---
title: "Supermemory local"
sidebarTitle: "Overview"
description: "State-of-the-art memory, running on your machine. One binary, zero config."
icon: "/icons/hugeicons/server-stack-01.svg"
---

Supermemory runs on your own hardware. It's the same memory engine behind the [hosted platform](https://console.supermemory.ai) — ingestion, memory extraction, hybrid semantic search, and the full API — as a single self-contained binary.

<CodeGroup>
```bash curl
curl -fsSL https://supermemory.ai/install | bash
```

```bash npx
npx supermemory local
```
</CodeGroup>

No Docker. No database to provision. No config files. It boots in seconds with everything built in. The SDKs and other components in the [public repository](https://git.new/memory) are open source; the downloadable self-hosted server binary is built from a separate, non-public codebase.

## Zero config, actually

Run the binary with nothing set and you get a complete memory system:

- **The Supermemory graph engine, embedded** — created automatically on first boot. No database to stand up, no connection strings.
- **Built-in local embeddings** — default `Xenova/bge-base-en-v1.5` (768d) on your machine, no API key. Same provider stack as cloud if you opt into OpenAI, Gemini, or Ollama — see [Embeddings](/self-hosting/embeddings).
- **An API key, generated for you** — printed on first boot, ready to paste into any SDK.
- **The full Memory API** — `/ns/{namespace}/document`, `/ns/{namespace}/search`, `/ns/{namespace}/profile`, namespaces, the works.

The only thing you bring is a model. In production, Supermemory runs its own proprietary models, purpose-tuned for long-horizon data understanding and memory extraction. Self-hosted, the same pipeline runs on whatever model you point it at — OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint. Bring a key and go. Or don't bring one at all:

## Runs fully offline

Supermemory works with any OpenAI-compatible endpoint, which means it runs end-to-end on your machine with a local model — Ollama, LM Studio, vLLM, llama.cpp. `gpt-oss-20b` is a great fit:

```bash
OPENAI_BASE_URL=http://localhost:11434/v1 \
OPENAI_API_KEY=ollama \
OPENAI_MODEL=gpt-oss:20b \
supermemory-server
```

Local graph engine, local embeddings, local LLM. Text-memory processing can stay on your machine after the embedding model's first download. Supermemory collects telemetry; you can disable it with `SUPERMEMORY_DISABLE_TELEMETRY=1`. URL ingestion uses a hosted reader service; use local text or file inputs for offline operation.

## Drop-in with your existing code

<Note>
The v5 SDKs (`supermemory` 5.x on npm and PyPI) need `supermemory-server` v0.0.9 or later. Older servers only speak v3/v4; run `supermemory-server upgrade` first.
</Note>

The self-hosted server speaks the same API as the hosted platform. Point any Supermemory SDK at it with a one-line change:

```typescript
const client = new Supermemory({
  apiKey: "sm_...", // printed on first boot
  baseUrl: "http://localhost:6767",
})
```

Everything in the [Memory API docs](/quickstart) works the same way. The coding plugins do too — [Claude Code](/integrations/claude-code), [Muse Code](/integrations/muse-code), [Codex](/integrations/codex), and [OpenCode](/integrations/opencode) all target your local server with `SUPERMEMORY_API_URL=http://localhost:6767` (Muse: set `baseUrl` in `.muse/supermemory.json`, because hook env is cleared).

## Self-hosted vs. the platform

Self-hosted is free within its lite license limit and useful for local development and privacy-sensitive workloads. The server binary is not open source; the hosted platform is where the full product lives:

| | Self-hosted | Platform |
|---|---|---|
| Full Memory API | ✅ | ✅ |
| Hybrid semantic search | ✅ | ✅ |
| Embeddings | Local default (or OpenAI / Gemini / Ollama) | Same provider stack, managed |
| File ingestion (PDFs, images) | ✅ | ✅ |
| [Connectors](/connectors/overview) (Google Drive, Notion, Gmail, OneDrive) | — | ✅ |
| [Supermemory MCP](/supermemory-mcp/mcp) | — | ✅ |
| Memory extraction | Your model, your key | Proprietary long-horizon models — higher quality, cheaper at scale |
| Infrastructure | Your machine | Globally distributed, scales with you |

If you outgrow a single machine — or want connectors, MCP, and the best-tuned extraction pipeline — [the platform](https://console.supermemory.ai) is one `serverURL` change away. Running this for a team or organization? See [Local vs. Enterprise](/self-hosting/local-vs-enterprise).

## Next steps

<CardGroup cols={3}>
  <Card title="Quickstart" icon="/icons/hugeicons/play.svg" href="/self-hosting/quickstart">
    Install, run, and store your first memory in under two minutes
  </Card>
  <Card title="Configuration" icon="/icons/hugeicons/settings-01.svg" href="/self-hosting/configuration">
    Every environment variable: LLM providers, storage, auth, tuning
  </Card>
  <Card title="Embeddings" icon="/icons/hugeicons/route-02.svg" href="/self-hosting/embeddings">
    Local default, remote providers, multilingual, dimension lock
  </Card>
</CardGroup>