step workflow orchestration
+- Decision tree navigation
+- Dynamic tool selection
+- Context-aware processing
+
+### Testing Requirements
+
+- Unit tests for each tool execution path
+- Integration tests with external services
+- Security tests for input validation
+- Performance tests under load
+- Error scenario testing
+- End-to-end agent workflow tests
+
+### Monitoring and Observability
+
+- Tool usage metrics and analytics
+- Performance monitoring and latency tracking
+- Error rate monitoring and alerting
+- Cost tracking for external API usage
+- Security audit logging
+- User behavior analysis
+
+Focus on building secure, reliable, and well-tested tool integrations that enhance AI capabilities while maintaining proper security and monitoring practices.