supermemoryai/supermemory — a trending TypeScript project

supermemoryai/supermemory — TypeScript · 30,008 stars · 2,629 forks · +140 today

Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.

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

Docs · Quickstart · Self-host · Dashboard · Discord

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#1 on every major AI memory benchmark — LongMemEval, LoCoMo, and ConvoMem.
95% Recall@15 with a 99.4% context reduction · ~50ms user profiles.
Read the research →

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.

curl -fsSL https://supermemory.ai/install | bash

Plugins and the MCP server give any compatible AI assistant persistent memory. One install, and your AI remembers you.

Supermemory comes built with plugins for Claude Code, Muse Code, Cursor, Codex, OpenCode, OpenClaw, and Hermes.

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

You can find them here:

• Claude Code plugin: https://github.com/supermemoryai/claude-supermemory
• Muse Code plugin: https://github.com/supermemoryai/muse-supermemory
• Cursor plugin: https://github.com/supermemoryai/cursor-supermemory
• Codex plugin: https://github.com/supermemoryai/codex-supermemory
• OpenClaw plugin: https://github.com/supermemoryai/openclaw-supermemory
• OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory
• Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent

Server URL:

https://mcp.supermemory.ai/mcp

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

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

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.

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

The MCP server is open source — view the source.

Add this to your MCP client config:

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

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.

npm install supermemory    # or: pip install supermemory

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

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.

Drop-in wrappers for every major AI framework:

// Vercel AI SDK
import { withSupermemory } from "@supermemory/tools/ai-sdk";
const model = withSupermemory(openai("gpt-4o"), { containerTag: "user_123", customId: "conv-1" });

// 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

// Hybrid (default) — RAG + Memory in one query
const results = await client.search({
  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({
  q: "user preferences",
  containerTag: "user_123",
  searchMode: "memories",
});

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

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.

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.

Full API reference → supermemory.ai/docs

State-of-the-art memory, on your machine. One binary. Zero config.

curl -fsSL https://supermemory.ai/install | bash
# or
npx supermemory local

supermemory-server

First boot sets up the embedded Supermemory graph engine, local embeddings, and your credentials, then prints an API key. The full Memory API — documents, memories, user profiles, hybrid search — runs against http://localhost:6767.

const client = new Supermemory({
  apiKey: "sm_...",
  baseURL: "http://localhost:6767", // that's the only change
});

• Bring any model — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. An interactive wizard walks you through it on first boot.
• Embeddings — local Xenova/bge-base-en-v1.5 by default (no API key); optionally OpenAI, Gemini, or Ollama. Same provider stack as cloud.
• Fully offline if you want — point it at Ollama (gpt-oss:20b works great) and nothing leaves your machine.
• Your data, one directory — everything lives in ./.supermemory, easy to back up or move.
• Same API as the platform — prototype locally, ship on the hosted platform by changing baseURL.

Read the self-hosting docs — quickstart, configuration, embeddings, and local vs. Enterprise.

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

On LongMemEval, supermemory reaches 95% Recall@15 while adding only ~720 tokens of context — a 99.4% context reduction (99.6% at @10, 99.8% at @5). Recall by category: Knowledge Updates 99%, Assistant recall 100%, User recall 97%, Multi-session 93%, Temporal Reasoning 91%, Preference 90%.

We also built the Supermemory Filesystem (SMFS), which uses 3.0× fewer tokens on Claude (24M vs 72M) and 1.75× fewer on Codex across the 110-question xAFS benchmark. See the full write-ups on our research page.

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

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

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

npx skills add supermemoryai/memorybench

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

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.

• 📖 Documentation
• 🚀 Quickstart
• 🖥️ Self-hosting (Supermemory local)
• 🧪 MemoryBench
• 🔌 Integrations
• 💬 Discord
• 𝕏 Twitter

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

Content above is the README of supermemoryai/supermemory, reproduced from GitHub’s trending list. All rights remain with the original authors.