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389 lines
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389 lines
10 KiB
Text
---
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title: "Vibe Coding Setup"
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description: "Automatic Supermemory integration using AI coding agents"
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icon: "zap"
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sidebarTitle: "Install with AI"
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---
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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.
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## Quick Setup
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<CardGroup cols={3}>
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<Card title="MCP (Claude/Cursor)" icon="plug" href="#mcp-server">
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Give your agent a way to reference and search through supermemory docs.
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</Card>
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<Card title="Copy Prompt" icon="copy" href="#the-prompt">
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Paste one prompt, answer questions, get working code
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</Card>
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<Card title="Claude Code Skill" icon="terminal" href="#claude-code-skill">
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Interactive guided setup
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</Card>
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</CardGroup>
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## MCP Server
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Give your agent a way to reference and search through supermemory docs.
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### Quick Install
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```bash
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npx -y install-mcp@latest https://supermemory.ai/docs/mcp --client claude-code --oauth=no -y
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```
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Replace `claude` with: `cursor`, `opencode`, or `vscode`
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---
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## The Prompt
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<Note>
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**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.
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</Note>
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After adding the MCP, paste this in your agent session:
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<Accordion title="Copy prompt below." icon='copy'>
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```
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You are integrating Supermemory into my application. Supermemory provides user memory, semantic search, and automatic knowledge extraction for AI applications.
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Note: You can always reference the documentation by using the **SearchSupermemoryDocs MCP** or running a web search tool for content on **supermemory.ai/docs**.
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STEP 1: ASK ME THESE QUESTIONS
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1. What are you building?
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- Personal chatbot/assistant
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- Team knowledge base
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- Customer support bot
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- Document Q&A
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- Other
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2. How do you want to integrate?
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- Vercel AI SDK (@supermemory/tools)
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- OpenAI plugins
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- Direct SDK (supermemory npm/pip)
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- Direct API calls
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3. Data model?
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- Individual users only → containerTag: userId
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- Organizations only → containerTag: orgId
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- Both users AND orgs → ask for strategy
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4. Do you want USER PROFILES?
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User profiles are automatically-maintained facts about users (what they like, what they're working on, preferences).
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- Yes (RECOMMENDED) → Use client.profile() to get context
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- No → Just use search
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5. How should I retrieve context?
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- OPTION A: One call with search included → profile({ containerTag, q: userMessage })
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- OPTION B: Separate calls → profile() for facts, search() for memories
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STEP 2: INSTALL
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# Get API key: https://console.supermemory.ai
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npm install supermemory # or: pip install supermemory
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# For Vercel AI SDK: npm install @supermemory/tools
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export SUPERMEMORY_API_KEY="sm_..."
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STEP 3: CONFIGURE SETTINGS (DO THIS FIRST)
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typescript
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// PATCH https://api.supermemory.ai/v3/settings
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fetch('https://api.supermemory.ai/v3/settings', {
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method: 'PATCH',
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headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
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body: JSON.stringify({
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shouldLLMFilter: true,
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filterPrompt: `This is a [your app description]. containerTag is [userId/orgId]. We store [what data].`
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})
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})
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STEP 4: CONTAINER TAG STRATEGY
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Based on their data model answer:
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USER-ONLY APP:
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typescript
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ORG-ONLY APP:
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typescript
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containerTag: orgId // Org members share memories
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BOTH (ask which):
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- Option A: `containerTag: \`\${userId}-\${orgId}\``
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- Option B: `containerTag: orgId, metadata: { userId }`
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- Option C: `containerTag: userId, metadata: { orgId }`
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STEP 5: INTEGRATION CODE
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Based on their integration choice:
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--- VERCEL AI SDK ---
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typescript
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import { streamText } from 'ai'
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import { anthropic } from '@ai-sdk/anthropic'
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import { supermemoryTools } from '@supermemory/tools/ai-sdk'
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// Option 1: Agent tools (recommended for agentic flows)
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const result = await streamText({
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model: anthropic('claude-3-5-sonnet-20241022'),
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prompt: userMessage,
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tools: supermemoryTools(process.env.SUPERMEMORY_API_KEY, {
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containerTags: [userId]
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})
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})
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// Agent gets searchMemories, addMemory, fetchMemory tools
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// Option 2: Profile middleware (automatic context injection)
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import { withSupermemory } from '@supermemory/tools/ai-sdk'
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const modelWithMemory = withSupermemory(anthropic('claude-3-5-sonnet-20241022'), {
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containerTag: userId,
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customId: 'conversation-1',
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})
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const result = await generateText({
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model: modelWithMemory,
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messages: [{ role: 'user', content: userMessage }]
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})
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// Profile is automatically injected into context
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--- DIRECT SDK (WITH PROFILES) ---
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typescript
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import Supermemory from 'supermemory'
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const client = new Supermemory()
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// Before each LLM call:
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const { profile, searchResults } = await client.profile({
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containerTag: userId,
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q: userMessage // Include this if they chose OPTION A (one call)
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// Omit if they chose OPTION B (separate calls)
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})
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// Build context
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const context = `
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Static facts: ${profile.static.join('\n')}
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Recent context: ${profile.dynamic.join('\n')}
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${searchResults ? `Memories: ${searchResults.results.map(r => r.memory).join('\n')}` : ''}
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`
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// Send to LLM
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const messages = [
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{ role: 'system', content: `User context:\n${context}` },
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{ role: 'user', content: userMessage }
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]
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// After LLM responds:
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await client.add({
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content: `user: ${userMessage}\nassistant: ${response}`,
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containerTag: userId
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})
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--- DIRECT SDK (NO PROFILES) ---
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```typescript
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import Supermemory from 'supermemory'
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const client = new Supermemory()
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// Search for relevant memories
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const results = await client.search({
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q: userMessage,
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containerTag: userId,
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searchMode: 'hybrid', // Searches memories + document chunks
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limit: 5
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})
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// Build context
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const context = results.results.map(r => r.memory || r.chunk).join('\n')
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// Send to LLM with context
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const messages = [
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{ role: 'system', content: `Relevant context:\n${context}` },
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{ role: 'user', content: userMessage }
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]
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// Store the conversation
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await client.add({
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content: `user: ${userMessage}\nassistant: ${response}`,
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containerTag: userId
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})
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--- PYTHON VERSION ---
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python
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from supermemory import Supermemory
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client = Supermemory()
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# With profiles (if they want it)
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profile_data = client.profile(
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container_tag=user_id,
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q=user_message # Include if OPTION A, omit if OPTION B
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)
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context = f"""
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Static: {chr(10).join(profile_data.profile.static)}
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Dynamic: {chr(10).join(profile_data.profile.dynamic)}
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"""
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# Store conversation
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client.add(content=f"user: {user_message}\\nassistant: {response}", container_tag=user_id)
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--- DIRECT API ---
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bash
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# Add memory
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curl -X POST https://api.supermemory.ai/v3/documents \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"content": "conversation", "containerTag": "userId"}'
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# Get profile
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curl -X POST https://api.supermemory.ai/v4/profile \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"containerTag": "userId", "q": "search query"}'
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# Search
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curl -X POST https://api.supermemory.ai/v4/search \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"q": "query", "containerTag": "userId", "searchMode": "hybrid"}'
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STEP 6: FILE UPLOADS (if they need it)
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typescript
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// Files are automatically extracted (PDFs, images with OCR, videos with transcription)
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const formData = new FormData()
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formData.append('file', fileBlob)
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formData.append('containerTag', userId)
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await fetch('https://api.supermemory.ai/v3/documents/file', {
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method: 'POST',
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headers: { 'x-supermemory-api-key': process.env.SUPERMEMORY_API_KEY },
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body: formData
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})
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// Processing is async - check status before assuming searchable
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// GET /v3/documents/{documentId}
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STEP 7: SEARCH MODES
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typescript
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// HYBRID (recommended) - searches memories + document chunks
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searchMode: 'hybrid'
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// MEMORIES ONLY - just extracted memories, no original text
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searchMode: 'memories'
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STEP 8: METADATA FILTERS (if they need secondary filtering)
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typescript
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await client.search({
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q: query,
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containerTag: userId,
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filters: {
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AND: [
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{ key: 'type', value: 'conversation', type: 'string_equal' },
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{ key: 'timestamp', value: '2024', type: 'string_contains' }
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]
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}
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})
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KEY POINTS:
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1. Configure settings FIRST with filterPrompt
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2. User profiles = automatic facts about users (profile.static + profile.dynamic)
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3. profile({ containerTag, q }) combines profile + search in ONE call
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4. Search modes: 'hybrid' (recommended) or 'memories'
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5. File extraction is automatic - no config needed
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6. Store conversations after each interaction
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7. containerTag should match what you put in filterPrompt
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TESTING:
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bash
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# 1. Configure settings
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curl -X PATCH https://api.supermemory.ai/v3/settings \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"shouldLLMFilter": true, "filterPrompt": "..."}'
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# 2. Add test memory
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curl -X POST https://api.supermemory.ai/v3/documents \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"content": "Test", "containerTag": "test_user"}'
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# 3. Get profile
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curl -X POST https://api.supermemory.ai/v4/profile \
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-H "x-supermemory-api-key: $SUPERMEMORY_API_KEY" \
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-d '{"containerTag": "test_user"}'
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NOW:
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1. Ask me the 5 questions above
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2. Generate complete working code based on my answers
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3. Include installation, settings config, and full integration
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DOCS: https://supermemory.ai/docs
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```
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</Accordion>
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---
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## Claude Code Skill
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Interactive setup for Claude Code users.
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### Install
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```bash
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# 1. Clone repo
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git clone https://github.com/supermemoryai/supermemory.git
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# 2. Copy skill
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mkdir -p ~/.claude/skills
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cp supermemory/.claude/skills/supermemory-integrate.md ~/.claude/skills/
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# 3. Restart Claude Code
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```
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### Use
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```bash
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/supermemory-integrate
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```
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The skill asks questions interactively and generates code for your specific setup.
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---
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## Next Steps
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<CardGroup cols={2}>
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<Card title="Quickstart" icon="rocket" href="/quickstart">
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Manual integration guide
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</Card>
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<Card title="User Profiles" icon="user" href="/concepts/user-profiles">
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Deep dive into profiles
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</Card>
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<Card title="Search API" icon="search" href="/search">
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Search modes and parameters
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</Card>
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<Card title="API Reference" icon="code" href="https://api.supermemory.ai/v3/openapi">
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Complete API docs
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</Card>
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</CardGroup>
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