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Hindsight AI SDK - Personal Chef

Complete Application

This is a complete, runnable application demonstrating Hindsight integration. View source on GitHub →

A personal food assistant demonstrating three key Hindsight integrations using the Vercel AI SDK v6.

Architecture: Single Bank with User Tags​

This demo uses a single Hindsight bank (taste-ai) for all users, with each user's data tagged using user:${username}.

// All users share the same bank
const BANK_ID = 'taste-ai';

// Each memory is tagged with the user
await hindsightTools.retain.execute({
  bankId: BANK_ID,
  content: userData,
  tags: [`user:${username}`],
});

This architecture enables:

  • Per-user queries: Filter by user:alice to get personalized results
  • Aggregated insights: Query across all users to find popular recipes or common dietary patterns
  • Simplified management: One bank to maintain instead of per-user banks

Three Hindsight Integrations​

1. Meal Suggestions with Memory Recall & Reflection​

Uses recall and reflect tools with AI SDK's agent-based approach to gather personalized context.

const contextResult = await generateText({
  model: llmModel,
  tools: {
    recall: hindsightTools.recall,
    reflect: hindsightTools.reflect,
  },
  toolChoice: 'auto',
  prompt: `You are gathering context for personalized ${mealType} recipe suggestions.

Use the recall tool to search for the user's food preferences, dislikes, and recent meals.
Then use the reflect tool to analyze their dietary patterns and restrictions.

After gathering context, summarize their preferences and recent eating patterns.`,
});

The AI agent autonomously:

  • Searches memory for cuisine preferences and dietary restrictions
  • Analyzes recent protein consumption for variety
  • Identifies foods to avoid

2. Goal Progress Tracking with Mental Models​

Uses mental models to automatically maintain updated insights about user progress.

// Create a mental model that auto-refreshes after new meals
await hindsightTools.createMentalModel.execute({
  bankId: BANK_ID,
  mentalModelId: getMentalModelId(username, 'goals'),
  name: `${username}'s Goal Progress`,
  sourceQuery: `Analyze ${username}'s dietary goals and eating patterns.
    Describe their progress towards their stated goals (weight loss, muscle gain, etc.).`,
  tags: [`user:${username}`],
  autoRefresh: true, // Refreshes automatically after consolidation
});

// Query the mental model for current insights
const result = await hindsightTools.queryMentalModel.execute({
  bankId: BANK_ID,
  mentalModelId: mentalModelId,
});

Mental models automatically:

  • Track progress towards dietary goals
  • Update after each new meal is logged
  • Provide fresh insights without manual refresh

3. Language Enforcement with Directives​

Uses directives to ensure all responses match user's language preference.

await hindsightClient.createDirective(BANK_ID, {
  name: `${username}'s Language Preference`,
  content: `Always respond in ${language}. All suggestions must be in ${language}.`,
  priority: 100,
  tags: [`user:${username}`, 'directive:language'],
});

Directives are automatically injected when mental models generate insights, ensuring consistent language across all interactions.

Running the Demo​

npm install
npm run dev

Requirements:

  • Hindsight server running at http://localhost:8888 (or set HINDSIGHT_URL)
  • Node.js 18+

Learn More​