path: root/tooling/vercel-ai-sdk/.claude/commands/ai-experimental-setup.md

---
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.