---
title: "Supermemory local"
sidebarTitle: "Overview"
description: "State-of-the-art memory, running on your machine. One binary, zero config."
icon: "server"
---
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.
```bash curl
curl -fsSL https://supermemory.ai/install | bash
```
```bash npx
npx supermemory local
```
No Docker. No database to provision. No config files. It boots in seconds with everything built in, and it's [open source](https://git.new/memory).
## 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** — `/v3/documents`, `/v4/search`, `/v4/profile`, spaces, 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. Your data never leaves the building.
## Drop-in with your existing code
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), [Codex](/integrations/codex), and [OpenCode](/integrations/opencode) all target your local server with `SUPERMEMORY_API_URL=http://localhost:6767`.
## Self-hosted vs. the platform
Self-hosted is free, open source, and great for local development, air-gapped environments, and privacy-sensitive workloads. 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 `baseURL` change away. Running this for a team or organization? See [Local vs. Enterprise](/self-hosting/local-vs-enterprise).
## Next steps
Install, run, and store your first memory in under two minutes
Every environment variable: LLM providers, storage, auth, tuning
Local default, remote providers, multilingual, dimension lock