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User profiles are extremely short summaries of context about an entity (Usually a user, but can be anything) which includes both the static facts about them, as well as a few recent episodes.
You can think of these as a dynamic compaction that’s done by supermemory in real-time.
This profile should be injected into the agent context for truly personalized experiences. To read more, visit User profiles - Concept Get a user’s profile — their static facts and dynamic context — with a single API call.
Profiles are built automatically as you ingest content. No setup required.

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Quick Start

import Supermemory from 'supermemory';  const client = new Supermemory();  const { profile } = await client.profile({  containerTag: "user_123" });  console.log(profile.static); // Long-term facts console.log(profile.dynamic); // Recent context
Response: 
{  "profile": {  "static": [  "User is a software engineer",  "User specializes in Python and React",  "User prefers dark mode interfaces"  ],  "dynamic": [  "User is working on Project Alpha",  "User recently started learning Rust",  "User is debugging authentication issues"  ]  } }
Get profile and search results in one call by adding the q parameter: 
const result = await client.profile({  containerTag: "user_123",  q: "deployment errors" });  // Profile data const { static: facts, dynamic: context } = result.profile;  // Search results (only if q was provided) const memories = result.searchResults?.results || [];

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Parameters

ParameterTypeRequiredDescription
containerTagstringYesUser/project identifier
qstringNoSearch query (includes search results in response)
threshold0-1NoFilter search results by relevance score

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Building Prompts

The most common pattern — inject profile into your LLM’s system prompt: 
async function chat(userId: string, message: string) {  const { profile } = await client.profile({ containerTag: userId });   const systemPrompt = `You are assisting a user.  ABOUT THE USER: ${profile.static?.join('\n') || 'No profile yet.'}  CURRENT CONTEXT: ${profile.dynamic?.join('\n') || 'No recent activity.'}  Personalize responses to their expertise and preferences.`;   return llm.chat({  messages: [  { role: "system", content: systemPrompt },  { role: "user", content: message }  ]  }); }

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Full Context Pattern

Get profile + query-specific memories in one call: 
async function getContext(userId: string, query: string) {  const result = await client.profile({  containerTag: userId,  q: query,  threshold: 0.6  });   return ` User Background: ${result.profile.static.join('\n')}  Current Context: ${result.profile.dynamic.join('\n')}  Relevant Memories: ${result.searchResults?.results.map(m => m.memory).join('\n') || 'None'}  `; }

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Framework Examples

async function withProfile(req, res, next) {  if (!req.user?.id) return next();   try {  const { profile } = await client.profile({  containerTag: req.user.id  });  req.userProfile = profile;  } catch (e) {  req.userProfile = null;  }  next(); }  app.use(withProfile);  app.post('/chat', (req, res) => {  // req.userProfile available in all routes }); 
// app/api/chat/route.ts export async function POST(req: NextRequest) {  const { userId, message } = await req.json();   const { profile } = await client.profile({  containerTag: userId  });   const response = await generateResponse(message, profile);  return NextResponse.json({ response }); } 
import { withSupermemory } from "@supermemory/tools/ai-sdk" import { openai } from "@ai-sdk/openai"  // Profiles automatically injected const model = withSupermemory(openai("gpt-4"), "user-123")  const result = await generateText({  model,  messages: [{ role: "user", content: "Help with my project" }] });
See AI SDK Integration for details.

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Response Schema

interface ProfileResponse {  profile: {  static: string[]; // Long-term facts  dynamic: string[]; // Recent context  };  searchResults?: { // Only if q parameter provided  results: SearchResult[];  total: number;  timing: number;  }; }

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Next Steps