mirror of
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Rewrites 339 TypeScript calls across 50 pages from the rc.5 `method({ namespace, body })` form to the shipped `method(namespace, { ... })` form, and aligns field names with the live v5 spec: `attach` to `include`, `authUrl` to `authorization`, `lastSync` to `latestRun`, `deletedCount` to `count`, and the paginated `namespaces.list()`.
Renames container tags to namespaces across concepts, connectors, integrations and snippets. The namespace pages keep container tag in the description, search keywords and a rename note so old searches still land, and the v3 reference page points at v5.
The migration guide's SDK table now covers both 5.0.0 SDKs, and the SDK integration page uses the real client options (`baseUrl`, `timeoutInSeconds`, `maxRetries`) and error classes.
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92 lines
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---
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title: "Supermemory local"
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sidebarTitle: "Overview"
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description: "State-of-the-art memory, running on your machine. One binary, zero config."
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icon: "/icons/hugeicons/server-stack-01.svg"
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---
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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.
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<CodeGroup>
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```bash curl
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curl -fsSL https://supermemory.ai/install | bash
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```
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```bash npx
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npx supermemory local
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```
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</CodeGroup>
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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.
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## Zero config, actually
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Run the binary with nothing set and you get a complete memory system:
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- **The Supermemory graph engine, embedded** — created automatically on first boot. No database to stand up, no connection strings.
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- **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).
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- **An API key, generated for you** — printed on first boot, ready to paste into any SDK.
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- **The full Memory API** — `/ns/{namespace}/document`, `/ns/{namespace}/search`, `/ns/{namespace}/profile`, namespaces, the works.
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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:
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## Runs fully offline
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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:
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```bash
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OPENAI_BASE_URL=http://localhost:11434/v1 \
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OPENAI_API_KEY=ollama \
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OPENAI_MODEL=gpt-oss:20b \
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supermemory-server
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```
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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.
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## Drop-in with your existing code
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<Note>
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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.
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</Note>
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The self-hosted server speaks the same API as the hosted platform. Point any Supermemory SDK at it with a one-line change:
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```typescript
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const client = new Supermemory({
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apiKey: "sm_...", // printed on first boot
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baseUrl: "http://localhost:6767",
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})
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```
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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).
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## Self-hosted vs. the platform
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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:
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| | Self-hosted | Platform |
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|---|---|---|
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| Full Memory API | ✅ | ✅ |
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| Hybrid semantic search | ✅ | ✅ |
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| Embeddings | Local default (or OpenAI / Gemini / Ollama) | Same provider stack, managed |
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| File ingestion (PDFs, images) | ✅ | ✅ |
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| [Connectors](/connectors/overview) (Google Drive, Notion, Gmail, OneDrive) | — | ✅ |
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| [Supermemory MCP](/supermemory-mcp/mcp) | — | ✅ |
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| Memory extraction | Your model, your key | Proprietary long-horizon models — higher quality, cheaper at scale |
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| Infrastructure | Your machine | Globally distributed, scales with you |
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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).
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## Next steps
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<CardGroup cols={3}>
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<Card title="Quickstart" icon="/icons/hugeicons/play.svg" href="/self-hosting/quickstart">
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Install, run, and store your first memory in under two minutes
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</Card>
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<Card title="Configuration" icon="/icons/hugeicons/settings-01.svg" href="/self-hosting/configuration">
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Every environment variable: LLM providers, storage, auth, tuning
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</Card>
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<Card title="Embeddings" icon="/icons/hugeicons/route-02.svg" href="/self-hosting/embeddings">
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Local default, remote providers, multilingual, dimension lock
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</Card>
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</CardGroup>
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