Supermemory Use Cases & Examples

Concrete examples showing how to use Supermemory for common AI application patterns.

Table of Contents

1. Personalized Chatbot
2. Long-Term Task Assistant
3. Document Knowledge Base
4. Customer Support AI
5. Code Review Assistant
6. Learning Companion
7. Multi-Tenant SaaS Application
8. Research Assistant

---

1. Personalized Chatbot

Build a chatbot that remembers user preferences and past conversations.

Implementation (TypeScript + Vercel AI SDK)

import { Supermemory } from 'supermemory';
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';

const memory = new Supermemory();

async function chat(userId: string, message: string) {
  // 1. Retrieve user context
  const response = await memory.profile({
    containerTag: userId,
    q: message,
    threshold: 0.6
  });

  // 2. Build system prompt with personalization
  const staticFacts = response.profile.static.map(f => `- ${f}`).join('\n');
  const dynamicFacts = response.profile.dynamic.map(f => `- ${f}`).join('\n');

  const systemPrompt = `
You are a helpful assistant with perfect memory.

User Profile:
${staticFacts}

Recent Context:
${dynamicFacts}

Use this context to provide personalized, contextually aware responses.
  `.trim();

  // 3. Generate response
  const { text } = await generateText({
    model: openai('gpt-4'),
    system: systemPrompt,
    prompt: message
  });

  // 4. Store the interaction
  await memory.add({
    content: `User: ${message}\nAssistant: ${text}`,
    containerTag: userId,
    metadata: {
      timestamp: new Date().toISOString(),
      messageId: crypto.randomUUID(),
      type: 'conversation'
    }
  });

  return text;
}

// Usage
const response = await chat('user_123', 'What did I tell you about my preferences?');
console.log(response); // Uses stored context to answer accurately

Python Version

from supermemory import Supermemory
from openai import OpenAI
import datetime

memory = Supermemory()
openai_client = OpenAI()

def chat(user_id: str, message: str) -> str:
    # 1. Retrieve context
    response = memory.profile(
        container_tag=user_id,
        q=message,
        threshold=0.6
    )

    # 2. Build system prompt
    static_facts = "\n".join(f"- {fact}" for fact in response['profile']['static'])
    dynamic_facts = "\n".join(f"- {fact}" for fact in response['profile']['dynamic'])

    system_prompt = f"""
You are a helpful assistant with perfect memory.

User Profile:
{static_facts}

Recent Context:
{dynamic_facts}

Use this context to provide personalized responses.
    """.strip()

    # 3. Generate response
    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": message}
        ]
    )
    text = response.choices[0].message.content

    # 4. Store interaction
    memory.add(
        content=f"User: {message}\nAssistant: {text}",
        container_tag=user_id,
        metadata={
            "timestamp": datetime.datetime.now().isoformat(),
            "type": "conversation"
        }
    )

    return text

Key Benefits

• Personalized responses based on user history
• Remembers preferences across sessions
• Reduces repetitive questions
• Builds trust through consistency

---

2. Long-Term Task Assistant

AI assistant that tracks ongoing projects and tasks over weeks/months.

Implementation

import { Supermemory } from 'supermemory';
import { anthropic } from '@ai-sdk/anthropic';
import { generateText } from 'ai';

const memory = new Supermemory();

interface Task {
  id: string;
  title: string;
  status: 'todo' | 'in_progress' | 'done';
  priority: 'low' | 'medium' | 'high';
}

async function taskAssistant(userId: string, query: string) {
  // Get task-related context
  const response = await memory.profile({
    containerTag: `${userId}_tasks`,
    q: query,
    threshold: 0.5
  });

  // Build context from profile
  const tasks = response.profile.dynamic.map(f => `- ${f}`).join('\n');

  // Generate intelligent response
  const { text } = await generateText({
    model: anthropic('claude-3-5-sonnet-20241022'),
    system: `
You are a task management assistant with perfect memory of the user's projects.

Active Tasks and Context:
${tasks}

Help the user track, prioritize, and complete their tasks.
    `,
    prompt: query
  });

  return text;
}

async function addTask(userId: string, task: Task) {
  await memory.add({
    content: `Task: ${task.title} (Status: ${task.status}, Priority: ${task.priority})`,
    containerTag: `${userId}_tasks`,
    customId: task.id,
    metadata: {
      status: task.status,
      priority: task.priority,
      createdAt: new Date().toISOString()
    }
  });
}

async function updateTask(userId: string, taskId: string, status: Task['status']) {
  // Add update (Supermemory will create relationship)
  await memory.add({
    content: `Task ${taskId} updated to status: ${status}`,
    containerTag: `${userId}_tasks`,
    metadata: {
      taskId,
      status,
      updatedAt: new Date().toISOString(),
      type: 'update'
    }
  });
}

// Usage
await addTask('user_123', {
  id: 'task_1',
  title: 'Implement authentication',
  status: 'in_progress',
  priority: 'high'
});

await taskAssistant('user_123', 'What are my high priority tasks?');
// Returns: "You have 1 high priority task: Implement authentication (in progress)"

await updateTask('user_123', 'task_1', 'done');
await taskAssistant('user_123', 'What did I complete today?');
// Returns: "You completed 'Implement authentication' today!"

Key Benefits

• Long-term project tracking
• Automatic task status history
• Intelligent prioritization suggestions
• Context-aware task recommendations

---

3. Document Knowledge Base

Semantic search across documentation, manuals, and knowledge articles.

Implementation

import { Supermemory } from 'supermemory';

const memory = new Supermemory();

// 1. Index documentation
async function indexDocumentation() {
  const docs = [
    { url: 'https://docs.example.com/getting-started', category: 'onboarding' },
    { url: 'https://docs.example.com/api-reference', category: 'api' },
    { url: 'https://docs.example.com/security', category: 'security' },
  ];

  for (const doc of docs) {
    await memory.add({
      content: doc.url,
      containerTag: 'documentation',
      metadata: {
        category: doc.category,
        type: 'documentation',
        indexed_at: new Date().toISOString()
      }
    });
  }
}

// 2. Search documentation
async function searchDocs(query: string, category?: string) {
  const filters = category ? {
    metadata: { category }
  } : undefined;

  const results = await memory.search.memories({
    q: query,
    containerTag: 'documentation',
    searchMode: 'hybrid',  // Use hybrid search for better RAG accuracy
    threshold: 0.3,
    limit: 10,
    filters
  });

  return results.map(r => ({
    content: r.content,
    relevance: r.score,
    metadata: r.metadata
  }));
}

// 3. Intelligent Q&A over documentation
async function askDocumentation(question: string) {
  const results = await searchDocs(question);

  const context = results
    .slice(0, 5) // Top 5 results
    .map(r => r.content)
    .join('\n\n---\n\n');

  const { text } = await generateText({
    model: openai('gpt-4'),
    system: `
You are a documentation assistant. Answer questions using ONLY the provided context.

Context:
${context}

If the answer isn't in the context, say so.
    `,
    prompt: question
  });

  return {
    answer: text,
    sources: results.slice(0, 5)
  };
}

// Usage
await indexDocumentation();

const result = await askDocumentation('How do I authenticate API requests?');
console.log(result.answer);
console.log('Sources:', result.sources);

Key Benefits

• Semantic search (not keyword matching)
• Multi-document understanding
• Automatic source citation
• Scales to thousands of documents

---

4. Customer Support AI

AI agent that remembers customer history and provides personalized support.

Implementation

import { Supermemory } from 'supermemory';

const memory = new Supermemory();

interface Customer {
  id: string;
  name: string;
  email: string;
  plan: 'free' | 'pro' | 'enterprise';
}

interface Ticket {
  id: string;
  customerId: string;
  subject: string;
  description: string;
  status: 'open' | 'resolved';
}

// 1. Store customer profile
async function createCustomer(customer: Customer) {
  await memory.add({
    content: `Customer: ${customer.name} (${customer.email}), Plan: ${customer.plan}`,
    containerTag: customer.id,
    metadata: {
      type: 'profile',
      plan: customer.plan,
      email: customer.email
    }
  });
}

// 2. Log support ticket
async function createTicket(ticket: Ticket) {
  await memory.add({
    content: `Ticket ${ticket.id}: ${ticket.subject}\n${ticket.description}`,
    containerTag: ticket.customerId,
    customId: ticket.id,
    metadata: {
      type: 'ticket',
      status: ticket.status,
      subject: ticket.subject,
      createdAt: new Date().toISOString()
    }
  });
}

// 3. Resolve ticket
async function resolveTicket(customerId: string, ticketId: string, resolution: string) {
  await memory.add({
    content: `Ticket ${ticketId} resolved: ${resolution}`,
    containerTag: customerId,
    metadata: {
      type: 'resolution',
      ticketId,
      resolvedAt: new Date().toISOString()
    }
  });
}

// 4. Support agent assistant
async function supportAssistant(customerId: string, query: string) {
  const response = await memory.profile({
    containerTag: customerId,
    q: query,
    threshold: 0.5
  });

  const staticInfo = response.profile.static.map(f => `- ${f}`).join('\n');
  const recentTickets = response.profile.dynamic.map(f => `- ${f}`).join('\n');

  const { text } = await generateText({
    model: openai('gpt-4'),
    system: `
You are a customer support AI with access to full customer history.

Customer Profile:
${staticInfo}

Previous Tickets and Interactions:
${recentTickets}

Provide helpful, personalized support based on this history.
    `,
    prompt: query
  });

  return text;
}

// Usage
await createCustomer({
  id: 'cust_123',
  name: 'Alice Johnson',
  email: 'alice@example.com',
  plan: 'pro'
});

await createTicket({
  id: 'ticket_001',
  customerId: 'cust_123',
  subject: 'Cannot export data',
  description: 'Getting error when trying to export CSV',
  status: 'open'
});

const suggestion = await supportAssistant(
  'cust_123',
  'Customer is asking about data export again'
);
// Returns: "This is a recurring issue for Alice. She's on the Pro plan and has
// previously had trouble with CSV exports (ticket #001). Let's check if she's
// using the latest version..."

await resolveTicket('cust_123', 'ticket_001', 'Updated to latest version, issue resolved');

Key Benefits

• Complete customer interaction history
• Personalized support responses
• Pattern detection (recurring issues)
• Reduced resolution time

---

5. Code Review Assistant

AI that learns your codebase and provides contextual code reviews.

Implementation

import { Supermemory } from 'supermemory';
import * as fs from 'fs';
import * as path from 'path';

const memory = new Supermemory();

// 1. Index codebase
async function indexCodebase(projectId: string, directory: string) {
  const files = getAllFiles(directory, ['.ts', '.tsx', '.js', '.jsx']);

  for (const file of files) {
    const content = fs.readFileSync(file, 'utf-8');
    const relativePath = path.relative(directory, file);

    await memory.add({
      content: `File: ${relativePath}\n\n${content}`,
      containerTag: `${projectId}_codebase`,
      customId: relativePath,
      metadata: {
        type: 'source_file',
        language: path.extname(file).slice(1),
        path: relativePath,
        lines: content.split('\n').length
      }
    });
  }
}

// 2. Index pull requests and reviews
async function indexPR(projectId: string, prNumber: number, diff: string, comments: string[]) {
  await memory.add({
    content: `PR #${prNumber}\n\nDiff:\n${diff}\n\nComments:\n${comments.join('\n')}`,
    containerTag: `${projectId}_reviews`,
    customId: `pr_${prNumber}`,
    metadata: {
      type: 'pull_request',
      number: prNumber,
      createdAt: new Date().toISOString()
    }
  });
}

// 3. Review code with context
async function reviewCode(projectId: string, code: string, fileName: string) {
  // Search for similar code patterns
  const similarCode = await memory.search.memories({
    q: code,
    containerTag: `${projectId}_codebase`,
    threshold: 0.3,
    limit: 5
  });

  // Get past review comments
  const pastReviews = await memory.search.memories({
    q: `code review comments for ${fileName}`,
    containerTag: `${projectId}_reviews`,
    threshold: 0.3,
    limit: 5
  });

  const { text } = await generateText({
    model: anthropic('claude-3-5-sonnet-20241022'),
    system: `
You are a code review assistant familiar with this codebase.

Similar Code Patterns:
${similarCode.map(c => c.content).slice(0, 3).join('\n\n---\n\n')}

Past Review Patterns:
${pastReviews.map(p => p.content).slice(0, 3).join('\n\n---\n\n')}

Provide a thoughtful code review, considering existing patterns and past feedback.
    `,
    prompt: `Review this code from ${fileName}:\n\n${code}`
  });

  return text;
}

function getAllFiles(dir: string, extensions: string[]): string[] {
  // Implementation omitted for brevity
  return [];
}

// Usage
await indexCodebase('project_abc', './src');
await indexPR('project_abc', 123, '...diff...', ['Great work!', 'Consider adding tests']);

const review = await reviewCode('project_abc', `
async function fetchUser(id: string) {
  const response = await fetch(\`/api/users/\${id}\`);
  return response.json();
}
`, 'api/users.ts');

console.log(review);
// Returns: "This code lacks error handling. Based on past reviews in this project,
// we typically wrap fetch calls in try/catch and validate responses. See similar
// pattern in api/products.ts for reference..."

Key Benefits

• Consistent code review standards
• Learns from past feedback
• Detects anti-patterns
• Suggests improvements based on codebase

---

6. Learning Companion

AI tutor that adapts to student progress and learning style.

Implementation

import { Supermemory } from 'supermemory';

const memory = new Supermemory();

interface LearningSession {
  studentId: string;
  topic: string;
  content: string;
  understanding: 'low' | 'medium' | 'high';
  questions: string[];
}

async function recordLearningSession(session: LearningSession) {
  await memory.add({
    content: `
Topic: ${session.topic}
Understanding: ${session.understanding}
Content covered: ${session.content}
Questions asked: ${session.questions.join(', ')}
    `,
    containerTag: session.studentId,
    metadata: {
      type: 'learning_session',
      topic: session.topic,
      understanding: session.understanding,
      timestamp: new Date().toISOString()
    }
  });
}

async function adaptiveTutor(studentId: string, question: string) {
  const response = await memory.profile({
    containerTag: studentId,
    q: question,
    threshold: 0.5
  });

  // Get learning history from search results (if available)
  const searchResults = response.searchResults?.results || [];

  // Analyze learning patterns from metadata
  const weakTopics = searchResults
    .filter(r => r.metadata?.understanding === 'low')
    .map(r => r.metadata?.topic);

  const strongTopics = searchResults
    .filter(r => r.metadata?.understanding === 'high')
    .map(r => r.metadata?.topic);

  const staticInfo = response.profile.static.map(f => `- ${f}`).join('\n');
  const recentLearning = response.profile.dynamic.slice(0, 5).map(f => f).join('\n\n');

  const { text } = await generateText({
    model: openai('gpt-4'),
    system: `
You are an adaptive tutor who knows the student's learning history.

Student Profile:
${staticInfo}

Topics the student struggles with: ${weakTopics.join(', ') || 'None yet'}
Topics the student excels at: ${strongTopics.join(', ') || 'None yet'}

Recent Learning:
${recentLearning}

Adapt your teaching style and difficulty to match the student's level.
Use analogies to topics they understand well.
    `,
    prompt: question
  });

  return text;
}

// Usage
await recordLearningSession({
  studentId: 'student_456',
  topic: 'React Hooks',
  content: 'useState and useEffect basics',
  understanding: 'medium',
  questions: ['When should I use useEffect?', 'What is the dependency array?']
});

await recordLearningSession({
  studentId: 'student_456',
  topic: 'TypeScript',
  content: 'Type annotations and interfaces',
  understanding: 'high',
  questions: []
});

const explanation = await adaptiveTutor('student_456', 'Explain useMemo to me');
// Returns: "Since you understand TypeScript well, think of useMemo as adding
// type safety to your computed values - it 'memoizes' them. Like how TypeScript
// prevents you from accidentally changing a type, useMemo prevents unnecessary
// recalculations..."

Key Benefits

• Personalized learning pace
• Adapts to learning style
• Identifies knowledge gaps
• Builds on existing strengths

---

7. Multi-Tenant SaaS Application

Isolate data per organization while enabling shared knowledge bases.

Implementation

import { Supermemory } from 'supermemory';

const memory = new Supermemory();

interface Organization {
  id: string;
  name: string;
}

interface User {
  id: string;
  orgId: string;
  name: string;
}

// Container tag strategy
function getContainerTags(orgId: string, userId: string) {
  return {
    org: `org_${orgId}`,
    user: `org_${orgId}_user_${userId}`,
    shared: `org_${orgId}_shared`
  };
}

// 1. Store organization-wide knowledge
async function addOrgKnowledge(orgId: string, content: string) {
  const tags = getContainerTags(orgId, '');

  await memory.add({
    content,
    containerTag: tags.shared,
    metadata: {
      type: 'org_knowledge',
      visibility: 'organization'
    }
  });
}

// 2. Store user-specific data
async function addUserData(orgId: string, userId: string, content: string) {
  const tags = getContainerTags(orgId, userId);

  await memory.add({
    content,
    containerTag: tags.user,
    metadata: {
      type: 'user_data',
      visibility: 'private'
    }
  });
}

// 3. Search with proper isolation
async function search(orgId: string, userId: string, query: string, includeShared: boolean = true) {
  const tags = getContainerTags(orgId, userId);

  const containerTags = includeShared
    ? [tags.user, tags.shared]  // User + org shared
    : [tags.user];              // User only

  const results = await memory.search.memories({
    q: query,
    containerTag: containerTags[0],  // Use first tag
    threshold: 0.3,
    limit: 10
  });

  return results;
}

// Usage
const org1 = 'acme_corp';
const org2 = 'other_corp';
const user1 = 'alice';
const user2 = 'bob';

// Organization-wide knowledge (visible to all users in org)
await addOrgKnowledge(org1, 'Company policy: Remote work allowed');
await addOrgKnowledge(org2, 'Company policy: Office-only');

// User-specific data (visible only to that user)
await addUserData(org1, user1, 'Alice prefers dark mode');
await addUserData(org1, user2, 'Bob prefers light mode');

// Alice searches (sees org1 shared + her own data)
const aliceResults = await search(org1, user1, 'work policy');
// Returns: "Company policy: Remote work allowed" ✅
// Does NOT return: Bob's preferences ✅
// Does NOT return: org2 data ✅

// Bob searches (sees org1 shared + his own data)
const bobResults = await search(org1, user2, 'preferences');
// Returns: "Bob prefers light mode" ✅
// Does NOT return: Alice's preferences ✅

Key Benefits

• Perfect data isolation per tenant
• Shared knowledge bases
• Flexible visibility controls
• Scales to thousands of organizations

---

8. Research Assistant

Manage research papers, notes, and insights with automatic relationship discovery.

Implementation

import { Supermemory } from 'supermemory';

const memory = new Supermemory();

interface Paper {
  title: string;
  authors: string[];
  abstract: string;
  url: string;
  year: number;
}

async function addPaper(userId: string, paper: Paper) {
  await memory.add({
    content: `
Title: ${paper.title}
Authors: ${paper.authors.join(', ')}
Year: ${paper.year}
Abstract: ${paper.abstract}
URL: ${paper.url}
    `,
    containerTag: `${userId}_research`,
    customId: paper.url,
    metadata: {
      type: 'paper',
      year: paper.year,
      authors: paper.authors,
      title: paper.title
    }
  });
}

async function addResearchNote(userId: string, note: string, relatedPapers: string[]) {
  await memory.add({
    content: note,
    containerTag: `${userId}_research`,
    metadata: {
      type: 'note',
      relatedPapers,
      createdAt: new Date().toISOString()
    }
  });
}

async function findRelatedResearch(userId: string, topic: string) {
  const results = await memory.search.memories({
    q: topic,
    containerTag: `${userId}_research`,
    threshold: 0.3,
    limit: 20
  });

  // Group by type
  const papers = results.filter(r => r.metadata?.type === 'paper');
  const notes = results.filter(r => r.metadata?.type === 'note');

  return { papers, notes };
}

async function synthesizeInsights(userId: string, research_question: string) {
  const related = await findRelatedResearch(userId, research_question);

  const context = [
    '=== Related Papers ===',
    ...related.papers.map(p => p.content),
    '\n=== Your Notes ===',
    ...related.notes.map(n => n.content)
  ].join('\n\n');

  const { text } = await generateText({
    model: anthropic('claude-3-5-sonnet-20241022'),
    system: `
You are a research assistant helping synthesize insights from papers and notes.

Relevant Research:
${context}

Provide a synthesis that connects ideas across papers and notes.
    `,
    prompt: research_question
  });

  return text;
}

// Usage
await addPaper('researcher_123', {
  title: 'Attention Is All You Need',
  authors: ['Vaswani et al.'],
  year: 2017,
  abstract: 'We propose a new simple network architecture, the Transformer...',
  url: 'https://arxiv.org/abs/1706.03762'
});

await addPaper('researcher_123', {
  title: 'BERT: Pre-training of Deep Bidirectional Transformers',
  authors: ['Devlin et al.'],
  year: 2018,
  abstract: 'We introduce a new language representation model called BERT...',
  url: 'https://arxiv.org/abs/1810.04805'
});

await addResearchNote(
  'researcher_123',
  'BERT builds on the Transformer architecture introduced in "Attention Is All You Need"',
  ['https://arxiv.org/abs/1706.03762', 'https://arxiv.org/abs/1810.04805']
);

const synthesis = await synthesizeInsights(
  'researcher_123',
  'How did transformers evolve from 2017 to 2018?'
);
// Returns: "The Transformer architecture (Vaswani et al., 2017) introduced
// self-attention mechanisms. BERT (Devlin et al., 2018) extended this by
// adding bidirectional pre-training, as noted in your research notes..."

Key Benefits

• Automatic relationship discovery between papers
• Connect notes to relevant research
• Synthesize insights across sources
• Never lose track of references

---

Common Patterns Across Use Cases

Pattern 1: Context Retrieval Before Generation

// Always retrieve context first
const response = await memory.profile({
  containerTag: userId,
  q: userMessage
});

// Then use in generation
const staticFacts = response.profile.static.join('\n');
const dynamicFacts = response.profile.dynamic.join('\n');

const llmResponse = await generateText({
  system: `User Profile:\n${staticFacts}\n\nRecent Context:\n${dynamicFacts}`,
  prompt: userMessage
});

Pattern 2: Store After Interaction

// Always store the result
await memory.add({
  content: `Input: ${input}\nOutput: ${output}`,
  containerTag: userId,
  metadata: { timestamp: new Date().toISOString() }
});

Pattern 3: Rich Metadata for Filtering

await memory.add({
  content: data,
  containerTag: userId,
  metadata: {
    type: 'conversation',
    category: 'support',
    priority: 'high',
    tags: ['billing', 'urgent'],
    timestamp: new Date().toISOString()
  }
});

// Later filter by metadata
const results = await memory.search.memories({
  q: 'billing issues',
  containerTag: 'user_123',
  filters: {
    metadata: { priority: 'high', type: 'conversation' }
  }
});

Pattern 4: Hierarchical Container Tags

// Organization → Team → User hierarchy
const tags = {
  org: `org_${orgId}`,
  team: `org_${orgId}_team_${teamId}`,
  user: `org_${orgId}_team_${teamId}_user_${userId}`
};

// Search at appropriate level
const orgWide = await memory.search.memories({
  q: 'company policies',
  containerTag: tags.org,
  limit: 10
});

const teamSpecific = await memory.search.memories({
  q: 'team resources',
  containerTag: tags.team,
  limit: 10
});

Next Steps

Ready to build your own use case? Start with the Quickstart Guide or explore the SDK Documentation.

For questions or custom use cases, visit console.supermemory.ai.