Self-Hosting Quickstart

From zero to your first memory in under two minutes.

Install

curl

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

npx

npx supermemory local

bunx

bunx supermemory local

The installer detects your OS and architecture, downloads the right binary, verifies it, and (when run interactively) prompts you for an LLM API key. Supported platforms: macOS (Apple Silicon & Intel), Linux (x64 & arm64).

Pin or change versions

Pass an explicit version to install (or roll back to) a specific release instead of latest:

curl -fsSL https://supermemory.ai/install | bash -s -- 0.0.3

Before rolling back, back up your data directory. The installer replaces the binary, but an older server may not understand data or schema changes made by a newer release.

Release tags are server-v<version> on GitHub Releases (for example server-v0.0.3).

To move to the newest release later:

supermemory-server upgrade

The binary may also print an “update available” notification on startup. If you intentionally pinned an older version (for example while debugging a regression), you can ignore that message until you are ready to upgrade.

Run

supermemory-server

First boot sets everything up — the embedded Supermemory graph engine, local embeddings, and your credentials:

  ┌──────────────────────────────────────────────────┐
  │  url       http://localhost:6767                 │
  │  database  ./.supermemory                        │
  │  api key   sm_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx     │
  │  org id    xxxxxxxxxxxxxxxxxxxxxx                │
  └──────────────────────────────────────────────────┘

Save that API key — it’s your bearer token for every request.

In production, Supermemory runs proprietary models tuned for long-horizon data understanding. Self-hosted, you bring any model: if no provider key is set, first boot launches an interactive setup wizard — pick a provider (OpenAI, Anthropic, Gemini, Groq, or any OpenAI-compatible endpoint like Ollama), paste your key, and it’s saved encrypted for every future launch. After the LLM key, you can optionally pick an embedding model (press Enter to keep local Xenova/bge-base-en-v1.5). See all providers, embeddings, and fully-offline local models.

Docker / non-interactive: set an LLM key via env and, if you don’t want local embeddings, set SUPERMEMORY_EMBEDDING_PROVIDER / MODEL / DIMENSIONS. There is no wizard without a TTY.

Add your first memory

TypeScript

import Supermemory from "supermemory"

const client = new Supermemory({
  apiKey: "sm_...",
  baseURL: "http://localhost:6767",
})

await client.memories.add({
  content: "I'm Dhravya. I love building dev tools and I'm allergic to peanuts.",
  containerTag: "user_dhravya",
})

Python

from supermemory import Supermemory

client = Supermemory(
    api_key="sm_...",
    base_url="http://localhost:6767",
)

client.memories.add(
    content="I'm Dhravya. I love building dev tools and I'm allergic to peanuts.",
    container_tag="user_dhravya",
)

curl

curl http://localhost:6767/v3/documents \
  -H "Authorization: Bearer sm_..." \
  -H "Content-Type: application/json" \
  -d '{
    "content": "I am Dhravya. I love building dev tools and I am allergic to peanuts.",
    "containerTag": "user_dhravya"
  }'

Search it

TypeScript

const results = await client.search({
  q: "what food should I avoid?",
  containerTag: "user_dhravya",
})

Python

results = client.search(
    q="what food should I avoid?",
    container_tag="user_dhravya",
)

curl

curl http://localhost:6767/v3/search \
  -H "Authorization: Bearer sm_..." \
  -H "Content-Type: application/json" \
  -d '{
    "q": "what food should I avoid?",
    "containerTag": "user_dhravya"
  }'

That’s it. Everything in the Memory API — documents, memories, user profiles, spaces, filtering — works identically against your local server.

Where things live

By default, all state lives in a single directory you can back up or move:

Path	Contents
./.supermemory/ (or $SUPERMEMORY_DATA_DIR)	The Supermemory graph engine’s data, auth secret, embedding model cache
~/.supermemory/env	API keys saved by the installer, loaded on every launch

Next steps

Configuration

LLM providers, local models, performance tuning

Embeddings

Local default, OpenAI / Gemini / Ollama, multilingual

Memory API

The full API — it all works against your local server