supermemoryai/supermemory

State-of-the-art memory and context engine for AI.

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Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.

All of this is in our single memory structure and ontology.

Use Supermemory

Give your AI memory

The Supermemory App, browser extension, plugins and MCP server gives any compatible AI assistant persistent memory. One install, and your AI remembers you.

The app

You can use supermemory without any code, by using our consumer-facing app for free.

Start at https://app.supermemory.ai

It also comes with an agent embedded inside, which we call Nova.

Supermemory Plugins

Supermemory comes built with Plugins for Claude Code, OpenCode and OpenClaw.

These plugins are implementations of the supermemory API, and they are open source!

You can find them here:

• Openclaw plugin: https://github.com/supermemoryai/openclaw-supermemory
• Claude code plugin: https://github.com/supermemoryai/claude-supermemory
• OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory

MCP - Quick install

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npx -y install-mcp@latest https://mcp.supermemory.ai/mcp --client claude --oauth=yes

Replace claude with your client: cursor, windsurf, vscode, etc.

Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp

What your AI gets

How it works

Once installed, Supermemory runs in the background:

1. You talk to your AI normally. Share preferences, mention projects, discuss problems.
2. Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise.
3. Next conversation, your AI already knows you. It recalls what you’re working on, how you like things, what you discussed before.

Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else.

Supported clients

Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw

The MCP server is open source — view the source.

Manual configuration

Add this to your MCP client config:

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{
  "mcpServers": {
    "supermemory": {
      "url": "https://mcp.supermemory.ai/mcp"
    }
  }
}

Or use an API key instead of OAuth:

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{
  "mcpServers": {
    "supermemory": {
      "url": "https://mcp.supermemory.ai/mcp",
      "headers": {
        "Authorization": "Bearer sm_your_api_key_here"
      }
    }
  }
}

Build with Supermemory (API)

If you’re building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing.

Install

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npm install supermemory    # or: pip install supermemory

Quickstart

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import Supermemory from "supermemory";

const client = new Supermemory();

// Store a conversation
await client.add({
  content: "User loves TypeScript and prefers functional patterns",
  containerTag: "user_123",
});

// Get user profile + relevant memories in one call
const { profile, searchResults } = await client.profile({
  containerTag: "user_123",
  q: "What programming style does the user prefer?",
});

// profile.static  → ["Loves TypeScript", "Prefers functional patterns"]
// profile.dynamic → ["Working on API integration"]
// searchResults   → Relevant memories ranked by similarity

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from supermemory import Supermemory

client = Supermemory()

client.add(
    content="User loves TypeScript and prefers functional patterns",
    container_tag="user_123"
)

result = client.profile(container_tag="user_123", q="programming style")

print(result.profile.static)   # Long-term facts
print(result.profile.dynamic)  # Recent context

Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies.

Framework integrations

Drop-in wrappers for every major AI framework:

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// Vercel AI SDK
import { withSupermemory } from "@supermemory/tools/ai-sdk";
const model = withSupermemory(openai("gpt-4o"), "user_123");

// Mastra
import { withSupermemory } from "@supermemory/tools/mastra";
const agent = new Agent(withSupermemory(config, "user-123", { mode: "full" }));

Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n

Search modes

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// Hybrid (default) — RAG + Memory in one query
const results = await client.search.memories({
  q: "how do I deploy?",
  containerTag: "user_123",
  searchMode: "hybrid",
});
// Returns deployment docs (RAG) + user's deploy preferences (Memory)

// Memories only
const results = await client.search.memories({
  q: "user preferences",
  containerTag: "user_123",
  searchMode: "memories",
});

User profiles

Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user:

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const { profile } = await client.profile({ containerTag: "user_123" });

// profile.static  → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"]
// profile.dynamic → ["Working on auth migration", "Debugging rate limits"]

One call. ~50ms. Inject into your system prompt and your agent instantly knows who it’s talking to.

Connectors

Auto-sync external data into your knowledge base:

Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler

Real-time webhooks. Documents automatically processed, chunked, and searchable.

API at a glance

Full API reference → supermemory.ai/docs

Benchmarks

Supermemory is state of the art across all major AI memory benchmarks:

We also built MemoryBench — an open-source framework for standardized, reproducible benchmarks of memory providers. Compare Supermemory, Mem0, Zep, and others head-to-head:

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bun run src/index.ts run -p supermemory -b longmemeval -j gpt-4o -r my-run

Benchmarking your own memory solution

We provide an Agent skill for companies to benchmark their own context and memory solutions against supermemory.

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npx skills add supermemoryai/memorybench

Simply run this and do /benchmark-context - Supermemory will automatically do the work for you!

How memory works under the hood

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Your app / AI tool
        ↓
   Supermemory
        │
        ├── Memory Engine     Extracts facts, tracks updates, resolves contradictions,
        │                     auto-forgets expired info
        ├── User Profiles     Static facts + dynamic context built from engine, always fresh
        ├── Hybrid Search     RAG + Memory in one query
        ├── Connectors        Real-time sync from Google Drive, Gmail, Notion, GitHub...
        └── File Processing   PDFs, images, videos, code → searchable chunks

Memory is not RAG. RAG retrieves document chunks — stateless, same results for everyone. Memory extracts and tracks facts about users over time. It understands that “I just moved to SF” supersedes “I live in NYC.” Supermemory runs both together by default, so you get knowledge base retrieval and personalized context in every query. Read more about this here - https://supermemory.ai/docs/concepts/memory-vs-rag

Automatic forgetting. Supermemory knows when memories become irrelevant. Temporary facts (“I have an exam tomorrow”) expire after the date passes. Contradictions are resolved automatically. Noise never becomes permanent memory.

Links

• 📖 Documentation
• 🚀 Quickstart
• 🧪 MemoryBench
• 🔌 Integrations
• 💬 Discord
• 𝕏 Twitter

Give your AI a memory. It's about time..