Vibe Coding Setup

Automatic Supermemory integration using AI coding agents

Get your AI coding agent to integrate Supermemory in minutes. Copy the prompt below, paste it into Claude/GPT/Cursor, and let it do the work.

Quick Setup

MCP (Claude/Cursor)

Give your agent a way to reference and search through supermemory docs.

Copy Prompt

Paste one prompt, answer questions, get working code

Claude Code Skill

Interactive guided setup

MCP Server

Give your agent a way to reference and search through supermemory docs.

Quick Install

npx -y install-mcp@latest  --client claude-code --oauth=no -y

Replace claude with: cursor, opencode, or vscode

---

The Prompt

INFO

Copy everything in the code block below and paste it into your AI coding agent. It will ask you questions and generate complete integration code.

After adding the MCP, paste this in your agent session:

Copy prompt below.

  You are integrating Supermemory into my application. Supermemory provides user memory, semantic search, and automatic knowledge extraction for AI applications.

  Note: You can always reference the documentation by using the **SearchSupermemoryDocs MCP** or running a web search tool for content on **supermemory.ai/docs**.

  STEP 1: ASK ME THESE QUESTIONS

  1. What are you building?
     - Personal chatbot/assistant
     - Team knowledge base
     - Customer support bot
     - Document Q&A
     - Other

  2. How do you want to integrate?
     - Vercel AI SDK (@supermemory/tools)
     - OpenAI plugins
     - Direct SDK (supermemory npm/pip)
     - Direct API calls

  3. Data model?
     - Individual users only → containerTag: userId
     - Organizations only → containerTag: orgId
     - Both users AND orgs → ask for strategy

  4. Do you want USER PROFILES?
     User profiles are automatically-maintained facts about users (what they like, what they're working on, preferences).
     - Yes (RECOMMENDED) → Use client.profile() to get context
     - No → Just use search

  5. How should I retrieve context?
     - OPTION A: One call with search included → profile({ containerTag, q: userMessage })
     - OPTION B: Separate calls → profile() for facts, search() for memories

  STEP 2: INSTALL

  # Get API key: 
  npm install supermemory  # or: pip install supermemory
  # For Vercel AI SDK: npm install @supermemory/tools
  export SUPERMEMORY_API_KEY="sm_..."


  STEP 3: CONFIGURE SETTINGS (DO THIS FIRST)

  typescript
  // PATCH 
  fetch(' {
    method: 'PATCH',
    headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
    body: JSON.stringify({
      shouldLLMFilter: true,
      filterPrompt: `This is a [your app description]. containerTag is [userId/orgId]. We store [what data].`
    })
  })

  STEP 4: CONTAINER TAG STRATEGY

  Based on their data model answer:

  USER-ONLY APP:
  typescript


  ORG-ONLY APP:
  typescript
  containerTag: orgId  // Org members share memories


  BOTH (ask which):
  - Option A: `containerTag: \`\${userId}-\${orgId}\``
  - Option B: `containerTag: orgId, metadata: { userId }`
  - Option C: `containerTag: userId, metadata: { orgId }`

  STEP 5: INTEGRATION CODE

  Based on their integration choice:

  --- VERCEL AI SDK ---

  typescript
  import { streamText } from 'ai'
  import { anthropic } from '@ai-sdk/anthropic'
  import { supermemoryTools } from '@supermemory/tools/ai-sdk'

  // Option 1: Agent tools (recommended for agentic flows)
  const result = await streamText({
    model: anthropic('claude-3-5-sonnet-20241022'),
    prompt: userMessage,
    tools: supermemoryTools(process.env.SUPERMEMORY_API_KEY, {
      containerTags: [userId]
    })
  })
  // Agent gets searchMemories, addMemory, fetchMemory tools

  // Option 2: Profile middleware (automatic context injection)
  import { withSupermemory } from '@supermemory/tools/ai-sdk'
  const modelWithMemory = withSupermemory(anthropic('claude-3-5-sonnet-20241022'), {
    containerTag: userId,
    customId: 'conversation-1',
  })

  const result = await generateText({
    model: modelWithMemory,
    messages: [{ role: 'user', content: userMessage }]
  })
  // Profile is automatically injected into context


  --- DIRECT SDK (WITH PROFILES) ---

  typescript
  import Supermemory from 'supermemory'

  const client = new Supermemory()

  // Before each LLM call:
  const { profile, searchResults } = await client.profile({
    containerTag: userId,
    q: userMessage  // Include this if they chose OPTION A (one call)
                    // Omit if they chose OPTION B (separate calls)
  })

  // Build context
  const context = `
  Static facts: ${profile.static.join('\n')}
  Recent context: ${profile.dynamic.join('\n')}
  ${searchResults ? `Memories: ${searchResults.results.map(r => r.memory).join('\n')}` : ''}
  `

  // Send to LLM
  const messages = [
    { role: 'system', content: `User context:\n${context}` },
    { role: 'user', content: userMessage }
  ]

  // After LLM responds:
  await client.add({
    content: `user: ${userMessage}\nassistant: ${response}`,
    containerTag: userId
  })


  --- DIRECT SDK (NO PROFILES) ---

  ```typescript
  import Supermemory from 'supermemory'

  const client = new Supermemory()

  // Search for relevant memories
  const results = await client.search({
    q: userMessage,
    containerTag: userId,
    searchMode: 'hybrid',  // Searches memories + document chunks
    limit: 5
  })

  // Build context
  const context = results.results.map(r => r.memory || r.chunk).join('\n')

  // Send to LLM with context
  const messages = [
    { role: 'system', content: `Relevant context:\n${context}` },
    { role: 'user', content: userMessage }
  ]

  // Store the conversation
  await client.add({
    content: `user: ${userMessage}\nassistant: ${response}`,
    containerTag: userId
  })

  --- PYTHON VERSION ---
  python
  from supermemory import Supermemory

  client = Supermemory()

  # With profiles (if they want it)
  profile_data = client.profile(
      container_tag=user_id,
      q=user_message  # Include if OPTION A, omit if OPTION B
  )

  context = f"""
  Static: {chr(10).join(profile_data.profile.static)}
  Dynamic: {chr(10).join(profile_data.profile.dynamic)}
  """

  # Store conversation
  client.add(content=f"user: {user_message}\\nassistant: {response}", container_tag=user_id)

  --- DIRECT API ---

  bash
  # Add memory
  curl -X POST  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"content": "conversation", "containerTag": "userId"}'

  # Get profile
  curl -X POST  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"containerTag": "userId", "q": "search query"}'

  # Search
  curl -X POST  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"q": "query", "containerTag": "userId", "searchMode": "hybrid"}'

  STEP 6: FILE UPLOADS (if they need it)

  typescript
  // Files are automatically extracted (PDFs, images with OCR, videos with transcription)
  const formData = new FormData()
  formData.append('file', fileBlob)
  formData.append('containerTag', userId)

  await fetch(' {
    method: 'POST',
    headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
    body: formData
  })

  // Processing is async - check status before assuming searchable
  // GET /v3/documents/{documentId}


  STEP 7: SEARCH MODES

  typescript
  // HYBRID (recommended) - searches memories + document chunks
  searchMode: 'hybrid'

  // MEMORIES ONLY - just extracted memories, no original text
  searchMode: 'memories'


  STEP 8: METADATA FILTERS (if they need secondary filtering)

  typescript
  await client.search({
    q: query,
    containerTag: userId,
    filters: {
      AND: [
        { key: 'type', value: 'conversation', type: 'string_equal' },
        { key: 'timestamp', value: '2024', type: 'string_contains' }
      ]
    }
  })

  KEY POINTS:

  1. Configure settings FIRST with filterPrompt
  2. User profiles = automatic facts about users (profile.static + profile.dynamic)
  3. profile({ containerTag, q }) combines profile + search in ONE call
  4. Search modes: 'hybrid' (recommended) or 'memories'
  5. File extraction is automatic - no config needed
  6. Store conversations after each interaction
  7. containerTag should match what you put in filterPrompt

  TESTING:

  bash
  # 1. Configure settings
  curl -X PATCH  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"shouldLLMFilter": true, "filterPrompt": "..."}'

  # 2. Add test memory
  curl -X POST  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"content": "Test", "containerTag": "test_user"}'

  # 3. Get profile
  curl -X POST  \
    -H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
    -d '{"containerTag": "test_user"}'


  NOW:
  1. Ask me the 5 questions above
  2. Generate complete working code based on my answers
  3. Include installation, settings config, and full integration

  DOCS:

---

Claude Code Skill

Interactive setup for Claude Code users.

Install

# 1. Clone repo
git clone 

# 2. Copy skill
mkdir -p ~/.claude/skills
cp supermemory/.claude/skills/supermemory-integrate.md ~/.claude/skills/

# 3. Restart Claude Code

Use

/supermemory-integrate

The skill asks questions interactively and generates code for your specific setup.

---

Next Steps

Quickstart

Manual integration guide

User Profiles

Deep dive into profiles

Search API

Search modes and parameters

API Reference